diff --git a/A7_Machine_Learning.ipynb b/A7_Machine_Learning.ipynb
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+++ b/A7_Machine_Learning.ipynb
@@ -0,0 +1,1577 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5t0sPs0TCgJj"
+      },
+      "source": [
+        "# Assignment: Trees\n",
+        "\n",
+        "## Do two questions in total: \"Q1+Q2\" or \"Q1+Q3\"\n",
+        "\n",
+        "`! git clone https://github.com/ds3001f25/linear_models_assignment.git`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QYfamPubCgJl"
+      },
+      "source": [
+        "**Q1.** Please answer the following questions in your own words.\n",
+        "1. Why is the Gini a good loss function for categorical target variables?\n",
+        "2. Why do trees tend to overfit, and how can this tendency be constrained?\n",
+        "3. True or false, and explain: Trees only really perform well in situations with lots of categorical variables as features/covariates.\n",
+        "4. Why don't most versions of classification/regression tree concept allow for more than two branches after a split?\n",
+        "5. What are some heuristic ways you can examine a tree and decide whether it is probably over- or under-fitting?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N_2EnMDJCgJo",
+        "outputId": "8ae9368a-7aa8-480a-8ebd-9dcf2796f48c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'linear_models_assignment'...\n",
+            "remote: Enumerating objects: 9, done.\u001b[K\n",
+            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
+            "remote: Total 9 (delta 0), reused 0 (delta 0), pack-reused 6 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (9/9), 18.05 KiB | 1.39 MiB/s, done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Clone the assignment\n",
+        "! git clone https://github.com/ds3001f25/linear_models_assignment.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Question 1.**\n",
+        "he Gini impurity is a good loss function for categorical target variables because it measures how often a randomly chosen sample would be misclassified if it were labeled according to the class distribution in a node. It works well with categorical data since it directly quantifies class mixing; here, lower values mean purer nodes with clearer class separation.\n",
+        "\n",
+        "**Question 2.**\n",
+        "rees tend to overfit because they keep splitting until every leaf fits the training data perfectly, capturing noise rather than general patterns. This tendency can be constrained by setting limits such as maximum depth, minimum samples per leaf, or pruning back branches that do not significantly improve predictive accuracy.\n",
+        "\n",
+        "**Question 3.**\n",
+        "False. Trees can handle both categorical and numerical features well. Their strength lies in capturing nonlinear relationships and interactions between variables, not just categorical ones. They partition the data adaptively, so they are flexible across many feature types.\n",
+        "\n",
+        "**Question 4.**\n",
+        "Most classification and regression tree algorithms use binary splits (two branches) because they are computationally efficient and easier to interpret. Binary splits also allow trees to explore combinations of variables more flexibly, rather than forcing many-way splits that can fragment the data too much.\n",
+        "\n",
+        "**Question 5.**\n",
+        "A tree is probably overfitting if it is very deep, has many small leaves with few samples, or performs much better on training data than on validation data. It is likely underfitting if it is very shallow, has few splits, and shows poor accuracy on both training and validation sets."
+      ],
+      "metadata": {
+        "id": "QbL969R6CrqY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nd4FC3syCgJp"
+      },
+      "source": [
+        "**Q2.** This is a case study about classification and regression trees.\n",
+        "\n",
+        "1. Load the `Breast Cancer METABRIC.csv` dataset. How many observations and variables does it contain? Print out the first few rows of data.\n",
+        "\n",
+        "2.  We'll use a consistent set of feature/explanatory variables. For numeric variables, we'll include `Tumor Size`, `Lymph nodes examined positive`, `Age at Diagnosis`. For categorical variables, we'll include `Tumor Stage`, `Chemotherapy`, and `Cancer Type Detailed`. One-hot-encode the categorical variables and concatenate them with the numeric variables into a feature/covariate matrix, $X$.\n",
+        "\n",
+        "3. Let's predict `Overall Survival Status` given the features/covariates $X$. There are 528 missing values, unfortunately: Either drop those rows from your data or add them as a category to predict. Constrain the minimum samples per leaf to 10. Print a dendrogram of the tree. Print a confusion matrix of the algorithm's performance. What is the accuracy?\n",
+        "\n",
+        "4. For your model in part three, compute three statistics:\n",
+        "    - The **true positive rate** or **sensitivity**:\n",
+        "        $$\n",
+        "        TPR = \\dfrac{TP}{TP+FN}\n",
+        "        $$\n",
+        "    - The **true negative rate** or **specificity**:\n",
+        "        $$\n",
+        "        TNR = \\dfrac{TN}{TN+FP}\n",
+        "        $$\n",
+        "    Does your model tend to perform better with respect to one of these metrics?\n",
+        "\n",
+        "5. Let's predict `Overall Survival (Months)` given the features/covariates $X$. Use the train/test split to pick the optimal `min_samples_leaf` value that gives the highest $R^2$ on the test set (it's about 110). What is the $R^2$? Plot the test values against the predicted values. How do you feel about this model for clinical purposes?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VKUpW7oaCgJq"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5PjP75bgCgJq"
+      },
+      "source": [
+        "**Q3.** This is a case study about trees using bond rating data. This is a dataset about bond ratings for different companies, alongside a bunch of business statistics and other data. Companies often have multiple reviews at different dates. We want to predict the bond rating (AAA, AA, A, BBB, BB, B, ..., C, D). Do business fundamentals predict the company's rating?\n",
+        "\n",
+        "1. Load the `./data/corporate_ratings.csv` dataset. How many observations and variables does it contain? Print out the first few rows of data.\n",
+        "\n",
+        "2.  Plot a histogram of the `ratings` variable. It turns out that the gradations of AAA/AA/A and BBB/BB/B and so on make it hard to get good results with trees. Collapse all AAA/AA/A ratings into just A, and similarly for B and C.\n",
+        "\n",
+        "3. Use all of the variables **except** Rating, Date, Name, Symbol, and Rating Agency Name. To include Sector, make a dummy/one-hot-encoded representation and include it in your features/covariates. Collect the relevant variables into a data matrix $X$.\n",
+        "\n",
+        "4. Do a train/test split of the data and use a decision tree classifier to predict the bond rating. Including a min_samples_leaf constraint can raise the accuracy and speed up computation time. Print a confusion matrix and the accuracy of your model. How well do you predict the different bond ratings?\n",
+        "\n",
+        "5. If you include the rating agency as a feature/covariate/predictor variable, do the results change? How do you interpret this?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "DIjJ0u5kCgJr",
+        "outputId": "0f374240-f8d5-492f-ebea-635f6d70fc78"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-01d4d178-d248-4553-bc05-75d9d48c74ae\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-01d4d178-d248-4553-bc05-75d9d48c74ae\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving corporate_ratings.csv to corporate_ratings.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "#import pandas + upload the file here\n",
+        "import pandas as pd\n",
+        "from google.colab import files\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "21941bec",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 516
+        },
+        "outputId": "b292cfdd-7d68-4342-834a-91e4c698a588"
+      },
+      "source": [
+        "#Question 3.1: How many observations and variations?\n",
+        "#read the csv file\n",
+        "df = pd.read_csv('corporate_ratings.csv')\n",
+        "\n",
+        "#print out the dimension\n",
+        "print(f\"Observations (rows): {df.shape[0]}\")\n",
+        "print(f\"Variables (columns): {df.shape[1]}\")\n",
+        "\n",
+        "#display the first couple of rows so we can visualize what is going on\n",
+        "df.head()"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Observations (rows): 2029\n",
+            "Variables (columns): 31\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  Rating                   Name Symbol                  Rating Agency Name  \\\n",
+              "0      A  Whirlpool Corporation    WHR          Egan-Jones Ratings Company   \n",
+              "1    BBB  Whirlpool Corporation    WHR          Egan-Jones Ratings Company   \n",
+              "2    BBB  Whirlpool Corporation    WHR                       Fitch Ratings   \n",
+              "3    BBB  Whirlpool Corporation    WHR                       Fitch Ratings   \n",
+              "4    BBB  Whirlpool Corporation    WHR  Standard & Poor's Ratings Services   \n",
+              "\n",
+              "         Date             Sector  currentRatio  quickRatio  cashRatio  \\\n",
+              "0  11/27/2015  Consumer Durables      0.945894    0.426395   0.099690   \n",
+              "1   2/13/2014  Consumer Durables      1.033559    0.498234   0.203120   \n",
+              "2    3/6/2015  Consumer Durables      0.963703    0.451505   0.122099   \n",
+              "3   6/15/2012  Consumer Durables      1.019851    0.510402   0.176116   \n",
+              "4  10/24/2016  Consumer Durables      0.957844    0.495432   0.141608   \n",
+              "\n",
+              "   daysOfSalesOutstanding  ...  effectiveTaxRate  \\\n",
+              "0               44.203245  ...          0.202716   \n",
+              "1               38.991156  ...          0.074155   \n",
+              "2               50.841385  ...          0.214529   \n",
+              "3               41.161738  ...          1.816667   \n",
+              "4               47.761126  ...          0.166966   \n",
+              "\n",
+              "   freeCashFlowOperatingCashFlowRatio  freeCashFlowPerShare  cashPerShare  \\\n",
+              "0                            0.437551              6.810673      9.809403   \n",
+              "1                            0.541997              8.625473     17.402270   \n",
+              "2                            0.513185              9.693487     13.103448   \n",
+              "3                           -0.147170             -1.015625     14.440104   \n",
+              "4                            0.451372              7.135348     14.257556   \n",
+              "\n",
+              "   companyEquityMultiplier  ebitPerRevenue  enterpriseValueMultiple  \\\n",
+              "0                 4.008012        0.049351                 7.057088   \n",
+              "1                 3.156783        0.048857                 6.460618   \n",
+              "2                 4.094575        0.044334                10.491970   \n",
+              "3                 3.630950       -0.012858                 4.080741   \n",
+              "4                 4.012780        0.053770                 8.293505   \n",
+              "\n",
+              "   operatingCashFlowPerShare  operatingCashFlowSalesRatio  payablesTurnover  \n",
+              "0                  15.565438                     0.058638          3.906655  \n",
+              "1                  15.914250                     0.067239          4.002846  \n",
+              "2                  18.888889                     0.074426          3.483510  \n",
+              "3                   6.901042                     0.028394          4.581150  \n",
+              "4                  15.808147                     0.058065          3.857790  \n",
+              "\n",
+              "[5 rows x 31 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-43a09598-30af-4159-a0a4-6aeba62cb5e2\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rating</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Symbol</th>\n",
+              "      <th>Rating Agency Name</th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Sector</th>\n",
+              "      <th>currentRatio</th>\n",
+              "      <th>quickRatio</th>\n",
+              "      <th>cashRatio</th>\n",
+              "      <th>daysOfSalesOutstanding</th>\n",
+              "      <th>...</th>\n",
+              "      <th>effectiveTaxRate</th>\n",
+              "      <th>freeCashFlowOperatingCashFlowRatio</th>\n",
+              "      <th>freeCashFlowPerShare</th>\n",
+              "      <th>cashPerShare</th>\n",
+              "      <th>companyEquityMultiplier</th>\n",
+              "      <th>ebitPerRevenue</th>\n",
+              "      <th>enterpriseValueMultiple</th>\n",
+              "      <th>operatingCashFlowPerShare</th>\n",
+              "      <th>operatingCashFlowSalesRatio</th>\n",
+              "      <th>payablesTurnover</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>A</td>\n",
+              "      <td>Whirlpool Corporation</td>\n",
+              "      <td>WHR</td>\n",
+              "      <td>Egan-Jones Ratings Company</td>\n",
+              "      <td>11/27/2015</td>\n",
+              "      <td>Consumer Durables</td>\n",
+              "      <td>0.945894</td>\n",
+              "      <td>0.426395</td>\n",
+              "      <td>0.099690</td>\n",
+              "      <td>44.203245</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.202716</td>\n",
+              "      <td>0.437551</td>\n",
+              "      <td>6.810673</td>\n",
+              "      <td>9.809403</td>\n",
+              "      <td>4.008012</td>\n",
+              "      <td>0.049351</td>\n",
+              "      <td>7.057088</td>\n",
+              "      <td>15.565438</td>\n",
+              "      <td>0.058638</td>\n",
+              "      <td>3.906655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>BBB</td>\n",
+              "      <td>Whirlpool Corporation</td>\n",
+              "      <td>WHR</td>\n",
+              "      <td>Egan-Jones Ratings Company</td>\n",
+              "      <td>2/13/2014</td>\n",
+              "      <td>Consumer Durables</td>\n",
+              "      <td>1.033559</td>\n",
+              "      <td>0.498234</td>\n",
+              "      <td>0.203120</td>\n",
+              "      <td>38.991156</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.074155</td>\n",
+              "      <td>0.541997</td>\n",
+              "      <td>8.625473</td>\n",
+              "      <td>17.402270</td>\n",
+              "      <td>3.156783</td>\n",
+              "      <td>0.048857</td>\n",
+              "      <td>6.460618</td>\n",
+              "      <td>15.914250</td>\n",
+              "      <td>0.067239</td>\n",
+              "      <td>4.002846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>BBB</td>\n",
+              "      <td>Whirlpool Corporation</td>\n",
+              "      <td>WHR</td>\n",
+              "      <td>Fitch Ratings</td>\n",
+              "      <td>3/6/2015</td>\n",
+              "      <td>Consumer Durables</td>\n",
+              "      <td>0.963703</td>\n",
+              "      <td>0.451505</td>\n",
+              "      <td>0.122099</td>\n",
+              "      <td>50.841385</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.214529</td>\n",
+              "      <td>0.513185</td>\n",
+              "      <td>9.693487</td>\n",
+              "      <td>13.103448</td>\n",
+              "      <td>4.094575</td>\n",
+              "      <td>0.044334</td>\n",
+              "      <td>10.491970</td>\n",
+              "      <td>18.888889</td>\n",
+              "      <td>0.074426</td>\n",
+              "      <td>3.483510</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>BBB</td>\n",
+              "      <td>Whirlpool Corporation</td>\n",
+              "      <td>WHR</td>\n",
+              "      <td>Fitch Ratings</td>\n",
+              "      <td>6/15/2012</td>\n",
+              "      <td>Consumer Durables</td>\n",
+              "      <td>1.019851</td>\n",
+              "      <td>0.510402</td>\n",
+              "      <td>0.176116</td>\n",
+              "      <td>41.161738</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.816667</td>\n",
+              "      <td>-0.147170</td>\n",
+              "      <td>-1.015625</td>\n",
+              "      <td>14.440104</td>\n",
+              "      <td>3.630950</td>\n",
+              "      <td>-0.012858</td>\n",
+              "      <td>4.080741</td>\n",
+              "      <td>6.901042</td>\n",
+              "      <td>0.028394</td>\n",
+              "      <td>4.581150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>BBB</td>\n",
+              "      <td>Whirlpool Corporation</td>\n",
+              "      <td>WHR</td>\n",
+              "      <td>Standard &amp; Poor's Ratings Services</td>\n",
+              "      <td>10/24/2016</td>\n",
+              "      <td>Consumer Durables</td>\n",
+              "      <td>0.957844</td>\n",
+              "      <td>0.495432</td>\n",
+              "      <td>0.141608</td>\n",
+              "      <td>47.761126</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.166966</td>\n",
+              "      <td>0.451372</td>\n",
+              "      <td>7.135348</td>\n",
+              "      <td>14.257556</td>\n",
+              "      <td>4.012780</td>\n",
+              "      <td>0.053770</td>\n",
+              "      <td>8.293505</td>\n",
+              "      <td>15.808147</td>\n",
+              "      <td>0.058065</td>\n",
+              "      <td>3.857790</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 31 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43a09598-30af-4159-a0a4-6aeba62cb5e2')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-43a09598-30af-4159-a0a4-6aeba62cb5e2 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-43a09598-30af-4159-a0a4-6aeba62cb5e2');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-986b9424-a360-46e8-94cb-698fd9103527\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-986b9424-a360-46e8-94cb-698fd9103527')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-986b9424-a360-46e8-94cb-698fd9103527 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 957
+        },
+        "id": "59fb8c12",
+        "outputId": "a8958bd2-60a7-4d81-a5f9-df1a2e6b1699"
+      },
+      "source": [
+        "#Question 3.2: Histogram\n",
+        "#import matplotlib for histogram visualization\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#plot the original ratings histogram + make it look pretty with labels\n",
+        "plt.figure(figsize=(8,5))\n",
+        "df['Rating'].hist(bins=len(df['Rating'].unique()), edgecolor='black')\n",
+        "plt.title('Original Bond Ratings Distribution')\n",
+        "plt.xlabel('Rating')\n",
+        "plt.ylabel('Count')\n",
+        "plt.show()\n",
+        "\n",
+        "#this will collapse the ratings where AAA/AA/A --> A, BBB/BB/B --> B, CCC/CC/C --> C\n",
+        "collapse_map = {\n",
+        "    'AAA': 'A', 'AA': 'A', 'A': 'A',\n",
+        "    'BBB': 'B', 'BB': 'B', 'B': 'B',\n",
+        "    'CCC': 'C', 'CC': 'C', 'C': 'C',\n",
+        "    'D': 'D'\n",
+        "}\n",
+        "\n",
+        "#create a data frame of the ratings with collapsed\n",
+        "df['rating_collapsed'] = df['Rating'].map(collapse_map)\n",
+        "\n",
+        "#plot the collapsed histogram so we visualize the original and the collapsed plotted against each other + make it pretty!\n",
+        "plt.figure(figsize=(8,5))\n",
+        "df['rating_collapsed'].hist(edgecolor='black')\n",
+        "plt.title('Collapsed Bond Ratings Distribution')\n",
+        "plt.xlabel('Collapsed Rating')\n",
+        "plt.ylabel('Count')\n",
+        "plt.show()"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "e17b4cbc",
+        "outputId": "2af94d85-0190-4cfc-b466-b0e57f7b7360"
+      },
+      "source": [
+        "#Question 3.3: Data Matrix\n",
+        "\n",
+        "#create a new variable to exclude all of the columns we do not want\n",
+        "exclude_cols = {'Rating', 'rating', 'rating_collapsed', 'Date', 'Name', 'Symbol', 'Rating Agency Name'}\n",
+        "\n",
+        "#create a new variable for one-hot encode sector\n",
+        "sector_dummies = pd.get_dummies(df['Sector'], prefix='Sector') if 'Sector' in df.columns else pd.DataFrame(index=df.index)\n",
+        "\n",
+        "#create a variable to keep all remaining numeric/boolean columns except the excluded ones\n",
+        "base_feats = df.drop(columns=[c for c in exclude_cols if c in df.columns] + (['Sector'] if 'Sector' in df.columns else []))\n",
+        "base_feats = base_feats.select_dtypes(include=['number', 'bool'])\n",
+        "\n",
+        "#Combine this all into one singular variable called 'X'\n",
+        "X = pd.concat([base_feats.reset_index(drop=True),\n",
+        "               sector_dummies.reset_index(drop=True)], axis=1)\n",
+        "\n",
+        "#print 'X' shape + visualize it\n",
+        "print(\"X shape:\", X.shape)\n",
+        "X.head()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X shape: (2029, 37)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   currentRatio  quickRatio  cashRatio  daysOfSalesOutstanding  \\\n",
+              "0      0.945894    0.426395   0.099690               44.203245   \n",
+              "1      1.033559    0.498234   0.203120               38.991156   \n",
+              "2      0.963703    0.451505   0.122099               50.841385   \n",
+              "3      1.019851    0.510402   0.176116               41.161738   \n",
+              "4      0.957844    0.495432   0.141608               47.761126   \n",
+              "\n",
+              "   netProfitMargin  pretaxProfitMargin  grossProfitMargin  \\\n",
+              "0         0.037480            0.049351           0.176631   \n",
+              "1         0.044062            0.048857           0.175715   \n",
+              "2         0.032709            0.044334           0.170843   \n",
+              "3         0.020894           -0.012858           0.138059   \n",
+              "4         0.042861            0.053770           0.177720   \n",
+              "\n",
+              "   operatingProfitMargin  returnOnAssets  returnOnCapitalEmployed  ...  \\\n",
+              "0               0.061510        0.041189                 0.091514  ...   \n",
+              "1               0.066546        0.053204                 0.104800  ...   \n",
+              "2               0.059783        0.032497                 0.075955  ...   \n",
+              "3               0.042430        0.025690                -0.027015  ...   \n",
+              "4               0.065354        0.046363                 0.096945  ...   \n",
+              "\n",
+              "   Sector_Consumer Durables  Sector_Consumer Non-Durables  \\\n",
+              "0                      True                         False   \n",
+              "1                      True                         False   \n",
+              "2                      True                         False   \n",
+              "3                      True                         False   \n",
+              "4                      True                         False   \n",
+              "\n",
+              "   Sector_Consumer Services  Sector_Energy  Sector_Finance  \\\n",
+              "0                     False          False           False   \n",
+              "1                     False          False           False   \n",
+              "2                     False          False           False   \n",
+              "3                     False          False           False   \n",
+              "4                     False          False           False   \n",
+              "\n",
+              "   Sector_Health Care  Sector_Miscellaneous  Sector_Public Utilities  \\\n",
+              "0               False                 False                    False   \n",
+              "1               False                 False                    False   \n",
+              "2               False                 False                    False   \n",
+              "3               False                 False                    False   \n",
+              "4               False                 False                    False   \n",
+              "\n",
+              "   Sector_Technology  Sector_Transportation  \n",
+              "0              False                  False  \n",
+              "1              False                  False  \n",
+              "2              False                  False  \n",
+              "3              False                  False  \n",
+              "4              False                  False  \n",
+              "\n",
+              "[5 rows x 37 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9f50df6f-b876-4967-9c41-2b053f3907cd\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>currentRatio</th>\n",
+              "      <th>quickRatio</th>\n",
+              "      <th>cashRatio</th>\n",
+              "      <th>daysOfSalesOutstanding</th>\n",
+              "      <th>netProfitMargin</th>\n",
+              "      <th>pretaxProfitMargin</th>\n",
+              "      <th>grossProfitMargin</th>\n",
+              "      <th>operatingProfitMargin</th>\n",
+              "      <th>returnOnAssets</th>\n",
+              "      <th>returnOnCapitalEmployed</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Sector_Consumer Durables</th>\n",
+              "      <th>Sector_Consumer Non-Durables</th>\n",
+              "      <th>Sector_Consumer Services</th>\n",
+              "      <th>Sector_Energy</th>\n",
+              "      <th>Sector_Finance</th>\n",
+              "      <th>Sector_Health Care</th>\n",
+              "      <th>Sector_Miscellaneous</th>\n",
+              "      <th>Sector_Public Utilities</th>\n",
+              "      <th>Sector_Technology</th>\n",
+              "      <th>Sector_Transportation</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.945894</td>\n",
+              "      <td>0.426395</td>\n",
+              "      <td>0.099690</td>\n",
+              "      <td>44.203245</td>\n",
+              "      <td>0.037480</td>\n",
+              "      <td>0.049351</td>\n",
+              "      <td>0.176631</td>\n",
+              "      <td>0.061510</td>\n",
+              "      <td>0.041189</td>\n",
+              "      <td>0.091514</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1.033559</td>\n",
+              "      <td>0.498234</td>\n",
+              "      <td>0.203120</td>\n",
+              "      <td>38.991156</td>\n",
+              "      <td>0.044062</td>\n",
+              "      <td>0.048857</td>\n",
+              "      <td>0.175715</td>\n",
+              "      <td>0.066546</td>\n",
+              "      <td>0.053204</td>\n",
+              "      <td>0.104800</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0.963703</td>\n",
+              "      <td>0.451505</td>\n",
+              "      <td>0.122099</td>\n",
+              "      <td>50.841385</td>\n",
+              "      <td>0.032709</td>\n",
+              "      <td>0.044334</td>\n",
+              "      <td>0.170843</td>\n",
+              "      <td>0.059783</td>\n",
+              "      <td>0.032497</td>\n",
+              "      <td>0.075955</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.019851</td>\n",
+              "      <td>0.510402</td>\n",
+              "      <td>0.176116</td>\n",
+              "      <td>41.161738</td>\n",
+              "      <td>0.020894</td>\n",
+              "      <td>-0.012858</td>\n",
+              "      <td>0.138059</td>\n",
+              "      <td>0.042430</td>\n",
+              "      <td>0.025690</td>\n",
+              "      <td>-0.027015</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.957844</td>\n",
+              "      <td>0.495432</td>\n",
+              "      <td>0.141608</td>\n",
+              "      <td>47.761126</td>\n",
+              "      <td>0.042861</td>\n",
+              "      <td>0.053770</td>\n",
+              "      <td>0.177720</td>\n",
+              "      <td>0.065354</td>\n",
+              "      <td>0.046363</td>\n",
+              "      <td>0.096945</td>\n",
+              "      <td>...</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 37 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f50df6f-b876-4967-9c41-2b053f3907cd')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9f50df6f-b876-4967-9c41-2b053f3907cd button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9f50df6f-b876-4967-9c41-2b053f3907cd');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-69eb2cce-03a7-4cbe-a0ba-558bb7ddff80\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69eb2cce-03a7-4cbe-a0ba-558bb7ddff80')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-69eb2cce-03a7-4cbe-a0ba-558bb7ddff80 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "X"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 3.4: Confusion Matrix\n",
+        "#we will need the following packages\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.metrics import confusion_matrix, accuracy_score, ConfusionMatrixDisplay\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#Define target (collapsed ratings) and features (X)\n",
+        "y = df['rating_collapsed']\n",
+        "\n",
+        "#These will remove rows where the collapsed rating is 'D' as it has only one sample\n",
+        "df_filtered = df[df['rating_collapsed'] != 'D'].copy()\n",
+        "X_filtered = X[df['rating_collapsed'] != 'D'].copy()\n",
+        "y_filtered = df_filtered['rating_collapsed']\n",
+        "\n",
+        "#Here we will split the data into training and testing set\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X_filtered, y_filtered, test_size=0.3, random_state=42, stratify=y_filtered)\n",
+        "\n",
+        "#'clf' will create and fit Decision Tree model\n",
+        "clf = DecisionTreeClassifier(random_state=42, min_samples_leaf=10)\n",
+        "clf.fit(X_train, y_train)\n",
+        "\n",
+        "#predict the test set\n",
+        "y_pred = clf.predict(X_test)\n",
+        "\n",
+        "#evaluate and print the accuracy\n",
+        "acc = accuracy_score(y_test, y_pred)\n",
+        "print(f\"Accuracy: {acc:.3f}\")\n",
+        "\n",
+        "#This will create a confusion matrix\n",
+        "cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)\n",
+        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf.classes_)\n",
+        "disp.plot(cmap='Blues')\n",
+        "plt.title(\"Confusion Matrix for Bond Rating Prediction\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "id": "cMBjldP8KPm9",
+        "outputId": "9b397c6c-60c9-46e8-ec3b-56386e0923ee"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy: 0.742\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Question 3.4:**\n",
+        "An accuracy of 0.742 means the tree correctly predicts about 74.2% of ratings in the test set. That suggests the model is capturing meaningful patterns in the business fundamentals. In addition, based on the generated Confusion Matrix, it shows that the model performs well overall but is somewhat biased toward predicting the majority class, B. Most B-rated companies are correctly classified (365), while a large portion of A and C ratings are misclassified as B. Essentially, it recognizes whether a company is roughly high-, mid-, or low-quality but has difficulty separating close credit tiers."
+      ],
+      "metadata": {
+        "id": "qEH-XkTSLcdr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 3.5: How do the results change?\n",
+        "\n",
+        "#here we will use our collapsed target\n",
+        "y = df['rating_collapsed']\n",
+        "\n",
+        "#remove rows where the collapsed rating is 'D' as it has only one sample\n",
+        "df_filtered = df[df['rating_collapsed'] != 'D'].copy()\n",
+        "X_filtered = X[df['rating_collapsed'] != 'D'].copy()\n",
+        "y_filtered = df_filtered['rating_collapsed']\n",
+        "\n",
+        "#one-hot rating encode rating agency and append to features\n",
+        "if 'Rating Agency Name' in df_filtered.columns: # Use df_filtered here\n",
+        "    agency_dummies = pd.get_dummies(df_filtered['Rating Agency Name'], prefix='Agency')\n",
+        "else:\n",
+        "    agency_dummies = pd.DataFrame(index=df_filtered.index)\n",
+        "\n",
+        "#this will concatenate without resetting index\n",
+        "X_with_agency = pd.concat([X_filtered, agency_dummies], axis=1)\n",
+        "\n",
+        "\n",
+        "#this will make a single index split to keep apples-to-apples comparison\n",
+        "idx_train, idx_test = train_test_split(df_filtered.index, test_size=0.30, random_state=42, stratify=y_filtered) # Use y_filtered here\n",
+        "\n",
+        "X_tr,  X_te  = X_filtered.loc[idx_train], X_filtered.loc[idx_test] # Use X_filtered here\n",
+        "Xag_tr, Xag_te = X_with_agency.loc[idx_train], X_with_agency.loc[idx_test]\n",
+        "y_tr,  y_te  = y_filtered.loc[idx_train], y_filtered.loc[idx_test] # Use y_filtered here\n",
+        "\n",
+        "\n",
+        "#Model WITHOUT agency\n",
+        "clf_no_ag = DecisionTreeClassifier(random_state=42, min_samples_leaf=10)\n",
+        "clf_no_ag.fit(X_tr, y_tr)\n",
+        "pred_no_ag = clf_no_ag.predict(X_te)\n",
+        "acc_no_ag = accuracy_score(y_te, pred_no_ag)\n",
+        "\n",
+        "#Model WITH agency\n",
+        "clf_ag = DecisionTreeClassifier(random_state=42, min_samples_leaf=10)\n",
+        "clf_ag.fit(Xag_tr, y_tr)\n",
+        "pred_ag = clf_ag.predict(Xag_te)\n",
+        "acc_ag = accuracy_score(y_te, pred_ag)\n",
+        "\n",
+        "#print this out\n",
+        "print(f\"Accuracy WITHOUT agency: {acc_no_ag:.3f}\")\n",
+        "print(f\"Accuracy WITH agency:    {acc_ag:.3f}\")\n",
+        "\n",
+        "#Confusion matrix for the WITH-agency model\n",
+        "cm = confusion_matrix(y_te, pred_ag, labels=clf_ag.classes_)\n",
+        "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=clf_ag.classes_)\n",
+        "disp.plot(cmap='Blues')\n",
+        "plt.title(\"Confusion Matrix (with Rating Agency feature)\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 508
+        },
+        "id": "RPHwb0JxK0iF",
+        "outputId": "cd925dc0-9f16-482d-a8c5-83c035fbbe1c"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy WITHOUT agency: 0.742\n",
+            "Accuracy WITH agency:    0.754\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Question 3.5**\n",
+        "\n",
+        "Including the rating agency as a predictor slightly improved accuracy from 0.742 to 0.754, meaning the model gained a small edge in prediction. The confusion matrix shows only minor shifts, with a few more A ratings correctly classified and marginally fewer misclassifications overall. This suggests that different agencies have slightly distinct rating patterns.\n",
+        "\n",
+        "However, this improvement reflects the model’s ability to capture agency-specific bias rather than a deeper understanding of company fundamentals. In other words, the model now predicts ratings a bit better because it knows who issued the rating, not necessarily because it better understands why the rating was given."
+      ],
+      "metadata": {
+        "id": "_PLVG0LQNERa"
+      }
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment_Clustering.ipynb b/Assignment_Clustering.ipynb
new file mode 100644
index 0000000..1f049e9
--- /dev/null
+++ b/Assignment_Clustering.ipynb
@@ -0,0 +1,2783 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/Assignment_Clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfc23963-2cc5-4f1d-8278-fb1b2026afc5",
+      "metadata": {
+        "id": "cfc23963-2cc5-4f1d-8278-fb1b2026afc5"
+      },
+      "source": [
+        "## Assignment: $k$ Means Clustering\n",
+        "\n",
+        "### `! git clone https://github.com/ds3001f25/clustering_assignment.git`\n",
+        "\n",
+        "### **Do two questions in total: \"Q1+Q2\" or \"Q1+Q3\"**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Clone the assignment\n",
+        "! git clone https://github.com/ds3001f25/clustering_assignment.git"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_vUxT-M-awey",
+        "outputId": "b0930148-8246-427e-f91f-c19e617be24e"
+      },
+      "id": "_vUxT-M-awey",
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'clustering_assignment'...\n",
+            "remote: Enumerating objects: 9, done.\u001b[K\n",
+            "remote: Counting objects: 100% (9/9), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
+            "remote: Total 9 (delta 0), reused 6 (delta 0), pack-reused 0 (from 0)\u001b[K\n",
+            "Receiving objects: 100% (9/9), 221.98 KiB | 1.72 MiB/s, done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "26523935-9e8f-4377-920c-6f65605c0e31",
+      "metadata": {
+        "id": "26523935-9e8f-4377-920c-6f65605c0e31"
+      },
+      "source": [
+        "**Q1.** This is a question about clustering. We want to investigate how adjusting the \"noisiness\" of the data impacts the quality of the algorithm and the difficulty of picking $k$.\n",
+        "\n",
+        "1. Run the code below, which creates four datasets: `df0_125`, `df0_25`, `df0_5`, `df1_0`, and `df2_0`. Each data set is created by increasing the amount of `noise` (standard deviation) around the cluster centers, from `0.125` to `0.25` to `0.5` to `1.0` to `2.0`.\n",
+        "\n",
+        "```\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "def createData(noise,N=50):\n",
+        "    np.random.seed(100) # Set the seed for replicability\n",
+        "    # Generate (x1,x2,g) triples:\n",
+        "    X1 = np.array([np.random.normal(1,noise,N),np.random.normal(1,noise,N)])\n",
+        "    X2 = np.array([np.random.normal(3,noise,N),np.random.normal(2,noise,N)])\n",
+        "    X3 = np.array([np.random.normal(5,noise,N),np.random.normal(3,noise,N)])\n",
+        "    # Concatenate into one data frame\n",
+        "    gdf1 = pd.DataFrame({'x1':X1[0,:],'x2':X1[1,:],'group':'a'})\n",
+        "    gdf2 = pd.DataFrame({'x1':X2[0,:],'x2':X2[1,:],'group':'b'})\n",
+        "    gdf3 = pd.DataFrame({'x1':X3[0,:],'x2':X3[1,:],'group':'c'})\n",
+        "    df = pd.concat([gdf1,gdf2,gdf3],axis=0)\n",
+        "    return df\n",
+        "\n",
+        "df0_125 = createData(0.125)\n",
+        "df0_25 = createData(0.25)\n",
+        "df0_5 = createData(0.5)\n",
+        "df1_0 = createData(1.0)\n",
+        "df2_0 = createData(2.0)\n",
+        "```\n",
+        "\n",
+        "2. Make scatterplots of the $(X1,X2)$ points by group for each of the datasets. As the `noise` goes up from 0.125 to 2.0, what happens to the visual distinctness of the clusters?\n",
+        "3. Create a scree plot for each of the datasets. Describe how the level of `noise` affects the scree plot (particularly the presence of a clear \"elbow\") and your ability to definitively select a $k$. (Pay attention to the vertical axis across plots, or put all the scree curves on a single canvas.)\n",
+        "4. Explain the intuition of the elbow, using this numerical simulation as an example."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "f3ef1845",
+      "metadata": {
+        "id": "f3ef1845"
+      },
+      "outputs": [],
+      "source": [
+        "#Question 1.1: Run the code from above\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "def createData(noise,N=50):\n",
+        "    np.random.seed(100) # Set the seed for replicability\n",
+        "    # Generate (x1,x2,g) triples:\n",
+        "    X1 = np.array([np.random.normal(1,noise,N),np.random.normal(1,noise,N)])\n",
+        "    X2 = np.array([np.random.normal(3,noise,N),np.random.normal(2,noise,N)])\n",
+        "    X3 = np.array([np.random.normal(5,noise,N),np.random.normal(3,noise,N)])\n",
+        "    # Concatenate into one data frame\n",
+        "    gdf1 = pd.DataFrame({'x1':X1[0,:],'x2':X1[1,:],'group':'a'})\n",
+        "    gdf2 = pd.DataFrame({'x1':X2[0,:],'x2':X2[1,:],'group':'b'})\n",
+        "    gdf3 = pd.DataFrame({'x1':X3[0,:],'x2':X3[1,:],'group':'c'})\n",
+        "    df = pd.concat([gdf1,gdf2,gdf3],axis=0)\n",
+        "    return df\n",
+        "\n",
+        "df0_125 = createData(0.125)\n",
+        "df0_25 = createData(0.25)\n",
+        "df0_5 = createData(0.5)\n",
+        "df1_0 = createData(1.0)\n",
+        "df2_0 = createData(2.0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 1.2: Scatterplots\n",
+        "#import all of the neccesary libraries\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#This is where we make the scatterplots\n",
+        "datasets = {\n",
+        "    \"Noise = 0.125\": df0_125,\n",
+        "    \"Noise = 0.25\": df0_25,\n",
+        "    \"Noise = 0.5\": df0_5,\n",
+        "    \"Noise = 1.0\": df1_0,\n",
+        "    \"Noise = 2.0\": df2_0\n",
+        "}\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 5, figsize=(20, 4), sharex=True, sharey=True)\n",
+        "\n",
+        "#create legend + make it look pretty\n",
+        "for ax, (title, df) in zip(axes, datasets.items()):\n",
+        "    for g in df['group'].unique():\n",
+        "        subset = df[df['group'] == g]\n",
+        "        ax.scatter(subset['x1'], subset['x2'], label=g, alpha=0.6)\n",
+        "    ax.set_title(title)\n",
+        "    ax.legend()\n",
+        "\n",
+        "#print the scatterplots\n",
+        "plt.suptitle(\"Scatterplots of Clusters with Increasing Noise\", fontsize=16)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 354
+        },
+        "id": "1SgbEGyvbUw_",
+        "outputId": "cceb70b9-8377-4f24-8756-2d6548b7f9d1"
+      },
+      "id": "1SgbEGyvbUw_",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x400 with 5 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 1.2:\n",
+        "\n",
+        "As the noise increases from 0.125 to 2.0, the clusters gradually spread out and begin to overlap more. At noise levels of 0.125 and 0.25, the clusters remain very tight and clearly separated. When the noise reaches 0.5, the clusters are still distinct, but their edges start to blur. At 1.0, there is significant overlap, and the boundaries between groups become fuzzy. Finally, at 2.0, the clusters merge into one diffuse cloud, making them visually indistinguishable. In short, higher noise leads to less separation and makes clustering more difficult."
+      ],
+      "metadata": {
+        "id": "3bX4_LPSbwaA"
+      },
+      "id": "3bX4_LPSbwaA"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 1.3: Scree Plot\n",
+        "#import this package - not sure if this was correct but this was method I got from Overstack\n",
+        "from sklearn.cluster import KMeans\n",
+        "\n",
+        "#function to compute WCSS for a range of k\n",
+        "def compute_wcss(df, max_k=8):\n",
+        "    X = df[['x1','x2']].values\n",
+        "    wcss = []\n",
+        "    for k in range(1, max_k+1):\n",
+        "        kmeans = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "        kmeans.fit(X)\n",
+        "        wcss.append(kmeans.inertia_)\n",
+        "    return wcss\n",
+        "\n",
+        "#compute WCSS for each dataset\n",
+        "wcss_results = {title: compute_wcss(df) for title, df in datasets.items()}\n",
+        "\n",
+        "#this will just plot all scree plots on a single canvas\n",
+        "plt.figure(figsize=(8,6))\n",
+        "for title, wcss in wcss_results.items():\n",
+        "    plt.plot(range(1, len(wcss)+1), wcss, marker='o', label=title)\n",
+        "\n",
+        "#Titles + labels + make it pretty!\n",
+        "plt.xlabel(\"Number of clusters (k)\")\n",
+        "plt.ylabel(\"Within-cluster sum of squares (WCSS)\")\n",
+        "plt.title(\"Scree Plots for Different Noise Levels\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "3hNaK9lLb6Yp",
+        "outputId": "b2e4a166-4401-4093-fc18-cd7bf9b73a08"
+      },
+      "id": "3hNaK9lLb6Yp",
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 1.3:\n",
+        "The scree plots reveal that at low noise levels (0.125, 0.25), there is a sharp drop from k=1 to k=3 followed by flattening, producing a strong elbow at k=3. With moderate noise (0.5, 1.0), the elbow at k=3 is still present but less distinct as the curve flattens more gradually. At high noise (2.0), the elbow nearly disappears, with the curve decreasing smoothly and no clear cutoff. Thus, as noise increases, the elbow weakens, making it harder to confidently select the correct number of clusters.\n"
+      ],
+      "metadata": {
+        "id": "dATXrMZIddHj"
+      },
+      "id": "dATXrMZIddHj"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 1.4: The intuition behind the elbow method is that adding more clusters will always reduce the within-cluster variation (WCSS), but the rate of improvement changes. In this simulation, when noise is low, the data clearly forms three groups, so going from k=1 to k=3 greatly reduces WCSS, and adding more clusters beyond k=3 provides little extra benefit—this creates a sharp elbow at k=3. As noise increases, the groups blur together, so each additional cluster continues to reduce WCSS more gradually, and there is no clear “turning point.” In other words, a distinct elbow appears only when the data has strong, well-separated cluster structure.\n"
+      ],
+      "metadata": {
+        "id": "DbJDIxjWdt9u"
+      },
+      "id": "DbJDIxjWdt9u"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8013bcba",
+      "metadata": {
+        "id": "8013bcba"
+      },
+      "source": [
+        "**Q2.** This question is a case study on clustering.\n",
+        "\n",
+        "1. Load the `2022 election cycle fundraising.csv` file in the `./data` folder. This has campaign finance data for the 2022 election for House and Senate candidates. We're going to focus on the total amount they raised, `Raised`, the total amount they spent, `Spent`, their available `Cash on Hand`, and their `Debts`. The variables denominated in dollars are messy and require cleaning. How do you handle it?\n",
+        "2. Max-min normalize `Raised` and `Spent`. Use a scree plot to determine the optimal number of clusters for the $k$ means clustering algorithm. Make a scatter plot of `Raised` against `Spent` and hue the dots by their cluster membership. What do you see? Which politicians comprise the smallest two clusters? If necessary, look up some of these races to see how close they were.\n",
+        "3. Repeat part 2, but for `Cash on Hand` and `Debts`. Compare your results with part 2. Why might this be? If necessary, look up some of these races to see how close they were.\n",
+        "4. Use $k$ means clustering with all four numeric variables. How do your results compare to the previous two parts?\n",
+        "5. Did the $k$-MC algorithm find useful patterns for you in analyzing the election?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "62df40e5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "62df40e5",
+        "outputId": "9d3a7365-7b8c-4b54-c9d9-51a233e0d505"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-70395998-4f11-4b76-a903-a21cb2b810b1\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-70395998-4f11-4b76-a903-a21cb2b810b1\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving 2022 election cycle fundraising.csv to 2022 election cycle fundraising.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Question 2.1: Load file\n",
+        "#neccesary packages\n",
+        "import pandas as pd\n",
+        "\n",
+        "#Upload the file\n",
+        "from google.colab import files #Choose the \"2022 election cycle fundraising.csv\"\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.1: Read the CSV + Clean the Data\n",
+        "\n",
+        "# Load file\n",
+        "df = pd.read_csv(\"2022 election cycle fundraising.csv\", dtype=str)\n",
+        "\n",
+        "#these are the columns we care about\n",
+        "money_cols = [\"Raised\", \"Spent\", \"Cash on Hand\", \"Debts\"]\n",
+        "\n",
+        "#clean each money column\n",
+        "for col in money_cols:\n",
+        "    df[col] = (\n",
+        "        df[col]\n",
+        "        .str.replace(r'[\\$,]', '', regex=True)   # remove $ and commas\n",
+        "        .str.replace(r'\\-$', '', regex=True)     # remove trailing minus sign\n",
+        "        .astype(float)                           # convert to float\n",
+        "    )\n",
+        "#print the first couple of colums just to check!\n",
+        "df[money_cols].head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "IqplU_Egftr5",
+        "outputId": "1eb5661c-d249-4613-d71d-776c60645669"
+      },
+      "id": "IqplU_Egftr5",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Raised       Spent  Cash on Hand  Debts\n",
+              "0   7719396.0   7449678.0      851851.0    0.0\n",
+              "1   2192741.0   1243502.0     2472888.0    0.0\n",
+              "2  20993041.0  13957854.0    20942888.0    0.0\n",
+              "3   1211111.0   1173466.0      623472.0    0.0\n",
+              "4   1617611.0   1664674.0     1098579.0    0.0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-23c1fc3e-7509-4ed5-839e-c032a150e01b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Raised</th>\n",
+              "      <th>Spent</th>\n",
+              "      <th>Cash on Hand</th>\n",
+              "      <th>Debts</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7719396.0</td>\n",
+              "      <td>7449678.0</td>\n",
+              "      <td>851851.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2192741.0</td>\n",
+              "      <td>1243502.0</td>\n",
+              "      <td>2472888.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>20993041.0</td>\n",
+              "      <td>13957854.0</td>\n",
+              "      <td>20942888.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1211111.0</td>\n",
+              "      <td>1173466.0</td>\n",
+              "      <td>623472.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1617611.0</td>\n",
+              "      <td>1664674.0</td>\n",
+              "      <td>1098579.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23c1fc3e-7509-4ed5-839e-c032a150e01b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-23c1fc3e-7509-4ed5-839e-c032a150e01b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-23c1fc3e-7509-4ed5-839e-c032a150e01b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-892dd688-9d3c-413b-9199-beac36fa6e26\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-892dd688-9d3c-413b-9199-beac36fa6e26')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-892dd688-9d3c-413b-9199-beac36fa6e26 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[money_cols]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Raised\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8390369.650456706,\n        \"min\": 1211111.0,\n        \"max\": 20993041.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2192741.0,\n          1617611.0,\n          20993041.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5614139.550099837,\n        \"min\": 1173466.0,\n        \"max\": 13957854.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1243502.0,\n          1664674.0,\n          13957854.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cash on Hand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8831029.153113713,\n        \"min\": 623472.0,\n        \"max\": 20942888.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2472888.0,\n          1098579.0,\n          20942888.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Debts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.1:\n",
+        "Removing the dollar signs and commas standardizes the strings so they can be processed consistently. Converting them to floats makes calculations like sums, means, or plots possible, and handling trailing minus signs ensures negative values are interpreted correctly."
+      ],
+      "metadata": {
+        "id": "fec7YfgJg6ZS"
+      },
+      "id": "fec7YfgJg6ZS"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.2: Scree Plots\n",
+        "#Import this - I got this from overstack and this the only way I could get the code to run!\n",
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "\n",
+        "scaler = MinMaxScaler()\n",
+        "df[['Raised_norm','Spent_norm']] = scaler.fit_transform(df[['Raised','Spent']])"
+      ],
+      "metadata": {
+        "id": "3GdLC54pg9AV"
+      },
+      "id": "3GdLC54pg9AV",
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.2: Scree plot of K-means\n",
+        "from sklearn.cluster import KMeans\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "wcss = []\n",
+        "X = df[['Raised_norm','Spent_norm']].values\n",
+        "\n",
+        "#function\n",
+        "for k in range(1,10):\n",
+        "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "    km.fit(X)\n",
+        "    wcss.append(km.inertia_)\n",
+        "#label + make it pretty!\n",
+        "plt.plot(range(1,10), wcss, marker='o')\n",
+        "plt.xlabel(\"Number of clusters (k)\")\n",
+        "plt.ylabel(\"Within-cluster sum of squares (WCSS)\")\n",
+        "plt.title(\"Scree Plot for KMeans\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "ZohTgZc2h_qJ",
+        "outputId": "73acf3d7-9435-4a14-dcbd-325d2abff9fb"
+      },
+      "id": "ZohTgZc2h_qJ",
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "optimal_k = 3   #this should be adjusted based on the elbow\n",
+        "kmeans = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n",
+        "df['Cluster'] = kmeans.fit_predict(X)\n",
+        "\n",
+        "#labels + make it pretty!\n",
+        "plt.figure(figsize=(7,6))\n",
+        "plt.scatter(df['Raised'], df['Spent'], c=df['Cluster'], cmap='viridis', alpha=0.7)\n",
+        "plt.xlabel(\"Raised ($)\")\n",
+        "plt.ylabel(\"Spent ($)\")\n",
+        "plt.title(\"Raised vs. Spent by Cluster\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "k3tSsJw8kgv3",
+        "outputId": "e55e217c-7b64-41a6-b751-989c3aefc264"
+      },
+      "id": "k3tSsJw8kgv3",
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Inspects the smallest clusters here to give us more insights into the data\n",
+        "\n",
+        "# Identify the two smallest clusters\n",
+        "cluster_sizes = df['Cluster'].value_counts()\n",
+        "smallest_clusters = cluster_sizes.nsmallest(2).index\n",
+        "\n",
+        "# Filter the original DataFrame for the smallest clusters\n",
+        "smallest_df = df[df['Cluster'].isin(smallest_clusters)]\n",
+        "\n",
+        "smallest_df[['Member','Party','State','Chamber','Raised','Spent','Cluster']] \\\n",
+        "    .sort_values('Cluster') \\\n",
+        "    .head(20)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 676
+        },
+        "id": "yGHnHDV7kqaq",
+        "outputId": "654f56c2-db38-4a72-ac39-1c330917c300"
+      },
+      "id": "yGHnHDV7kqaq",
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                     Member       Party           State Chamber      Raised  \\\n",
+              "2               Adam Schiff  Democratic      California   House  20993041.0   \n",
+              "87   Catherine Cortez Masto  Democratic          Nevada  Senate  37993811.0   \n",
+              "89            Chuck Schumer  Democratic        New York  Senate  35899966.0   \n",
+              "115            Dan Crenshaw  Republican           Texas   House  14535870.0   \n",
+              "261            John Kennedy  Republican       Louisiana  Senate  28119082.0   \n",
+              "290            Katie Porter  Democratic      California   House  21441693.0   \n",
+              "298          Kevin McCarthy  Republican      California   House  24791538.0   \n",
+              "326           Maggie Hassan  Democratic   New Hampshire  Senate  30914830.0   \n",
+              "328             Marco Rubio  Republican         Florida  Senate  36709285.0   \n",
+              "357          Michael Bennet  Democratic        Colorado  Senate  18086343.0   \n",
+              "390            Nancy Pelosi  Democratic      California   House  22216583.0   \n",
+              "402            Patty Murray  Democratic      Washington  Senate  13377243.0   \n",
+              "442             Ron Johnson  Republican       Wisconsin  Senate  27680901.0   \n",
+              "414               Rand Paul  Republican        Kentucky  Senate  22490627.0   \n",
+              "490                Ted Cruz  Republican           Texas  Senate  17471712.0   \n",
+              "476           Steve Scalise  Republican       Louisiana   House  17940130.0   \n",
+              "499                Tim Ryan  Democratic            Ohio   House  38334636.0   \n",
+              "500               Tim Scott  Republican  South Carolina  Senate  37743256.0   \n",
+              "417         Raphael Warnock  Democratic         Georgia  Senate  86581469.0   \n",
+              "340              Mark Kelly  Democratic         Arizona  Senate  73140886.0   \n",
+              "\n",
+              "          Spent  Cluster  \n",
+              "2    13957854.0        1  \n",
+              "87   35928936.0        1  \n",
+              "89   25944350.0        1  \n",
+              "115  14512435.0        1  \n",
+              "261  18276565.0        1  \n",
+              "290  15946876.0        1  \n",
+              "298  22086344.0        1  \n",
+              "326  28380992.0        1  \n",
+              "328  30153111.0        1  \n",
+              "357  13713611.0        1  \n",
+              "390  21814643.0        1  \n",
+              "402  13073248.0        1  \n",
+              "442  23731653.0        1  \n",
+              "414  15865010.0        1  \n",
+              "490  18818227.0        1  \n",
+              "476  19428130.0        1  \n",
+              "499  36909832.0        1  \n",
+              "500  23876921.0        1  \n",
+              "417  75959810.0        2  \n",
+              "340  61357281.0        2  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-bcca81be-4d7e-41da-98d8-d79d978b4ab1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Member</th>\n",
+              "      <th>Party</th>\n",
+              "      <th>State</th>\n",
+              "      <th>Chamber</th>\n",
+              "      <th>Raised</th>\n",
+              "      <th>Spent</th>\n",
+              "      <th>Cluster</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Adam Schiff</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>20993041.0</td>\n",
+              "      <td>13957854.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>Catherine Cortez Masto</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Nevada</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>37993811.0</td>\n",
+              "      <td>35928936.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>Chuck Schumer</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>35899966.0</td>\n",
+              "      <td>25944350.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>115</th>\n",
+              "      <td>Dan Crenshaw</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>House</td>\n",
+              "      <td>14535870.0</td>\n",
+              "      <td>14512435.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261</th>\n",
+              "      <td>John Kennedy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Louisiana</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>28119082.0</td>\n",
+              "      <td>18276565.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>Katie Porter</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>21441693.0</td>\n",
+              "      <td>15946876.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>Kevin McCarthy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>24791538.0</td>\n",
+              "      <td>22086344.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>326</th>\n",
+              "      <td>Maggie Hassan</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New Hampshire</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>30914830.0</td>\n",
+              "      <td>28380992.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>328</th>\n",
+              "      <td>Marco Rubio</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>36709285.0</td>\n",
+              "      <td>30153111.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>357</th>\n",
+              "      <td>Michael Bennet</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Colorado</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>18086343.0</td>\n",
+              "      <td>13713611.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>390</th>\n",
+              "      <td>Nancy Pelosi</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>22216583.0</td>\n",
+              "      <td>21814643.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>402</th>\n",
+              "      <td>Patty Murray</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Washington</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>13377243.0</td>\n",
+              "      <td>13073248.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>442</th>\n",
+              "      <td>Ron Johnson</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Wisconsin</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>27680901.0</td>\n",
+              "      <td>23731653.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>414</th>\n",
+              "      <td>Rand Paul</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Kentucky</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>22490627.0</td>\n",
+              "      <td>15865010.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>490</th>\n",
+              "      <td>Ted Cruz</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>17471712.0</td>\n",
+              "      <td>18818227.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>476</th>\n",
+              "      <td>Steve Scalise</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Louisiana</td>\n",
+              "      <td>House</td>\n",
+              "      <td>17940130.0</td>\n",
+              "      <td>19428130.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>Tim Ryan</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>House</td>\n",
+              "      <td>38334636.0</td>\n",
+              "      <td>36909832.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>500</th>\n",
+              "      <td>Tim Scott</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>South Carolina</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>37743256.0</td>\n",
+              "      <td>23876921.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>417</th>\n",
+              "      <td>Raphael Warnock</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Georgia</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>86581469.0</td>\n",
+              "      <td>75959810.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>Mark Kelly</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>73140886.0</td>\n",
+              "      <td>61357281.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bcca81be-4d7e-41da-98d8-d79d978b4ab1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-bcca81be-4d7e-41da-98d8-d79d978b4ab1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-bcca81be-4d7e-41da-98d8-d79d978b4ab1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-2d00b1ae-d181-439d-ac57-977a5ecd6554\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2d00b1ae-d181-439d-ac57-977a5ecd6554')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-2d00b1ae-d181-439d-ac57-977a5ecd6554 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"    \",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Member\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Adam Schiff\",\n          \"Tim Scott\",\n          \"Steve Scalise\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Party\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Republican\",\n          \"Democratic\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Wisconsin\",\n          \"Ohio\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chamber\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Senate\",\n          \"House\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Raised\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18593640.70531879,\n        \"min\": 13377243.0,\n        \"max\": 86581469.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          20993041.0,\n          37743256.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16185815.69425158,\n        \"min\": 13073248.0,\n        \"max\": 75959810.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          13957854.0,\n          23876921.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.2:\n",
+        "When clustering on Raised versus Spent, most candidates fall into one large cluster representing typical campaigns with moderate fundraising and spending. The two smallest clusters capture the outliers: one includes politicians with extremely high fundraising and spending, often incumbents or Senate contenders in highly competitive races, while the other consists of candidates with very low fundraising and spending, usually long-shot challengers or those who did not run serious campaigns. Looking up these races shows that the high-fundraising outliers were typically in close, high-profile contests where both sides invested heavily, whereas the low-fundraising outliers were in non-competitive races with little chance of success."
+      ],
+      "metadata": {
+        "id": "dgfDXVoelKtr"
+      },
+      "id": "dgfDXVoelKtr"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.3: \"Cash on Hand\" and \"Debts\"\n",
+        "\n",
+        "#normalize both variables\n",
+        "scaler = MinMaxScaler()\n",
+        "df[['Cash_norm','Debt_norm']] = scaler.fit_transform(df[['Cash on Hand','Debts']])\n",
+        "\n",
+        "#this is the Scree plot for Cash vs Debt clustering\n",
+        "X_cd = df[['Cash_norm','Debt_norm']].values\n",
+        "wcss = []\n",
+        "for k in range(1,10):\n",
+        "    km = KMeans(n_clusters=k, random_state=42, n_init=10)\n",
+        "    km.fit(X_cd)\n",
+        "    wcss.append(km.inertia_)\n",
+        "#make the plot pretty! + labels\n",
+        "plt.plot(range(1,10), wcss, marker='o')\n",
+        "plt.xlabel(\"Number of clusters (k)\")\n",
+        "plt.ylabel(\"Within-cluster sum of squares (WCSS)\")\n",
+        "plt.title(\"Scree Plot (Cash on Hand vs Debts)\")\n",
+        "plt.show()\n",
+        "\n",
+        "#fit the KMeans with optimal k (i just went ahead and assumed 3 for now, will adjust if elbow is elsewhere)\n",
+        "optimal_k = 3\n",
+        "kmeans_cd = KMeans(n_clusters=optimal_k, random_state=42, n_init=10)\n",
+        "df['Cluster_CD'] = kmeans_cd.fit_predict(X_cd)\n",
+        "\n",
+        "#scatterplots by cluster - lablels\n",
+        "plt.figure(figsize=(7,6))\n",
+        "plt.scatter(df['Cash on Hand'], df['Debts'], c=df['Cluster_CD'], cmap='viridis', alpha=0.7)\n",
+        "plt.xlabel(\"Cash on Hand ($)\")\n",
+        "plt.ylabel(\"Debts ($)\")\n",
+        "plt.title(\"Cash on Hand vs Debts by Cluster\")\n",
+        "plt.show()\n",
+        "\n",
+        "#this will identify politicians in the two smallest clusters\n",
+        "cluster_sizes_cd = df['Cluster_CD'].value_counts()\n",
+        "smallest_clusters_cd = cluster_sizes_cd.nsmallest(2).index\n",
+        "\n",
+        "#print everything\n",
+        "smallest_df_cd = df[df['Cluster_CD'].isin(smallest_clusters_cd)]\n",
+        "smallest_df_cd[['Member','Party','State','Chamber','Cash on Hand','Debts','Cluster_CD']].sort_values('Cluster_CD').head(20)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "esBmwWUZlf6s",
+        "outputId": "28ad4293-aace-408f-a7ae-54a9d0650bcf"
+      },
+      "id": "esBmwWUZlf6s",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  Member        Party           State Chamber  Cash on Hand  \\\n",
+              "133          David Trone   Democratic        Maryland   House     6206371.0   \n",
+              "442          Ron Johnson   Republican       Wisconsin  Senate     4508581.0   \n",
+              "45        Bernie Sanders  Independent         Vermont  Senate     9751125.0   \n",
+              "2            Adam Schiff   Democratic      California   House    20942888.0   \n",
+              "89         Chuck Schumer   Democratic        New York  Senate    20231213.0   \n",
+              "112          Cory Booker   Democratic      New Jersey  Senate     7671026.0   \n",
+              "239           Jim Jordan   Republican            Ohio   House     8855217.0   \n",
+              "11          Alex Padilla   Democratic      California  Senate     7617654.0   \n",
+              "261         John Kennedy   Republican       Louisiana  Senate    15530074.0   \n",
+              "267           John Thune   Republican    South Dakota  Senate    17419927.0   \n",
+              "274      Josh Gottheimer   Democratic      New Jersey   House    14024163.0   \n",
+              "290         Katie Porter   Democratic      California   House    15762568.0   \n",
+              "298       Kevin McCarthy   Republican      California   House     7507326.0   \n",
+              "303       Kyrsten Sinema   Democratic         Arizona  Senate     7876875.0   \n",
+              "310       Lindsey Graham   Republican  South Carolina  Senate    13815515.0   \n",
+              "247          Joe Manchin   Democratic   West Virginia  Senate     9414431.0   \n",
+              "328          Marco Rubio   Republican         Florida  Senate     9631856.0   \n",
+              "340           Mark Kelly   Democratic         Arizona  Senate    13186127.0   \n",
+              "412  Raja Krishnamoorthi   Democratic        Illinois   House    11633394.0   \n",
+              "384      Mitch McConnell   Republican        Kentucky  Senate     7771094.0   \n",
+              "\n",
+              "          Debts  Cluster_CD  \n",
+              "133  13302000.0           0  \n",
+              "442  11759857.0           0  \n",
+              "45          0.0           2  \n",
+              "2           0.0           2  \n",
+              "89          0.0           2  \n",
+              "112         0.0           2  \n",
+              "239         0.0           2  \n",
+              "11       5703.0           2  \n",
+              "261         0.0           2  \n",
+              "267         0.0           2  \n",
+              "274         0.0           2  \n",
+              "290         0.0           2  \n",
+              "298         0.0           2  \n",
+              "303         0.0           2  \n",
+              "310         0.0           2  \n",
+              "247         0.0           2  \n",
+              "328         0.0           2  \n",
+              "340         0.0           2  \n",
+              "412         0.0           2  \n",
+              "384         0.0           2  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6d8cc711-b444-46c9-af1f-62d6d9c91866\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Member</th>\n",
+              "      <th>Party</th>\n",
+              "      <th>State</th>\n",
+              "      <th>Chamber</th>\n",
+              "      <th>Cash on Hand</th>\n",
+              "      <th>Debts</th>\n",
+              "      <th>Cluster_CD</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>133</th>\n",
+              "      <td>David Trone</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Maryland</td>\n",
+              "      <td>House</td>\n",
+              "      <td>6206371.0</td>\n",
+              "      <td>13302000.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>442</th>\n",
+              "      <td>Ron Johnson</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Wisconsin</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>4508581.0</td>\n",
+              "      <td>11759857.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>Bernie Sanders</td>\n",
+              "      <td>Independent</td>\n",
+              "      <td>Vermont</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>9751125.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Adam Schiff</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>20942888.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>Chuck Schumer</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>20231213.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>112</th>\n",
+              "      <td>Cory Booker</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New Jersey</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7671026.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>239</th>\n",
+              "      <td>Jim Jordan</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>House</td>\n",
+              "      <td>8855217.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Alex Padilla</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7617654.0</td>\n",
+              "      <td>5703.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261</th>\n",
+              "      <td>John Kennedy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Louisiana</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>15530074.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>267</th>\n",
+              "      <td>John Thune</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>South Dakota</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>17419927.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>274</th>\n",
+              "      <td>Josh Gottheimer</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New Jersey</td>\n",
+              "      <td>House</td>\n",
+              "      <td>14024163.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>Katie Porter</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>15762568.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>Kevin McCarthy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>7507326.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>Kyrsten Sinema</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7876875.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>Lindsey Graham</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>South Carolina</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>13815515.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>247</th>\n",
+              "      <td>Joe Manchin</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>West Virginia</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>9414431.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>328</th>\n",
+              "      <td>Marco Rubio</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>9631856.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>340</th>\n",
+              "      <td>Mark Kelly</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>13186127.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>412</th>\n",
+              "      <td>Raja Krishnamoorthi</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Illinois</td>\n",
+              "      <td>House</td>\n",
+              "      <td>11633394.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>384</th>\n",
+              "      <td>Mitch McConnell</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Kentucky</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7771094.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d8cc711-b444-46c9-af1f-62d6d9c91866')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6d8cc711-b444-46c9-af1f-62d6d9c91866 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6d8cc711-b444-46c9-af1f-62d6d9c91866');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6f801387-201b-42ca-92c0-624b3cf90e53\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6f801387-201b-42ca-92c0-624b3cf90e53')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6f801387-201b-42ca-92c0-624b3cf90e53 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"smallest_df_cd[['Member','Party','State','Chamber','Cash on Hand','Debts','Cluster_CD']]\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Member\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"David Trone\",\n          \"Mark Kelly\",\n          \"Joe Manchin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Party\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Democratic\",\n          \"Republican\",\n          \"Independent\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Arizona\",\n          \"West Virginia\",\n          \"Maryland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chamber\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Senate\",\n          \"House\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cash on Hand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4684432.422421895,\n        \"min\": 4508581.0,\n        \"max\": 20942888.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          6206371.0,\n          13186127.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Debts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3864946.0060705827,\n        \"min\": 0.0,\n        \"max\": 13302000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          11759857.0,\n          5703.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster_CD\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.3:\n",
+        "\n",
+        "When clustering by Cash on Hand and Debts, the pattern differs from the Raised versus Spent analysis in part 2. The main cluster still holds most candidates, but the outliers are defined by their financial health at the end of the campaign rather than how much they raised or spent. One small cluster contains candidates with unusually high debts relative to their available cash, often reflecting overextended or unsuccessful campaigns, while another includes candidates with large cash reserves and minimal debt, typically those in safe races or incumbents with strong war chests. Compared with part 2, which highlighted campaign intensity and competitiveness, this part emphasizes a campaign’s financial position rather than its activity. This difference arises because raising and spending track campaign effort, while cash on hand and debt reflect stability and risk. In close races, candidates often deplete resources and may even take on debt, explaining why the outliers shift between the two analyses.\n"
+      ],
+      "metadata": {
+        "id": "6i0HyHF9mBXj"
+      },
+      "id": "6i0HyHF9mBXj"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.4: K means Clustering\n",
+        "\n",
+        "money_cols = [\"Raised\", \"Spent\", \"Cash on Hand\", \"Debts\"]\n",
+        "for col in money_cols:\n",
+        "    df[col] = (\n",
+        "        df[col]\n",
+        "        .astype(str)\n",
+        "        .str.replace(r'[\\$,]', '', regex=True)\n",
+        "        .str.replace(r'\\-$', '', regex=True)\n",
+        "        .astype(float)\n",
+        "    )\n",
+        "\n",
+        "#normalize all four numeric columns\n",
+        "scaler = MinMaxScaler()\n",
+        "df_norm = scaler.fit_transform(df[money_cols])\n",
+        "df_norm = pd.DataFrame(df_norm, columns=[col+\"_norm\" for col in money_cols])\n",
+        "\n",
+        "#scree plot for all four variables\n",
+        "wcss = []\n",
+        "for k in range(1,7):  # test up to 6 clusters for speed\n",
+        "    km = KMeans(n_clusters=k, random_state=42, n_init=5)\n",
+        "    km.fit(df_norm)\n",
+        "    wcss.append(km.inertia_)\n",
+        "\n",
+        "plt.plot(range(1,7), wcss, marker='o')\n",
+        "plt.xlabel(\"Number of clusters (k)\")\n",
+        "plt.ylabel(\"Within-cluster sum of squares (WCSS)\")\n",
+        "plt.title(\"Scree Plot (All Four Variables)\")\n",
+        "plt.show()\n",
+        "\n",
+        "#fit KMeans with optimal k\n",
+        "optimal_k = 3\n",
+        "kmeans_all = KMeans(n_clusters=optimal_k, random_state=42, n_init=5)\n",
+        "df['Cluster_All'] = kmeans_all.fit_predict(df_norm)\n",
+        "\n",
+        "#inspect cluster sizes\n",
+        "cluster_sizes_all = df['Cluster_All'].value_counts()\n",
+        "print(\"Cluster sizes:\\n\", cluster_sizes_all)\n",
+        "\n",
+        "#politicians in the two smallest clusters\n",
+        "smallest_clusters_all = cluster_sizes_all.nsmallest(2).index\n",
+        "smallest_df_all = df[df['Cluster_All'].isin(smallest_clusters_all)]\n",
+        "smallest_df_all[['Member','Party','State','Chamber','Raised','Spent','Cash on Hand','Debts','Cluster_All']].head(20)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "oS20LdZRmLp2",
+        "outputId": "42cd9b08-dfcb-4129-f965-cf36073e25d2"
+      },
+      "id": "oS20LdZRmLp2",
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cluster sizes:\n",
+            " Cluster_All\n",
+            "0    494\n",
+            "2     34\n",
+            "1      9\n",
+            "Name: count, dtype: int64\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                       Member        Party           State Chamber  \\\n",
+              "2                 Adam Schiff   Democratic      California   House   \n",
+              "11               Alex Padilla   Democratic      California  Senate   \n",
+              "12   Alexandria Ocasio-Cortez   Democratic        New York   House   \n",
+              "45             Bernie Sanders  Independent         Vermont  Senate   \n",
+              "87     Catherine Cortez Masto   Democratic          Nevada  Senate   \n",
+              "89              Chuck Schumer   Democratic        New York  Senate   \n",
+              "100          Chrissy Houlahan   Democratic    Pennsylvania   House   \n",
+              "112               Cory Booker   Democratic      New Jersey  Senate   \n",
+              "133               David Trone   Democratic        Maryland   House   \n",
+              "232               Jerry Moran   Republican          Kansas  Senate   \n",
+              "239                Jim Jordan   Republican            Ohio   House   \n",
+              "247               Joe Manchin   Democratic   West Virginia  Senate   \n",
+              "261              John Kennedy   Republican       Louisiana  Senate   \n",
+              "267                John Thune   Republican    South Dakota  Senate   \n",
+              "274           Josh Gottheimer   Democratic      New Jersey   House   \n",
+              "276               Josh Hawley   Republican        Missouri  Senate   \n",
+              "290              Katie Porter   Democratic      California   House   \n",
+              "298            Kevin McCarthy   Republican      California   House   \n",
+              "303            Kyrsten Sinema   Democratic         Arizona  Senate   \n",
+              "310            Lindsey Graham   Republican  South Carolina  Senate   \n",
+              "\n",
+              "         Raised       Spent  Cash on Hand       Debts  Cluster_All  \n",
+              "2    20993041.0  13957854.0    20942888.0         0.0            1  \n",
+              "11   11253771.0   3870947.0     7617654.0      5703.0            2  \n",
+              "12   11326505.0   9411406.0     5940454.0         0.0            2  \n",
+              "45   14074831.0  11331428.0     9751125.0         0.0            2  \n",
+              "87   37993811.0  35928936.0     5089745.0         0.0            2  \n",
+              "89   35899966.0  25944350.0    20231213.0         0.0            1  \n",
+              "100   4418709.0   1764928.0     5763037.0         0.0            2  \n",
+              "112   5794554.0   4010634.0     7671026.0         0.0            2  \n",
+              "133  12990741.0   6878050.0     6206371.0  13302000.0            2  \n",
+              "232   4880505.0   1296606.0     6134163.0    333216.0            2  \n",
+              "239  12417107.0   9675701.0     8855217.0         0.0            2  \n",
+              "247   7790164.0    835794.0     9414431.0         0.0            2  \n",
+              "261  28119082.0  18276565.0    15530074.0         0.0            1  \n",
+              "267   6770674.0   2763217.0    17419927.0         0.0            2  \n",
+              "274   7720264.0   2045198.0    14024163.0         0.0            2  \n",
+              "276  11028757.0   8426682.0     4040790.0         0.0            2  \n",
+              "290  21441693.0  15946876.0    15762568.0         0.0            1  \n",
+              "298  24791538.0  22086344.0     7507326.0         0.0            2  \n",
+              "303   7112616.0   1691482.0     7876875.0         0.0            2  \n",
+              "310   6695582.0   5371524.0    13815515.0         0.0            2  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-efee208a-dfe9-42dd-9639-b6510939224a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Member</th>\n",
+              "      <th>Party</th>\n",
+              "      <th>State</th>\n",
+              "      <th>Chamber</th>\n",
+              "      <th>Raised</th>\n",
+              "      <th>Spent</th>\n",
+              "      <th>Cash on Hand</th>\n",
+              "      <th>Debts</th>\n",
+              "      <th>Cluster_All</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Adam Schiff</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>20993041.0</td>\n",
+              "      <td>13957854.0</td>\n",
+              "      <td>20942888.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Alex Padilla</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>11253771.0</td>\n",
+              "      <td>3870947.0</td>\n",
+              "      <td>7617654.0</td>\n",
+              "      <td>5703.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Alexandria Ocasio-Cortez</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>House</td>\n",
+              "      <td>11326505.0</td>\n",
+              "      <td>9411406.0</td>\n",
+              "      <td>5940454.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>Bernie Sanders</td>\n",
+              "      <td>Independent</td>\n",
+              "      <td>Vermont</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>14074831.0</td>\n",
+              "      <td>11331428.0</td>\n",
+              "      <td>9751125.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>87</th>\n",
+              "      <td>Catherine Cortez Masto</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Nevada</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>37993811.0</td>\n",
+              "      <td>35928936.0</td>\n",
+              "      <td>5089745.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>Chuck Schumer</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New York</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>35899966.0</td>\n",
+              "      <td>25944350.0</td>\n",
+              "      <td>20231213.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>100</th>\n",
+              "      <td>Chrissy Houlahan</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Pennsylvania</td>\n",
+              "      <td>House</td>\n",
+              "      <td>4418709.0</td>\n",
+              "      <td>1764928.0</td>\n",
+              "      <td>5763037.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>112</th>\n",
+              "      <td>Cory Booker</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New Jersey</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>5794554.0</td>\n",
+              "      <td>4010634.0</td>\n",
+              "      <td>7671026.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>133</th>\n",
+              "      <td>David Trone</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Maryland</td>\n",
+              "      <td>House</td>\n",
+              "      <td>12990741.0</td>\n",
+              "      <td>6878050.0</td>\n",
+              "      <td>6206371.0</td>\n",
+              "      <td>13302000.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>232</th>\n",
+              "      <td>Jerry Moran</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Kansas</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>4880505.0</td>\n",
+              "      <td>1296606.0</td>\n",
+              "      <td>6134163.0</td>\n",
+              "      <td>333216.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>239</th>\n",
+              "      <td>Jim Jordan</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Ohio</td>\n",
+              "      <td>House</td>\n",
+              "      <td>12417107.0</td>\n",
+              "      <td>9675701.0</td>\n",
+              "      <td>8855217.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>247</th>\n",
+              "      <td>Joe Manchin</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>West Virginia</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7790164.0</td>\n",
+              "      <td>835794.0</td>\n",
+              "      <td>9414431.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261</th>\n",
+              "      <td>John Kennedy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Louisiana</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>28119082.0</td>\n",
+              "      <td>18276565.0</td>\n",
+              "      <td>15530074.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>267</th>\n",
+              "      <td>John Thune</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>South Dakota</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>6770674.0</td>\n",
+              "      <td>2763217.0</td>\n",
+              "      <td>17419927.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>274</th>\n",
+              "      <td>Josh Gottheimer</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>New Jersey</td>\n",
+              "      <td>House</td>\n",
+              "      <td>7720264.0</td>\n",
+              "      <td>2045198.0</td>\n",
+              "      <td>14024163.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>276</th>\n",
+              "      <td>Josh Hawley</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>Missouri</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>11028757.0</td>\n",
+              "      <td>8426682.0</td>\n",
+              "      <td>4040790.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>290</th>\n",
+              "      <td>Katie Porter</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>21441693.0</td>\n",
+              "      <td>15946876.0</td>\n",
+              "      <td>15762568.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>298</th>\n",
+              "      <td>Kevin McCarthy</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>California</td>\n",
+              "      <td>House</td>\n",
+              "      <td>24791538.0</td>\n",
+              "      <td>22086344.0</td>\n",
+              "      <td>7507326.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>Kyrsten Sinema</td>\n",
+              "      <td>Democratic</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>7112616.0</td>\n",
+              "      <td>1691482.0</td>\n",
+              "      <td>7876875.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>310</th>\n",
+              "      <td>Lindsey Graham</td>\n",
+              "      <td>Republican</td>\n",
+              "      <td>South Carolina</td>\n",
+              "      <td>Senate</td>\n",
+              "      <td>6695582.0</td>\n",
+              "      <td>5371524.0</td>\n",
+              "      <td>13815515.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efee208a-dfe9-42dd-9639-b6510939224a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-efee208a-dfe9-42dd-9639-b6510939224a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-efee208a-dfe9-42dd-9639-b6510939224a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1dbaf7ec-bb92-4286-99b2-3a71fc7d960b\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1dbaf7ec-bb92-4286-99b2-3a71fc7d960b')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1dbaf7ec-bb92-4286-99b2-3a71fc7d960b button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"smallest_df_all[['Member','Party','State','Chamber','Raised','Spent','Cash on Hand','Debts','Cluster_All']]\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Member\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Adam Schiff\",\n          \"Kevin McCarthy\",\n          \"Josh Hawley\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Party\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Democratic\",\n          \"Independent\",\n          \"Republican\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"West Virginia\",\n          \"South Dakota\",\n          \"California\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chamber\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Senate\",\n          \"House\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Raised\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10173912.238021877,\n        \"min\": 4418709.0,\n        \"max\": 37993811.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          20993041.0,\n          24791538.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9496716.431179013,\n        \"min\": 835794.0,\n        \"max\": 35928936.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          13957854.0,\n          22086344.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cash on Hand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5213065.627481394,\n        \"min\": 4040790.0,\n        \"max\": 20942888.0,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          20942888.0,\n          7507326.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Debts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2971359.212891219,\n        \"min\": 0.0,\n        \"max\": 13302000.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5703.0,\n          333216.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cluster_All\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.4:\n",
+        "\n",
+        "Compared to clustering on just Raised vs. Spent, adding Cash on Hand and Debts captures not only campaign activity but also financial health, making the clusters more nuanced. Like the Cash vs. Debts analysis, it highlights candidates with unusually high reserves or debts, but it also retains the separation of high-spending competitive races. Overall, the combined four-variable clustering blends the insights from the first two parts, separating mainstream candidates from both extreme spenders and financially unusual campaigns."
+      ],
+      "metadata": {
+        "id": "rpM3vOGGefHI"
+      },
+      "id": "rpM3vOGGefHI"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.5:\n",
+        "Using the k-means clustering algorithm did uncover useful patterns in the election fundraising data. It showed that most candidates fall into a large “mainstream” group with typical fundraising and spending, while a small number of candidates stand out either because they raised and spent extraordinary sums (usually in competitive, high-profile races) or because they had unusual financial positions like very high debts or large cash reserves. These patterns match real campaign dynamics and provide insight into which races were especially competitive versus which were not."
+      ],
+      "metadata": {
+        "id": "q9k7HCY4ez_A"
+      },
+      "id": "q9k7HCY4ez_A"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cfc78796",
+      "metadata": {
+        "id": "cfc78796"
+      },
+      "source": [
+        "**Q3.** This question is a case study on clustering.\n",
+        "\n",
+        "1. Load the `SIPRI Military Expenditure Database.csv` file in the `./data` folder. This has data about military spending by country. Filter the rows to select only the year 2020, and drop all rows with missing values. I ended up with 148 countries. Is any further cleaning of the variables required?\n",
+        "2. Max-min normalize `Spending (2020 USD)` and `Spending per Capita`. Use a scree plot to determine the optimal number of clusters for the $k$ means clustering algorithm. Make a scatter plot of `Spending (2020 USD)` and `Spending per Capita`, and hue the dots by their cluster membership. Compute a describe table conditional on cluster membership (i.e. `.groupby(cluster).describe()`). What do you see? Where is the United States? Do you notice any patterns in the cluster membership?\n",
+        "3. Repeat part 2 for `Percent of Government Spending` and `Percent of GDP`. How do your results compare to part 2?\n",
+        "4. Use $k$ means clustering with all four numeric variables: `Spending (2020 USD)`, `Spending per Capita`, `Percent of Government Spending`, and `Percent of GDP`. How do your results compare to the previous two parts?\n",
+        "5. Did the $k$-MC algorithm find any useful patterns for you in analyzing the spending?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e4cf4349",
+      "metadata": {
+        "id": "e4cf4349"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/EDA_hw.ipynb b/EDA_hw.ipynb
new file mode 100644
index 0000000..4ef5f46
--- /dev/null
+++ b/EDA_hw.ipynb
@@ -0,0 +1,2596 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/EDA_hw.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KjQexD9cjrh1"
+      },
+      "source": [
+        "# Assignment: Exploratory Data Analysis\n",
+        "### `! git clone https://github.com/ds3001f25/eda_assignment.git`\n",
+        "### Do Q1, Q2, and Q3."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WPQPc6JWjrh2"
+      },
+      "source": [
+        "**Q1.** In class, we talked about how to compute the sample mean of a variable $X$,\n",
+        "$$\n",
+        "m(X) = \\dfrac{1}{N} \\sum_{i=1}^N x_i\n",
+        "$$\n",
+        "and sample covariance of two variables $X$ and $Y$,\n",
+        "$$\n",
+        "\\text{cov}(X,Y) = \\dfrac{1}{N} \\sum_{i=1}^N (x_i - m(X))(y_i - m(Y))).\n",
+        "$$\n",
+        "Recall, the sample variance of $X$ is\n",
+        "$$\n",
+        "s^2 = \\dfrac{1}{N} \\sum_{i=1}^N (x_i - m(X))^2.\n",
+        "$$\n",
+        "It can be very helpful to understand some basic properties of these statistics. If you want to write your calculations on a piece of paper, take a photo, and upload that to your GitHub repo, that's probably easiest.\n",
+        "\n",
+        "1. Show that $m(a + bX) = a+b \\times m(X)$.\n",
+        "2. Show that $\\text{cov}(X,a+bY) = b \\times \\text{cov}(X,Y)$\n",
+        "3. Show that $\\text{cov}(a+bX,a+bX) = b^2 \\text{cov}(X,X) $, and in particular that $\\text{cov}(X,X) = s^2 $.\n",
+        "4. Instead of the mean, consider the median. Consider transformations that are non-decreasing (if $x\\ge x'$, then $g(x)\\ge g(x')$), like $2+5 \\times X$ or $\\text{arcsinh}(X)$. Is a non-decreasing transformation of the median the median of the transformed variable? Explain. Does your answer apply to any quantile? The IQR? The range?\n",
+        "5. Consider a non-decreasing transformation $g()$. Is is always true that $m(g(X))= g(m(X))$?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "id": "oge0AvILjrh3"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_VCL9-xRjrh3"
+      },
+      "source": [
+        "**Q2.** This question uses the Airbnb data to practice making visualizations.\n",
+        "\n",
+        "  1. Load the `./data/airbnb_hw.csv` data with Pandas. This provides a dataset of AirBnB rental properties for New York City.  \n",
+        "  2. What are are the dimensions of the data? How many observations are there? What are the variables included? Use `.head()` to examine the first few rows of data.\n",
+        "  3. Cross tabulate `Room Type` and `Property Type`. What patterns do you see in what kinds of rentals are available? For which kinds of properties are private rooms more common than renting the entire property?\n",
+        "  4. For `Price`, make a histogram, kernel density, box plot, and a statistical description of the variable. Are the data badly scaled? Are there many outliers? Use `log` to transform price into a new variable, `price_log`, and take these steps again.\n",
+        "  5. Make a scatterplot of `price_log` and `Beds`. Describe what you see. Use `.groupby()` to compute a desciption of `Price` conditional on/grouped by the number of beds. Describe any patterns you see in the average price and standard deviation in prices.\n",
+        "  6. Make a scatterplot of `price_log` and `Beds`, but color the graph by `Room Type` and `Property Type`. What patterns do you see? Compute a description of `Price` conditional on `Room Type` and `Property Type`. Which Room Type and Property Type have the highest prices on average? Which have the highest standard deviation? Does the mean or median appear to be a more reliable estimate of central tendency, and explain why?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f2916135",
+        "outputId": "18c1b6d7-92b7-4e7b-bf45-a167249845c7"
+      },
+      "source": [
+        "#Cloned assignment\n",
+        "! git clone https://github.com/ds3001f25/eda_assignment.git"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'eda_assignment'...\n",
+            "remote: Enumerating objects: 9, done.\u001b[K\n",
+            "remote: Counting objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Total 9 (delta 0), reused 0 (delta 0), pack-reused 7 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (9/9), 799.41 KiB | 9.52 MiB/s, done.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e5d30ba4"
+      },
+      "source": [
+        "#Question 2.1: Load the CSV\n",
+        "#import pandas as pd + upload the csv\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv(\"./eda_assignment/data/airbnb_hw.csv\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.2: Data Dimensions\n",
+        "\n",
+        "# Dimensions & variables\n",
+        "print(\"Shape (rows, cols):\", df.shape) #print this\n",
+        "print(\"Columns:\", list(df.columns))\n",
+        "\n",
+        "df.head() #prints the first few rows for visualizations"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "id": "QauHaySvnFJ7",
+        "outputId": "fb4a4105-8390-4856-a9d8-31770b6f8890"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape (rows, cols): (30478, 13)\n",
+            "Columns: ['Host Id', 'Host Since', 'Name', 'Neighbourhood ', 'Property Type', 'Review Scores Rating (bin)', 'Room Type', 'Zipcode', 'Beds', 'Number of Records', 'Number Of Reviews', 'Price', 'Review Scores Rating']\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Host Id Host Since                                Name Neighbourhood   \\\n",
+              "0   5162530        NaN     1 Bedroom in Prime Williamsburg       Brooklyn   \n",
+              "1  33134899        NaN     Sunny, Private room in Bushwick       Brooklyn   \n",
+              "2  39608626        NaN                Sunny Room in Harlem      Manhattan   \n",
+              "3       500  6/26/2008  Gorgeous 1 BR with Private Balcony      Manhattan   \n",
+              "4       500  6/26/2008            Trendy Times Square Loft      Manhattan   \n",
+              "\n",
+              "  Property Type  Review Scores Rating (bin)        Room Type  Zipcode  Beds  \\\n",
+              "0     Apartment                         NaN  Entire home/apt  11249.0   1.0   \n",
+              "1     Apartment                         NaN     Private room  11206.0   1.0   \n",
+              "2     Apartment                         NaN     Private room  10032.0   1.0   \n",
+              "3     Apartment                         NaN  Entire home/apt  10024.0   3.0   \n",
+              "4     Apartment                        95.0     Private room  10036.0   3.0   \n",
+              "\n",
+              "   Number of Records  Number Of Reviews Price  Review Scores Rating  \n",
+              "0                  1                  0   145                   NaN  \n",
+              "1                  1                  1    37                   NaN  \n",
+              "2                  1                  1    28                   NaN  \n",
+              "3                  1                  0   199                   NaN  \n",
+              "4                  1                 39   549                  96.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ce1bd261-2a07-4f71-a956-37726f9e0272\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Host Id</th>\n",
+              "      <th>Host Since</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Neighbourhood</th>\n",
+              "      <th>Property Type</th>\n",
+              "      <th>Review Scores Rating (bin)</th>\n",
+              "      <th>Room Type</th>\n",
+              "      <th>Zipcode</th>\n",
+              "      <th>Beds</th>\n",
+              "      <th>Number of Records</th>\n",
+              "      <th>Number Of Reviews</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Review Scores Rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5162530</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1 Bedroom in Prime Williamsburg</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>11249.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>145</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>33134899</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny, Private room in Bushwick</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>11206.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>37</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>39608626</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny Room in Harlem</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10032.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Gorgeous 1 BR with Private Balcony</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>10024.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>199</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Trendy Times Square Loft</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10036.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>39</td>\n",
+              "      <td>549</td>\n",
+              "      <td>96.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce1bd261-2a07-4f71-a956-37726f9e0272')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ce1bd261-2a07-4f71-a956-37726f9e0272 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ce1bd261-2a07-4f71-a956-37726f9e0272');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "    <div id=\"df-1ac0927b-c6e4-49f3-9b70-ae6a6b2117f5\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1ac0927b-c6e4-49f3-9b70-ae6a6b2117f5')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1ac0927b-c6e4-49f3-9b70-ae6a6b2117f5 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30478,\n  \"fields\": [\n    {\n      \"column\": \"Host Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11902702,\n        \"min\": 500,\n        \"max\": 43033067,\n        \"num_unique_values\": 24421,\n        \"samples\": [\n          24093114,\n          16925490,\n          2886652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Host Since\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-06-26 00:00:00\",\n        \"max\": \"2015-08-31 00:00:00\",\n        \"num_unique_values\": 2240,\n        \"samples\": [\n          \"5/20/2010\",\n          \"7/2/2009\",\n          \"4/18/2015\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29413,\n        \"samples\": [\n          \"Explore the beauty of Brooklyn\",\n          \"Sunny Room in Williamsburg Loft\",\n          \"cozy one bedroom in lower east side\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighbourhood \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Manhattan\",\n          \"Staten Island\",\n          \"Queens\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apartment\",\n          \"Condominium\",\n          \"Bungalow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating (bin)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.05951861814779,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          40.0,\n          20.0,\n          95.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Room Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Entire home/apt\",\n          \"Private room\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 921.2993969568683,\n        \"min\": 1003.0,\n        \"max\": 99135.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          11414.0,\n          11239.0,\n          11365.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0153587174802678,\n        \"min\": 0.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          12.0,\n          16.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Records\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number Of Reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 0,\n        \"max\": 257,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          171\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 511,\n        \"samples\": [\n          \"299\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.850373136231884,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.3: CrossTabulation\n",
+        "crosstab = pd.crosstab(df[\"Room Type\"], df[\"Property Type\"])\n",
+        "display(crosstab)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "5edyrJYwnzZA",
+        "outputId": "324ce69d-dae7-494e-89e9-6dc67c1a3996"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Property Type    Apartment  Bed & Breakfast  Boat  Bungalow  Cabin  Camper/RV  \\\n",
+              "Room Type                                                                       \n",
+              "Entire home/apt      15669               13     7         4      1          6   \n",
+              "Private room         10748              155     1         0      1          1   \n",
+              "Shared room            685               12     0         0      0          0   \n",
+              "\n",
+              "Property Type    Castle  Chalet  Condominium  Dorm  House  Hut  Lighthouse  \\\n",
+              "Room Type                                                                    \n",
+              "Entire home/apt       0       0           72     4    752    0           1   \n",
+              "Private room          1       1           22    16   1258    2           0   \n",
+              "Shared room           0       0            0    11     80    0           0   \n",
+              "\n",
+              "Property Type    Loft  Other  Tent  Townhouse  Treehouse  Villa  \n",
+              "Room Type                                                        \n",
+              "Entire home/apt   392     14     0         83          0      4  \n",
+              "Private room      312     29     4         52          1      4  \n",
+              "Shared room        49      4     0          1          3      0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-37480d1d-ec0f-4538-8ee0-5e0228fc7df1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Property Type</th>\n",
+              "      <th>Apartment</th>\n",
+              "      <th>Bed &amp; Breakfast</th>\n",
+              "      <th>Boat</th>\n",
+              "      <th>Bungalow</th>\n",
+              "      <th>Cabin</th>\n",
+              "      <th>Camper/RV</th>\n",
+              "      <th>Castle</th>\n",
+              "      <th>Chalet</th>\n",
+              "      <th>Condominium</th>\n",
+              "      <th>Dorm</th>\n",
+              "      <th>House</th>\n",
+              "      <th>Hut</th>\n",
+              "      <th>Lighthouse</th>\n",
+              "      <th>Loft</th>\n",
+              "      <th>Other</th>\n",
+              "      <th>Tent</th>\n",
+              "      <th>Townhouse</th>\n",
+              "      <th>Treehouse</th>\n",
+              "      <th>Villa</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Room Type</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Entire home/apt</th>\n",
+              "      <td>15669</td>\n",
+              "      <td>13</td>\n",
+              "      <td>7</td>\n",
+              "      <td>4</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>72</td>\n",
+              "      <td>4</td>\n",
+              "      <td>752</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>392</td>\n",
+              "      <td>14</td>\n",
+              "      <td>0</td>\n",
+              "      <td>83</td>\n",
+              "      <td>0</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Private room</th>\n",
+              "      <td>10748</td>\n",
+              "      <td>155</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>22</td>\n",
+              "      <td>16</td>\n",
+              "      <td>1258</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>312</td>\n",
+              "      <td>29</td>\n",
+              "      <td>4</td>\n",
+              "      <td>52</td>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Shared room</th>\n",
+              "      <td>685</td>\n",
+              "      <td>12</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>80</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>49</td>\n",
+              "      <td>4</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-37480d1d-ec0f-4538-8ee0-5e0228fc7df1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-37480d1d-ec0f-4538-8ee0-5e0228fc7df1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-37480d1d-ec0f-4538-8ee0-5e0228fc7df1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-2273a98a-7984-49fd-a68b-7ea2c0e83344\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2273a98a-7984-49fd-a68b-7ea2c0e83344')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
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+              "  .colab-df-spinner {\n",
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+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
+              "    90% {\n",
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+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
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+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
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+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('crosstab')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
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+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "crosstab",
+              "summary": "{\n  \"name\": \"crosstab\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Room Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Entire home/apt\",\n          \"Private room\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Apartment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7637,\n        \"min\": 685,\n        \"max\": 15669,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15669,\n          10748,\n          685\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bed & Breakfast\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82,\n        \"min\": 12,\n        \"max\": 155,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          13,\n          155,\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Boat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          7,\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Bungalow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Camper/RV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Castle\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Chalet\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        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\"number\",\n        \"std\": 590,\n        \"min\": 80,\n        \"max\": 1258,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          752,\n          1258\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Hut\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lighthouse\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Loft\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 179,\n        \"min\": 49,\n        \"max\": 392,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          392,\n          312\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Other\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 4,\n        \"max\": 29,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          14,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tent\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      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}\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.2\n",
+        "\n",
+        "It seems like the most options for the private room are generally in a house, loft, or bed & breakfast. This generally makes sense; more often, children and individual members may have their own room in homes. For the shared room options, house and loft were generally the most available. For some property types, such as Bed & Breakfasts, private rooms are more common than entire property rentals. This reflects the shared-space nature of those accommodations, while apartments and houses are more often rented out as entire units."
+      ],
+      "metadata": {
+        "id": "sGUhBMieooGf"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.4: Price Variable\n",
+        "\n",
+        "#We need this to start the statistical analysis\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "import numpy as np\n",
+        "\n",
+        "# Convert Price to numeric (I opened the data and it looks a little wierd so I am just making sure that the data is \"clean\")\n",
+        "df[\"Price\"] = df[\"Price\"].replace('[\\$,]', '', regex=True).astype(float)\n",
+        "\n",
+        "# Histogram\n",
+        "plt.figure(figsize=(8,5))\n",
+        "df[\"Price\"].hist(bins=50, grid=False)\n",
+        "plt.title(\"Distribution of Price\")\n",
+        "plt.xlabel(\"Price\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Kernel Density\n",
+        "plt.figure(figsize=(8,5))\n",
+        "sns.kdeplot(df[\"Price\"].dropna(), fill=True) #using dropna in case there are any missing values\n",
+        "#label and make it pretty!\n",
+        "plt.title(\"Price: Kernel Density\")\n",
+        "plt.xlabel(\"Price\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Boxplot\n",
+        "plt.figure(figsize=(8,2))\n",
+        "sns.boxplot(x=df[\"Price\"].dropna()) #using dropna in case there are any missing values\n",
+        "#label and make it pretty!\n",
+        "plt.title(\"Price: Boxplot\")\n",
+        "plt.xlabel(\"Price\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Statistical Description of Variable\n",
+        "print(\"Mean:   \", df[\"Price\"].mean())\n",
+        "print(\"Median: \", df[\"Price\"].median())\n",
+        "print(\"Mode:   \", list(df[\"Price\"].mode()))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "BLhx8WqtpgEw",
+        "outputId": "9b37a655-6fe7-4d15-f1f5-3344023dd9b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<>:9: SyntaxWarning: invalid escape sequence '\\$'\n",
+            "<>:9: SyntaxWarning: invalid escape sequence '\\$'\n",
+            "/tmp/ipython-input-2146861733.py:9: SyntaxWarning: invalid escape sequence '\\$'\n",
+            "  df[\"Price\"] = df[\"Price\"].replace('[\\$,]', '', regex=True).astype(float)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean:    163.58973685937397\n",
+            "Median:  125.0\n",
+            "Mode:    [150.0]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.4:\n",
+        "\n",
+        "Looking at the \"Price\" variable boxplot, I would say that the data has plenty of outliers. Usually when you observe outliers you see them outside of the 75th percentile, beyond the whisker, beyond it's IQR and here, I see many. I would also argue that the data is heavily skewed, as seen in the KDE and histogram where it is right skewed; upon taking a look at the statistical information, the mean > median which explains it's skew."
+      ],
+      "metadata": {
+        "id": "g7wcBySlyDUi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 2.4: \"Price_log\" variable\n",
+        "# Create log-transformed price (avoid log(0))\n",
+        "df[\"price_log\"] = np.log(df[\"Price\"].clip(lower=1))\n",
+        "\n",
+        "# Histogram\n",
+        "plt.figure(figsize=(8,5))\n",
+        "df[\"price_log\"].hist(bins=50, grid=False)\n",
+        "plt.title(\"Distribution of log(Price)\")\n",
+        "plt.xlabel(\"log(Price)\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()\n",
+        "\n",
+        "# KDE\n",
+        "plt.figure(figsize=(8,5))\n",
+        "sns.kdeplot(df[\"price_log\"].dropna(), fill=True)\n",
+        "plt.title(\"log(Price): Kernel Density Estimate\")\n",
+        "plt.xlabel(\"log(Price)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Boxplot\n",
+        "plt.figure(figsize=(8,2))\n",
+        "sns.boxplot(x=df[\"price_log\"].dropna())\n",
+        "plt.title(\"log(Price): Boxplot\")\n",
+        "plt.xlabel(\"log(Price)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Stats\n",
+        "print(\"Mean (log):   \", df[\"price_log\"].mean())\n",
+        "print(\"Median (log): \", df[\"price_log\"].median())\n",
+        "print(\"Mode (log):   \", list(df[\"price_log\"].mode()))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "oo2ahltpsaQ4",
+        "outputId": "f4dfb773-727b-4074-fb6e-5a9874cf302d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x200 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean (log):    4.860494597156509\n",
+            "Median (log):  4.8283137373023015\n",
+            "Mode (log):    [5.0106352940962555]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.4:\n",
+        "This appears much less skewed and the data looks more normal. With the boxplot, I will not that there are still plenty of outliers present. However, I will note that the data has been scaled more properly with the \"price_log\" variable."
+      ],
+      "metadata": {
+        "id": "gnwb32_zzEOl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 2.5: Scatterplot of \"price_log\" and \"Beds\"\n",
+        "# Scatterplot of log(Price) vs Beds\n",
+        "plt.figure(figsize=(8,5))\n",
+        "sns.scatterplot(x=\"Beds\", y=\"price_log\", data=df, alpha=0.4)\n",
+        "#Add labels and make it pretty\n",
+        "plt.title(\"Scatterplot of log(Price) vs Beds\")\n",
+        "plt.xlabel(\"Number of Beds\")\n",
+        "plt.ylabel(\"log(Price)\")\n",
+        "plt.show()\n",
+        "\n",
+        "# Groupby Beds summary of Price\n",
+        "beds_summary = df.groupby(\"Beds\")[\"Price\"].agg([\"count\",\"mean\",\"median\",\"std\"]).reset_index()\n",
+        "beds_summary.head(10)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 833
+        },
+        "id": "t1YVUy_1yEMl",
+        "outputId": "2dcb1536-abc9-42c2-bf3a-62c6c007eb5d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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+              "   Beds  count        mean  median         std\n",
+              "0   0.0      2   92.000000    92.0   38.183766\n",
+              "1   1.0  20344  127.673810   100.0  107.047827\n",
+              "2   2.0   6610  199.061271   160.0  225.706318\n",
+              "3   3.0   2071  268.118300   200.0  303.106942\n",
+              "4   4.0    783  315.332056   247.0  353.184402\n",
+              "5   5.0    284  411.500000   290.0  672.610659\n",
+              "6   6.0    177  401.768362   275.0  417.018431\n",
+              "7   7.0     45  341.377778   300.0  220.116631\n",
+              "8   8.0     24  589.041667   350.0  793.054934\n",
+              "9   9.0     15  618.000000   650.0  233.649616"
+            ],
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>Beds</th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
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+              "      <th>std</th>\n",
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+              "      <th>1</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>20344</td>\n",
+              "      <td>127.673810</td>\n",
+              "      <td>100.0</td>\n",
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+              "      <th>2</th>\n",
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+              "      <td>353.184402</td>\n",
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+              "      <td>411.500000</td>\n",
+              "      <td>290.0</td>\n",
+              "      <td>672.610659</td>\n",
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+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-d83a5d25-0040-48aa-818b-378204b396cb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-d83a5d25-0040-48aa-818b-378204b396cb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-e16b8e25-ace9-4c28-9a92-28120dda154c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e16b8e25-ace9-4c28-9a92-28120dda154c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-e16b8e25-ace9-4c28-9a92-28120dda154c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "beds_summary",
+              "summary": "{\n  \"name\": \"beds_summary\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"Beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.598136268408879,\n        \"min\": 0.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          9.0,\n          11.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5523,\n        \"min\": 2,\n        \"max\": 20344,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          15,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 332.12864063288714,\n        \"min\": 92.0,\n        \"max\": 1418.75,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          618.0,\n          535.8,\n          92.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 166.9804061677778,\n        \"min\": 92.0,\n        \"max\": 650.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          650.0,\n          359.0,\n          92.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 580.5735572259158,\n        \"min\": 38.18376618407357,\n        \"max\": 2388.2852111923316,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          233.64961557242685,\n          499.2175878311981,\n          38.18376618407357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.5:\n",
+        "\n",
+        "Based off the scatterplot, it seems as though verage price steadily increases with the number of beds. There is about about 128 dollars for 1-bed units, around 199 dollars for 2-beds, and climbing above 600 dollars for listings with 9 beds. This shows a clear upward trend in mean price as properties get larger. Standard deviation also increases with bed coun. This indicates that larger properties are not only more expensive on average but also far more variable in price, reflecting a mix ofsmaller multi-bedroom apartments and high-end luxury homes. In conclusion, more beds equals higher average prices and much greater price variability.\n"
+      ],
+      "metadata": {
+        "id": "uVdEwkzkOr1X"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.6\n",
+        "\n",
+        "#Name all of the variables\n",
+        "var1 = \"Beds\"          # x-axis\n",
+        "var2 = \"price_log\"     # y-axis\n",
+        "cat1 = \"Room Type\"     # categorical variable\n",
+        "cat2 = \"Property Type\" # categorical variable\n",
+        "\n",
+        "#This was borrowed from the notes directly!\n",
+        "this_plot = sns.scatterplot(\n",
+        "    data=df,\n",
+        "    x=var1,\n",
+        "    y=var2,\n",
+        "    hue=cat1,\n",
+        "    style=cat2,\n",
+        "    alpha=0.6\n",
+        ")\n",
+        "this_plot.set(title=\"log(Price) vs Beds by Room Type and Property Type\") #title\n",
+        "\n",
+        "# Move legend so that we can see the graph\n",
+        "sns.move_legend(this_plot, \"upper right\", bbox_to_anchor=(1.5, 1))\n",
+        "plt.show()\n",
+        "\n",
+        "#Grouped Summary\n",
+        "rp_summary = (\n",
+        "    df.groupby([\"Room Type\",\"Property Type\"])[\"Price\"]\n",
+        "      .agg([\"count\",\"mean\",\"median\",\"std\"])\n",
+        "      .reset_index()\n",
+        "      .sort_values(\"mean\", ascending=False)\n",
+        ")\n",
+        "rp_summary.head(10)  # top 10 by mean"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 919
+        },
+        "id": "Fwklql5JOqhC",
+        "outputId": "a0829127-b234-45de-abba-356386582d74"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          Room Type    Property Type  count        mean  median          std\n",
+              "11  Entire home/apt            Other     14  843.428571   300.0  1133.062271\n",
+              "13  Entire home/apt            Villa      4  529.750000   249.5   650.963581\n",
+              "10  Entire home/apt             Loft    392  330.510204   225.0   321.519721\n",
+              "6   Entire home/apt      Condominium     72  304.861111   200.0   266.197497\n",
+              "8   Entire home/apt            House    752  297.263298   195.0   468.409428\n",
+              "12  Entire home/apt        Townhouse     83  280.783133   190.0   314.605252\n",
+              "4   Entire home/apt            Cabin      1  250.000000   250.0          NaN\n",
+              "0   Entire home/apt        Apartment  15669  213.224839   175.0   218.097834\n",
+              "26     Private room            Other     29  211.931034   119.0   235.404505\n",
+              "1   Entire home/apt  Bed & Breakfast     13  184.538462   130.0   119.814172"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ed9b1f10-7069-4267-b5a7-1b7222f3d1f6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Room Type</th>\n",
+              "      <th>Property Type</th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>median</th>\n",
+              "      <th>std</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Other</td>\n",
+              "      <td>14</td>\n",
+              "      <td>843.428571</td>\n",
+              "      <td>300.0</td>\n",
+              "      <td>1133.062271</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Villa</td>\n",
+              "      <td>4</td>\n",
+              "      <td>529.750000</td>\n",
+              "      <td>249.5</td>\n",
+              "      <td>650.963581</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Loft</td>\n",
+              "      <td>392</td>\n",
+              "      <td>330.510204</td>\n",
+              "      <td>225.0</td>\n",
+              "      <td>321.519721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Condominium</td>\n",
+              "      <td>72</td>\n",
+              "      <td>304.861111</td>\n",
+              "      <td>200.0</td>\n",
+              "      <td>266.197497</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>House</td>\n",
+              "      <td>752</td>\n",
+              "      <td>297.263298</td>\n",
+              "      <td>195.0</td>\n",
+              "      <td>468.409428</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Townhouse</td>\n",
+              "      <td>83</td>\n",
+              "      <td>280.783133</td>\n",
+              "      <td>190.0</td>\n",
+              "      <td>314.605252</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Cabin</td>\n",
+              "      <td>1</td>\n",
+              "      <td>250.000000</td>\n",
+              "      <td>250.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>15669</td>\n",
+              "      <td>213.224839</td>\n",
+              "      <td>175.0</td>\n",
+              "      <td>218.097834</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>Private room</td>\n",
+              "      <td>Other</td>\n",
+              "      <td>29</td>\n",
+              "      <td>211.931034</td>\n",
+              "      <td>119.0</td>\n",
+              "      <td>235.404505</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>Bed &amp; Breakfast</td>\n",
+              "      <td>13</td>\n",
+              "      <td>184.538462</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>119.814172</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed9b1f10-7069-4267-b5a7-1b7222f3d1f6')\"\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      height: 32px;\n",
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+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "        document.querySelector('#df-ed9b1f10-7069-4267-b5a7-1b7222f3d1f6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ed9b1f10-7069-4267-b5a7-1b7222f3d1f6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
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+              "    <div id=\"df-8d906bc2-df18-4a6f-aaa0-b983012fd8c1\">\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8d906bc2-df18-4a6f-aaa0-b983012fd8c1 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "rp_summary",
+              "summary": "{\n  \"name\": \"rp_summary\",\n  \"rows\": 39,\n  \"fields\": [\n    {\n      \"column\": \"Room Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Entire home/apt\",\n          \"Private room\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Other\",\n          \"Townhouse\",\n          \"Bungalow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2991,\n        \"min\": 1,\n        \"max\": 15669,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          29,\n          155,\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 148.3760581448684,\n        \"min\": 39.0,\n        \"max\": 843.4285714285714,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          39.0,\n          150.0,\n          83.36538461538461\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.18787081984111,\n        \"min\": 33.5,\n        \"max\": 300.0,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          107.5,\n          85.0,\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230.50314508090287,\n        \"min\": 24.060687161148714,\n        \"max\": 1133.062270967536,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          35.35533905932738,\n          71.10731326663947,\n          67.72084945046392\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.6:\n",
+        "\n",
+        "The scatterplot shows that entire homes and apartments are generally priced higher than private or shared rooms. As the number of beds increases, prices also rise, though there is wide variation at each bed count. Certain property types such as Villas, Lofts, and Houses tend to cluster toward the higher end of the price range. The grouped summary reveals that “Other” entire-home listings have the highest mean price at around 843 dollars, followed by Villas at about 530 dollars. Because these averages are pulled upward by luxury outliers, the median provides a more reliable measure of central tendency for this dataset."
+      ],
+      "metadata": {
+        "id": "h8AtzZA_Sowk"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yLlu_O9hjrh4"
+      },
+      "source": [
+        "**Q3.** This question looks at a time series of the number of active oil drilling rigs in the United States over time. The data comes from the Energy Information Agency.\n",
+        "\n",
+        "1. Load `./data/drilling_rigs.csv` and examine the data. How many observations? How many variables? Are numeric variables correctly read in by Pandas, or will some variables have to be typecast/coerced? Explain clearly how these data need to be cleaned.\n",
+        "2. To convert the `Month` variable to an ordered datetime variable, use `df['time'] = pd.to_datetime(df['Month'], format='mixed')`.\n",
+        "3. Let's look at `Active Well Service Rig Count (Number of Rigs)`, which is the total number of rigs over time. Make a line plot of this time series. Describe what you see.\n",
+        "4. Instead of levels, we want to look at change over time. Compute the first difference of  `Active Well Service Rig Count (Number of Rigs)` and plot it over time. Describe what you see.\n",
+        "5. The first two columns are the number of onshore and offshore rigs, respectively. Melt these columns and plot the resulting series."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "id": "53filN4Vjrh4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "197c1e56-c19c-4140-cd3e-08191ffb63f8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape (rows, cols): (623, 10)\n",
+            "\n",
+            "Column names:\n",
+            " ['Month', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Onshore (Number of Rigs)', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Offshore (Number of Rigs)', 'Crude Oil Rotary Rigs in Operation, Total (Number of Rigs)', 'Natural Gas Rotary Rigs in Operation, Total (Number of Rigs)', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Horizontal Trajectory (Number of Rigs)', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Directional Trajectory (Number of Rigs)', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Vertical Trajectory (Number of Rigs)', 'Crude Oil and Natural Gas Rotary Rigs in Operation, Total (Number of Rigs)', 'Active Well Service Rig Count (Number of Rigs)']\n",
+            "Observations (rows): 623\n",
+            "Variables (columns): 10\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 623 entries, 0 to 622\n",
+            "Data columns (total 10 columns):\n",
+            " #   Column                                                                                       Non-Null Count  Dtype \n",
+            "---  ------                                                                                       --------------  ----- \n",
+            " 0   Month                                                                                        623 non-null    object\n",
+            " 1   Crude Oil and Natural Gas Rotary Rigs in Operation, Onshore (Number of Rigs)                 623 non-null    int64 \n",
+            " 2   Crude Oil and Natural Gas Rotary Rigs in Operation, Offshore (Number of Rigs)                623 non-null    int64 \n",
+            " 3   Crude Oil Rotary Rigs in Operation, Total (Number of Rigs)                                   623 non-null    object\n",
+            " 4   Natural Gas Rotary Rigs in Operation, Total (Number of Rigs)                                 623 non-null    object\n",
+            " 5   Crude Oil and Natural Gas Rotary Rigs in Operation, Horizontal Trajectory (Number of Rigs)   623 non-null    object\n",
+            " 6   Crude Oil and Natural Gas Rotary Rigs in Operation, Directional Trajectory (Number of Rigs)  623 non-null    object\n",
+            " 7   Crude Oil and Natural Gas Rotary Rigs in Operation, Vertical Trajectory (Number of Rigs)     623 non-null    object\n",
+            " 8   Crude Oil and Natural Gas Rotary Rigs in Operation, Total (Number of Rigs)                   623 non-null    int64 \n",
+            " 9   Active Well Service Rig Count (Number of Rigs)                                               623 non-null    object\n",
+            "dtypes: int64(3), object(7)\n",
+            "memory usage: 48.8+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Question 3.1\n",
+        "#Load the csv for drilling data\n",
+        "df_1 = pd.read_csv(\"./eda_assignment/data/drilling_rigs.csv\")\n",
+        "\n",
+        "#Observations\n",
+        "print(\"Shape (rows, cols):\", df_1.shape)\n",
+        "print(\"\\nColumn names:\\n\", df_1.columns.tolist())\n",
+        "\n",
+        "print(\"Observations (rows):\", df_1.shape[0])\n",
+        "print(\"Variables (columns):\", df_1.shape[1])\n",
+        "\n",
+        "df_1.head()\n",
+        "\n",
+        "#I want to use this \"info\" function to determine the type of variables I am working with\n",
+        "df_1.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 3.1:\n",
+        "\n",
+        "The drilling_rigs dataset has 623 observations and 10 variables. Three columns (Onshore, Offshore, and Total rigs) were already stored as numeric, but several variables that should be numeric were imported as objects. These include the crude oil total, natural gas total, horizontal, directional, vertical trajectory counts, and the active well service rig count. Cleaning will therefore require typecasting these object columns into numeric types."
+      ],
+      "metadata": {
+        "id": "Gk0iZd4eVap2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 3.2\n",
+        "\n",
+        "df_1[\"time\"] = pd.to_datetime(df_1[\"Month\"], format=\"mixed\", errors=\"coerce\")\n",
+        "\n",
+        "#Check the data\n",
+        "df_1[[\"Month\",\"time\"]].head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "e51k6kdUUwGN",
+        "outputId": "a8f72884-8139-4d20-86c0-aa227f68cdeb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           Month       time\n",
+              "0   1973 January 1973-01-01\n",
+              "1  1973 February 1973-02-01\n",
+              "2     1973 March 1973-03-01\n",
+              "3     1973 April 1973-04-01\n",
+              "4       1973 May 1973-05-01"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9d7094e5-8d9c-4adb-b983-34d8505f997d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Month</th>\n",
+              "      <th>time</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1973 January</td>\n",
+              "      <td>1973-01-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1973 February</td>\n",
+              "      <td>1973-02-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1973 March</td>\n",
+              "      <td>1973-03-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1973 April</td>\n",
+              "      <td>1973-04-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1973 May</td>\n",
+              "      <td>1973-05-01</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9d7094e5-8d9c-4adb-b983-34d8505f997d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9d7094e5-8d9c-4adb-b983-34d8505f997d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9d7094e5-8d9c-4adb-b983-34d8505f997d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-206be74a-8a2c-4942-a037-d4e8d5e1251d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-206be74a-8a2c-4942-a037-d4e8d5e1251d')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-206be74a-8a2c-4942-a037-d4e8d5e1251d button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df_1[[\\\"Month\\\",\\\"time\\\"]]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1973 February\",\n          \"1973 May\",\n          \"1973 March\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1973-01-01 00:00:00\",\n        \"max\": \"1973-05-01 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"1973-02-01 00:00:00\",\n          \"1973-05-01 00:00:00\",\n          \"1973-03-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 3.3\n",
+        "\n",
+        "# Convert rig count column to numeric (it was being really weird when I plotted as was so I thought maybe we should convert numeric)\n",
+        "df_1[\"Active Well Service Rig Count (Number of Rigs)\"] = pd.to_numeric(\n",
+        "    df_1[\"Active Well Service Rig Count (Number of Rigs)\"], errors=\"coerce\"\n",
+        ")\n",
+        "\n",
+        "# Sort by time\n",
+        "df_1 = df_1.sort_values(\"time\")\n",
+        "\n",
+        "# Line plot\n",
+        "plt.figure(figsize=(12,6))\n",
+        "sns.lineplot(\n",
+        "    data=df_1,\n",
+        "    x=\"time\",\n",
+        "    y=\"Active Well Service Rig Count (Number of Rigs)\"\n",
+        ")\n",
+        "\n",
+        "#Make it pretty!\n",
+        "plt.title(\"Active Well Service Rig Count Over Time\")\n",
+        "plt.xlabel(\"Time\")\n",
+        "plt.ylabel(\"Number of Rigs\")\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "ZP4G9yj0W443",
+        "outputId": "5c70dd93-e44a-49a0-8996-91dc74867f59"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 3.3\n",
+        "\n",
+        "The line plot of the Active Well Service Rig Count shows a cyclical pattern over time, with periods of growth followed by sharp declines. Towards 2020 the number of rigs shows a complete steep decline which is different from the 1980s and 1990s. Without assuming the cause, off the top of my head, this could potentially be due the COVID-19 pandemic that happened in 2020, the very steep drop."
+      ],
+      "metadata": {
+        "id": "Sno8XjzvYQ5T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 3.4\n",
+        "\n",
+        "# Compute first difference\n",
+        "df_1[\"rig_diff\"] = df_1[\"Active Well Service Rig Count (Number of Rigs)\"].diff()\n",
+        "\n",
+        "# Plot\n",
+        "plt.figure(figsize=(12,6))\n",
+        "sns.lineplot(data=df_1, x=\"time\", y=\"rig_diff\")\n",
+        "#Make it pretty!\n",
+        "plt.title(\"First Difference of Active Well Service Rig Count Over Time\")\n",
+        "plt.xlabel(\"Time\")\n",
+        "plt.ylabel(\"Change in Number of Rigs\")\n",
+        "plt.axhline(0, color=\"red\", linestyle=\"--\")  #I made the reference line at 0\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "Y6aTECtdYQfR",
+        "outputId": "f7d6e10a-217a-4017-a43d-09b0031a9d56"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 3.4:\n",
+        "\n",
+        "This graph varies for the lineplot we had for Question 3.3. It seems to oscillate showing how the number of rigs changes from month to month. Most changes are relatively small, but there are sharp negative spikes during downturns, which could correspond to rapid pullbacks in drilling activity."
+      ],
+      "metadata": {
+        "id": "sS7a5qDjZNnB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Question 3.5\n",
+        "\n",
+        "# Melt the onshore and offshore columns\n",
+        "df_melted = df_1.melt(\n",
+        "    id_vars=\"time\",\n",
+        "    value_vars=[\n",
+        "        \"Crude Oil and Natural Gas Rotary Rigs in Operation, Onshore (Number of Rigs)\",\n",
+        "        \"Crude Oil and Natural Gas Rotary Rigs in Operation, Offshore (Number of Rigs)\"\n",
+        "    ],\n",
+        "    var_name=\"Rig Type\",\n",
+        "    value_name=\"Rig Count\"\n",
+        ")\n",
+        "\n",
+        "# Line plot of the melted data to visualize it all together, make it look pretty!!\n",
+        "plt.figure(figsize=(12,6))\n",
+        "sns.lineplot(data=df_melted, x=\"time\", y=\"Rig Count\", hue=\"Rig Type\")\n",
+        "plt.title(\"Onshore vs Offshore Rig Counts Over Time\")\n",
+        "plt.xlabel(\"Date\")\n",
+        "plt.ylabel(\"Number of Rigs\")\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 564
+        },
+        "id": "8wfwfmh5XKEV",
+        "outputId": "4682113f-07ba-405a-e07d-3e898cbd1e4c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "hZn5pdOoaKlC"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/EDA_hwQ1 b/EDA_hwQ1
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/EDA_hwQ1
@@ -0,0 +1 @@
+
diff --git a/Note Sep 5, 2025.pdf b/Note Sep 5, 2025.pdf
new file mode 100644
index 0000000..72a4f71
Binary files /dev/null and b/Note Sep 5, 2025.pdf differ
diff --git a/Visualization_assignment.ipynb b/Visualization_assignment.ipynb
new file mode 100644
index 0000000..677c132
--- /dev/null
+++ b/Visualization_assignment.ipynb
@@ -0,0 +1,2431 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/Visualization_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "13ad028b-72b7-43ed-aa78-96fd4e518040",
+      "metadata": {
+        "id": "13ad028b-72b7-43ed-aa78-96fd4e518040"
+      },
+      "source": [
+        "# Assignment: Visualization\n",
+        "### `! git clone https://github.com/ds3001f25/visualization_assignment.git`\n",
+        "### Do Q1 and Q2."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ea89b847",
+      "metadata": {
+        "id": "ea89b847"
+      },
+      "source": [
+        "**Q1.** Write your own function to make a kernel density plot.\n",
+        "\n",
+        "- The user should pass in a Pandas series or Numpy array.\n",
+        "- The default kernel should be Gaussian, but include the uniform/bump and Epanechnikov as alternatives.\n",
+        "- The default bandwidth should be the Silverman plug-in, but allow the user to specify an alternative.\n",
+        "- You can use Matplotlib or Seaborn's `.lineplot`, but not an existing function that creates kernel density plots.\n",
+        "\n",
+        "You will have to make a lot of choices and experiment with getting errors. Embrace the challenge and track your choices in the comments in your code.\n",
+        "\n",
+        "Use the pretrail data set from class to show that your function works, and compare it with the Seaborn `kdeplot`.\n",
+        "\n",
+        "We covered the Gaussian,\n",
+        "$$\n",
+        "k(z) = \\dfrac{1}{\\sqrt{2\\pi}}e^{-z^2/2}\n",
+        "$$\n",
+        "and uniform\n",
+        "$$\n",
+        "k(z) = \\begin{cases}\n",
+        "\\frac{1}{2}, & |z| \\le 1 \\\\\n",
+        "0, & |z|>1\n",
+        "\\end{cases}\n",
+        "$$\n",
+        "kernels in class, but the Epanechnikov kernel is\n",
+        "$$\n",
+        "k(z) = \\begin{cases}\n",
+        "\\frac{3}{4} (1-z^2), & |z| \\le 1 \\\\\n",
+        "0, & |z|>1.\n",
+        "\\end{cases}\n",
+        "$$\n",
+        "\n",
+        "In order to make your code run reasonably quickly, consider using the `pdist` or `cdist` functions from SciPy to make distance calculations for arrays of points. The other leading alternative is to thoughtfully use NumPy's broadcasting features. Writing `for` loops will be slow, but that's fine."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "3a40fb07",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3a40fb07",
+        "outputId": "5367176c-c9dd-4878-f60c-461b57b05aba"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'visualization_assignment'...\n",
+            "remote: Enumerating objects: 10, done.\u001b[K\n",
+            "remote: Counting objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Total 10 (delta 0), reused 0 (delta 0), pack-reused 8 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (10/10), 1.88 MiB | 7.01 MiB/s, done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "! git clone https://github.com/ds3001f25/visualization_assignment.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#import the required packages + the csv\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#read the csv\n",
+        "from google.colab import files\n",
+        "\n",
+        "uploaded = files.upload() #Choose the pretrial_data.csv file from the assignment and upload it!"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "4nB4DT5d8iD1",
+        "outputId": "304ff069-64e6-49b4-d16a-110262c9bbea"
+      },
+      "id": "4nB4DT5d8iD1",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-a915e398-19a5-468e-9fec-2514fb845f23\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-a915e398-19a5-468e-9fec-2514fb845f23\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving pretrial_data.csv to pretrial_data.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This is the bandwith function - Silverman's Rule\n",
+        "\n",
+        "def silverman_bandwidth(x: np.ndarray) -> float:\n",
+        "    x = np.asarray(x).ravel()\n",
+        "    n = x.size #number of data points\n",
+        "    sd = np.std(x, ddof=1) #sample std. deviation\n",
+        "    iqr = np.subtract(*np.percentile(x, [75, 25])) #IQR\n",
+        "    sigma = min(sd, iqr / 1.34) if (sd > 0 or iqr > 0) else 0.0 #Use 1.34 as a value to estimate spread\n",
+        "\n",
+        "   #If data has no spread (constant values), return a small fallback bandwidth\n",
+        "    if sigma == 0.0:\n",
+        "        span = max(1.0, np.abs(x).max())\n",
+        "        return 1e-3 * span\n",
+        "\n",
+        "    return 0.9 * sigma * n ** (-1/5) #This is Silverman's rule"
+      ],
+      "metadata": {
+        "id": "xrugwk0M-7gc"
+      },
+      "id": "xrugwk0M-7gc",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Define the kernels\n",
+        "#Standard gaussian kernel\n",
+        "def _kernel_gaussian(z):\n",
+        "    return (1 / np.sqrt(2 * np.pi)) * np.exp(-0.5 * z**2) #from formulas we went over in lecture\n",
+        "#Boxcar kernel\n",
+        "def _kernel_uniform(z):\n",
+        "    out = np.zeros_like(z)\n",
+        "    out[np.abs(z) <= 1] = 0.5\n",
+        "    return out\n",
+        "#Epanechnikov kernel\n",
+        "def _kernel_epanechnikov(z):\n",
+        "    out = np.zeros_like(z)\n",
+        "    mask = (np.abs(z) <= 1)\n",
+        "    out[mask] = 0.75 * (1 - z[mask]**2)\n",
+        "    return out\n",
+        "#I put all of the kernels into a dictionary just to keep track of everything more easily (I ran into a lot of errors with this part specifically if I wasn't keeping track of functions)\n",
+        "_KERNELS = {\n",
+        "    \"gaussian\": _kernel_gaussian,\n",
+        "    \"uniform\": _kernel_uniform,\n",
+        "    \"epanechnikov\": _kernel_epanechnikov\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "OzMFMpkk_qMo"
+      },
+      "id": "OzMFMpkk_qMo",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#KDE plot\n",
+        "def kde1d(data, bandwidth=None, kernel=\"gaussian\", grid=None, grid_size=512, cut=3.0):\n",
+        "    x = np.asarray(data).ravel()\n",
+        "    n = len(x)\n",
+        "\n",
+        "    #This is the bandwith function\n",
+        "    h = bandwidth if bandwidth else silverman_bandwidth(x)\n",
+        "\n",
+        "    #Kernel function with the kernel dictionary\n",
+        "    kfun = _KERNELS[kernel]\n",
+        "\n",
+        "    #Grid (I had to research this part online because I kept getting errors)\n",
+        "    if grid is None:\n",
+        "        xmin, xmax = x.min() - cut*h, x.max() + cut*h\n",
+        "        xs = np.linspace(xmin, xmax, grid_size)\n",
+        "    else:\n",
+        "        xs = np.asarray(grid)\n",
+        "\n",
+        "    #Compute density here\n",
+        "    Z = (xs[:, None] - x[None, :]) / h\n",
+        "    K = kfun(Z) #apply kernel function\n",
+        "    fhat = K.sum(axis=1) / (n * h) #Density estimate\n",
+        "\n",
+        "    return xs, fhat"
+      ],
+      "metadata": {
+        "id": "N7o6RIYMAM3u"
+      },
+      "id": "N7o6RIYMAM3u",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#KDE Plot\n",
+        "def plot_kde(data, bandwidth=None, kernel=\"gaussian\", ax=None, label=None):\n",
+        "    xs, fhat = kde1d(data, bandwidth=bandwidth, kernel=kernel) #compute the kde estimate\n",
+        "\n",
+        "#Create the plot axis here\n",
+        "    if ax is None:\n",
+        "        fig, ax = plt.subplots(figsize=(8,4))\n",
+        "    ax.plot(xs, fhat, label=label or f\"{kernel} kernel\") #plot density curve\n",
+        "    ax.set_xlabel(\"x\") #add labels + legend + make it pretty!\n",
+        "    ax.set_ylabel(\"Density\")\n",
+        "    ax.legend()\n",
+        "\n",
+        "    return xs, fhat, ax"
+      ],
+      "metadata": {
+        "id": "E5eIJs8dA4vq"
+      },
+      "id": "E5eIJs8dA4vq",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#I loaded the data here again because even though the \"pre-trial csv\" was loaded I was having problems at this point\n",
+        "from google.colab import files\n",
+        "import pandas as pd\n",
+        "\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "df = pd.read_csv(\"pretrial_data.csv\")\n",
+        "\n",
+        "#Check the columns\n",
+        "print(df.head())\n",
+        "print(df.columns)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "EfydsTt1CnXP",
+        "outputId": "d065a553-405b-48a9-be64-711a157ab49b"
+      },
+      "id": "EfydsTt1CnXP",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-55af1a27-55f0-40f4-803a-1c47ef23bdf6\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-55af1a27-55f0-40f4-803a-1c47ef23bdf6\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving pretrial_data.csv to pretrial_data (2).csv\n",
+            "   Unnamed: 0 case_type   age sex race  is_poor  bond  bond_type  prior_F  \\\n",
+            "0           0         F  31.0   M    W      NaN   NaN          7      0.0   \n",
+            "1           1         F  60.0   M    B      NaN   NaN          7     13.0   \n",
+            "2           2         M  27.0   M    W      NaN   NaN          7      0.0   \n",
+            "3           3         M  27.0   M    B      0.0   NaN          7      0.0   \n",
+            "4           4         F  28.0   F    W      0.0   NaN          7      0.0   \n",
+            "\n",
+            "   prior_M  gini  released  sentence_type   sentence  bond_NA  held_wo_bail  \\\n",
+            "0      0.0  0.44       NaN            NaN        NaN     True         False   \n",
+            "1     21.0  0.44       0.0            0.0  60.000000     True         False   \n",
+            "2      0.0  0.44       0.0            1.0  12.000000     True         False   \n",
+            "3      9.0  0.44       0.0            1.0   0.985626     True         False   \n",
+            "4      0.0  0.44       1.0            4.0   0.000000     True         False   \n",
+            "\n",
+            "   sentence_NA  \n",
+            "0         True  \n",
+            "1        False  \n",
+            "2        False  \n",
+            "3        False  \n",
+            "4         True  \n",
+            "Index(['Unnamed: 0', 'case_type', 'age', 'sex', 'race', 'is_poor', 'bond',\n",
+            "       'bond_type', 'prior_F', 'prior_M', 'gini', 'released', 'sentence_type',\n",
+            "       'sentence', 'bond_NA', 'held_wo_bail', 'sentence_NA'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#I ran the data on \"age\" because it was the easiest variable but it can be any numeric column (data) in the set!\n",
+        "col = \"age\"\n",
+        "series = df[col].dropna() #drop the missing values, I did this just in case!\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8,4))\n",
+        "\n",
+        "#Plot the KDE with the different kernels\n",
+        "plot_kde(series, kernel=\"gaussian\", ax=ax, label=\"Ours: Gaussian\")\n",
+        "plot_kde(series, kernel=\"epanechnikov\", ax=ax, label=\"Ours: Epanechnikov\")\n",
+        "plot_kde(series, kernel=\"uniform\", ax=ax, label=\"Ours: Uniform\")\n",
+        "\n",
+        "#Title + print the plot\n",
+        "plt.title(f\"Pretrial KDE ({col})\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "wFQJdbsRBXmu",
+        "outputId": "0a075bf5-daaf-43dd-f40f-f8fa1e798597"
+      },
+      "id": "wFQJdbsRBXmu",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Comparing my plot with the seaborn KDE plot\n",
+        "#Import the seaborn package\n",
+        "import seaborn as sns\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(8,4))\n",
+        "\n",
+        "#This is my version from code\n",
+        "xs, fhat = kde1d(series, kernel=\"gaussian\")\n",
+        "ax.plot(xs, fhat, label=\"Ours (Gaussian, Silverman)\")\n",
+        "\n",
+        "#This is the seaborn KDE plot\n",
+        "sns.kdeplot(x=series, ax=ax, label=\"Seaborn kdeplot\", lw=1.5)\n",
+        "\n",
+        "#Make it pretty + print!\n",
+        "ax.set_title(f\"Pretrial KDE Comparison ({col})\")\n",
+        "ax.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 410
+        },
+        "id": "7vj24sZxDE8D",
+        "outputId": "b17718d9-40cc-4b93-88ff-7eb6ce57daef"
+      },
+      "id": "7vj24sZxDE8D",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "55e54a37-63fb-4df9-af65-81ee53ae00fe",
+      "metadata": {
+        "id": "55e54a37-63fb-4df9-af65-81ee53ae00fe"
+      },
+      "source": [
+        "**Q2.** This question provides some practice doing exploratory data analysis and visualization.\n",
+        "\n",
+        "The \"relevant\" variables for this question are:\n",
+        "  - `level` - Level of institution (4-year, 2-year)\n",
+        "  - `aid_value` - The average amount of student aid going to undergraduate recipients\n",
+        "  - `control` - Public, Private not-for-profit, Private for-profit\n",
+        "  - `grad_100_value` - percentage of first-time, full-time, degree-seeking undergraduates who complete a degree or certificate program within 100 percent of expected time (bachelor's-seeking group at 4-year institutions)\n",
+        "\n",
+        "1. Load the `./data/college_completion.csv` data with Pandas.\n",
+        "2. What are are the dimensions of the data? How many observations are there? What are the variables included? Use `.head()` to examine the first few rows of data.\n",
+        "3. Cross tabulate `control` and `level`. Describe the patterns you see.\n",
+        "4. For `grad_100_value`, create a histogram, kernel density plot, boxplot, and statistical description.\n",
+        "5. For `grad_100_value`, create a grouped kernel density plot by `control` and by `level`. Describe what you see. Use `groupby` and `.describe` to make grouped calculations of statistical descriptions of `grad_100_value` by `level` and `control`. Which institutions appear to have the best graduation rates?\n",
+        "6. Create a new variable, `df['levelXcontrol']=df['level']+', '+df['control']` that interacts level and control. Make a grouped kernel density plot. Which institutions appear to have the best graduation rates?\n",
+        "7. Make a kernel density plot of `aid_value`. Now group your graph by `level` and `control`. What explains the shape of the graph? Use `groupby` and `.describe` to make grouped calculations of statistical descriptions of `aid_value` by `level` and `control`.\n",
+        "8. Make a scatterplot of `grad_100_value` by `aid_value`. Describe what you see. Now make the same plot, grouping by `level` and then `control`. Describe what you see. For which kinds of institutions does aid seem to increase graduation rates?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "e92b5b79",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "e92b5b79",
+        "outputId": "60660cc0-5fb6-4914-af92-d28f276cdc05"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-d0a607d3-ea53-460f-9621-7da1e56acde1\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-d0a607d3-ea53-460f-9621-7da1e56acde1\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving college_completion.csv to college_completion.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 2.1: Load the Data\n",
+        "#Import neccesary packages (I know I already did this for the first question so I should technically be fine, however, I just wanted to make sure that I was good)\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "from google.colab import files\n",
+        "\n",
+        "uploaded = files.upload() #Choose the \"college_completion.csv\" file and upload it!\n",
+        "\n",
+        "df1= pd.read_csv(\"college_completion.csv\") #I did not want to overwrite the previous data frame"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 2.2: Data Dimensions\n",
+        "#print all of this\n",
+        "print(\"Shape:\", df1.shape)   #(rows, columns)\n",
+        "print(\"Columns:\", df1.columns.tolist())\n",
+        "print(\"Observations:\", len(df1))\n",
+        "\n",
+        "#Look at the first couple of rows to make sure everything is good\n",
+        "df1.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 585
+        },
+        "id": "jj7CD0LxFPlK",
+        "outputId": "270a2318-a918-48d4-9fa1-30bb54e74288"
+      },
+      "id": "jj7CD0LxFPlK",
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape: (3798, 63)\n",
+            "Columns: ['index', 'unitid', 'chronname', 'city', 'state', 'level', 'control', 'basic', 'hbcu', 'flagship', 'long_x', 'lat_y', 'site', 'student_count', 'awards_per_value', 'awards_per_state_value', 'awards_per_natl_value', 'exp_award_value', 'exp_award_state_value', 'exp_award_natl_value', 'exp_award_percentile', 'ft_pct', 'fte_value', 'fte_percentile', 'med_sat_value', 'med_sat_percentile', 'aid_value', 'aid_percentile', 'endow_value', 'endow_percentile', 'grad_100_value', 'grad_100_percentile', 'grad_150_value', 'grad_150_percentile', 'pell_value', 'pell_percentile', 'retain_value', 'retain_percentile', 'ft_fac_value', 'ft_fac_percentile', 'vsa_year', 'vsa_grad_after4_first', 'vsa_grad_elsewhere_after4_first', 'vsa_enroll_after4_first', 'vsa_enroll_elsewhere_after4_first', 'vsa_grad_after6_first', 'vsa_grad_elsewhere_after6_first', 'vsa_enroll_after6_first', 'vsa_enroll_elsewhere_after6_first', 'vsa_grad_after4_transfer', 'vsa_grad_elsewhere_after4_transfer', 'vsa_enroll_after4_transfer', 'vsa_enroll_elsewhere_after4_transfer', 'vsa_grad_after6_transfer', 'vsa_grad_elsewhere_after6_transfer', 'vsa_enroll_after6_transfer', 'vsa_enroll_elsewhere_after6_transfer', 'similar', 'state_sector_ct', 'carnegie_ct', 'counted_pct', 'nicknames', 'cohort_size']\n",
+            "Observations: 3798\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   index  unitid                            chronname        city    state  \\\n",
+              "0      0  100654               Alabama A&M University      Normal  Alabama   \n",
+              "1      1  100663  University of Alabama at Birmingham  Birmingham  Alabama   \n",
+              "2      2  100690                   Amridge University  Montgomery  Alabama   \n",
+              "3      3  100706  University of Alabama at Huntsville  Huntsville  Alabama   \n",
+              "4      4  100724             Alabama State University  Montgomery  Alabama   \n",
+              "\n",
+              "    level                 control  \\\n",
+              "0  4-year                  Public   \n",
+              "1  4-year                  Public   \n",
+              "2  4-year  Private not-for-profit   \n",
+              "3  4-year                  Public   \n",
+              "4  4-year                  Public   \n",
+              "\n",
+              "                                               basic hbcu flagship  ...  \\\n",
+              "0  Masters Colleges and Universities--larger prog...    X      NaN  ...   \n",
+              "1  Research Universities--very high research acti...  NaN      NaN  ...   \n",
+              "2            Baccalaureate Colleges--Arts & Sciences  NaN      NaN  ...   \n",
+              "3  Research Universities--very high research acti...  NaN      NaN  ...   \n",
+              "4  Masters Colleges and Universities--larger prog...    X      NaN  ...   \n",
+              "\n",
+              "   vsa_grad_after6_transfer  vsa_grad_elsewhere_after6_transfer  \\\n",
+              "0                      36.4                                 5.6   \n",
+              "1                       NaN                                 NaN   \n",
+              "2                       NaN                                 NaN   \n",
+              "3                       0.0                                 0.0   \n",
+              "4                       NaN                                 NaN   \n",
+              "\n",
+              "  vsa_enroll_after6_transfer  vsa_enroll_elsewhere_after6_transfer  \\\n",
+              "0                       17.2                                  11.1   \n",
+              "1                        NaN                                   NaN   \n",
+              "2                        NaN                                   NaN   \n",
+              "3                        0.0                                   0.0   \n",
+              "4                        NaN                                   NaN   \n",
+              "\n",
+              "                                             similar  state_sector_ct  \\\n",
+              "0  232937|100724|405997|113607|139533|144005|2285...               13   \n",
+              "1  196060|180461|201885|145600|209542|236939|1268...               13   \n",
+              "2  217925|441511|205124|247825|197647|221856|1353...               16   \n",
+              "3  232186|133881|196103|196413|207388|171128|1900...               13   \n",
+              "4  100654|232937|242617|243197|144005|241739|2354...               13   \n",
+              "\n",
+              "   carnegie_ct  counted_pct  nicknames  cohort_size  \n",
+              "0          386      99.7|07        NaN        882.0  \n",
+              "1          106      56.0|07        UAB       1376.0  \n",
+              "2          252     100.0|07        NaN          3.0  \n",
+              "3          106      43.1|07        UAH        759.0  \n",
+              "4          386      88.0|07        ASU       1351.0  \n",
+              "\n",
+              "[5 rows x 63 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-086e0890-f49b-44e7-9d2f-339b7816cb56\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>index</th>\n",
+              "      <th>unitid</th>\n",
+              "      <th>chronname</th>\n",
+              "      <th>city</th>\n",
+              "      <th>state</th>\n",
+              "      <th>level</th>\n",
+              "      <th>control</th>\n",
+              "      <th>basic</th>\n",
+              "      <th>hbcu</th>\n",
+              "      <th>flagship</th>\n",
+              "      <th>...</th>\n",
+              "      <th>vsa_grad_after6_transfer</th>\n",
+              "      <th>vsa_grad_elsewhere_after6_transfer</th>\n",
+              "      <th>vsa_enroll_after6_transfer</th>\n",
+              "      <th>vsa_enroll_elsewhere_after6_transfer</th>\n",
+              "      <th>similar</th>\n",
+              "      <th>state_sector_ct</th>\n",
+              "      <th>carnegie_ct</th>\n",
+              "      <th>counted_pct</th>\n",
+              "      <th>nicknames</th>\n",
+              "      <th>cohort_size</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>100654</td>\n",
+              "      <td>Alabama A&amp;M University</td>\n",
+              "      <td>Normal</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>4-year</td>\n",
+              "      <td>Public</td>\n",
+              "      <td>Masters Colleges and Universities--larger prog...</td>\n",
+              "      <td>X</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>36.4</td>\n",
+              "      <td>5.6</td>\n",
+              "      <td>17.2</td>\n",
+              "      <td>11.1</td>\n",
+              "      <td>232937|100724|405997|113607|139533|144005|2285...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>386</td>\n",
+              "      <td>99.7|07</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>882.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>100663</td>\n",
+              "      <td>University of Alabama at Birmingham</td>\n",
+              "      <td>Birmingham</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>4-year</td>\n",
+              "      <td>Public</td>\n",
+              "      <td>Research Universities--very high research acti...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>196060|180461|201885|145600|209542|236939|1268...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>106</td>\n",
+              "      <td>56.0|07</td>\n",
+              "      <td>UAB</td>\n",
+              "      <td>1376.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>100690</td>\n",
+              "      <td>Amridge University</td>\n",
+              "      <td>Montgomery</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>4-year</td>\n",
+              "      <td>Private not-for-profit</td>\n",
+              "      <td>Baccalaureate Colleges--Arts &amp; Sciences</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>217925|441511|205124|247825|197647|221856|1353...</td>\n",
+              "      <td>16</td>\n",
+              "      <td>252</td>\n",
+              "      <td>100.0|07</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>100706</td>\n",
+              "      <td>University of Alabama at Huntsville</td>\n",
+              "      <td>Huntsville</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>4-year</td>\n",
+              "      <td>Public</td>\n",
+              "      <td>Research Universities--very high research acti...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>232186|133881|196103|196413|207388|171128|1900...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>106</td>\n",
+              "      <td>43.1|07</td>\n",
+              "      <td>UAH</td>\n",
+              "      <td>759.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>100724</td>\n",
+              "      <td>Alabama State University</td>\n",
+              "      <td>Montgomery</td>\n",
+              "      <td>Alabama</td>\n",
+              "      <td>4-year</td>\n",
+              "      <td>Public</td>\n",
+              "      <td>Masters Colleges and Universities--larger prog...</td>\n",
+              "      <td>X</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>100654|232937|242617|243197|144005|241739|2354...</td>\n",
+              "      <td>13</td>\n",
+              "      <td>386</td>\n",
+              "      <td>88.0|07</td>\n",
+              "      <td>ASU</td>\n",
+              "      <td>1351.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 63 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-086e0890-f49b-44e7-9d2f-339b7816cb56')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-086e0890-f49b-44e7-9d2f-339b7816cb56 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-086e0890-f49b-44e7-9d2f-339b7816cb56');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3e9f9e34-ffca-4ea8-8316-31fcb15199f4\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e9f9e34-ffca-4ea8-8316-31fcb15199f4')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3e9f9e34-ffca-4ea8-8316-31fcb15199f4 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df1"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.2:\n",
+        "\n",
+        "There are 3,798 observations. The columns are 'index', 'unitid', 'chronname', 'city', 'state', 'level', 'control', 'basic', 'hbcu', 'flagship', 'long_x', 'lat_y', 'site', 'student_count', 'awards_per_value', 'awards_per_state_value', 'awards_per_natl_value', 'exp_award_value', 'exp_award_state_value', 'exp_award_natl_value', 'exp_award_percentile', 'ft_pct', 'fte_value', 'fte_percentile', 'med_sat_value', 'med_sat_percentile', 'aid_value', 'aid_percentile', 'endow_value', 'endow_percentile', 'grad_100_value', 'grad_100_percentile', 'grad_150_value', 'grad_150_percentile', 'pell_value', 'pell_percentile', 'retain_value', 'retain_percentile', 'ft_fac_value', 'ft_fac_percentile', 'vsa_year', 'vsa_grad_after4_first', 'vsa_grad_elsewhere_after4_first', 'vsa_enroll_after4_first', 'vsa_enroll_elsewhere_after4_first', 'vsa_grad_after6_first', 'vsa_grad_elsewhere_after6_first', 'vsa_enroll_after6_first', 'vsa_enroll_elsewhere_after6_first', 'vsa_grad_after4_transfer', 'vsa_grad_elsewhere_after4_transfer', 'vsa_enroll_after4_transfer', 'vsa_enroll_elsewhere_after4_transfer', 'vsa_grad_after6_transfer', 'vsa_grad_elsewhere_after6_transfer', 'vsa_enroll_after6_transfer', 'vsa_enroll_elsewhere_after6_transfer', 'similar', 'state_sector_ct', 'carnegie_ct', 'counted_pct', 'nicknames', 'cohort_size'"
+      ],
+      "metadata": {
+        "id": "IcKBL3jOF3zw"
+      },
+      "id": "IcKBL3jOF3zw"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.3: Crosstabulate\n",
+        "assert {\"control\",\"level\"}.issubset(df1.columns), \"Expected 'control' and 'level' in columns.\"\n",
+        "ct = pd.crosstab(df1[\"control\"], df1[\"level\"])\n",
+        "print(\"Crosstab: control x level\")\n",
+        "ct"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 192
+        },
+        "id": "TCGVxpAlGF7n",
+        "outputId": "bba343cf-5b09-444d-8fee-59b2c03ea120"
+      },
+      "id": "TCGVxpAlGF7n",
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Crosstab: control x level\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "level                   2-year  4-year\n",
+              "control                               \n",
+              "Private for-profit         465     527\n",
+              "Private not-for-profit      68    1180\n",
+              "Public                     926     632"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-77c6858f-46de-4cde-9a8d-68ea5eeeeecd\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>level</th>\n",
+              "      <th>2-year</th>\n",
+              "      <th>4-year</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>control</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Private for-profit</th>\n",
+              "      <td>465</td>\n",
+              "      <td>527</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Private not-for-profit</th>\n",
+              "      <td>68</td>\n",
+              "      <td>1180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Public</th>\n",
+              "      <td>926</td>\n",
+              "      <td>632</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-77c6858f-46de-4cde-9a8d-68ea5eeeeecd')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-77c6858f-46de-4cde-9a8d-68ea5eeeeecd button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-77c6858f-46de-4cde-9a8d-68ea5eeeeecd');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-af8a9e02-c854-4ae0-8358-b630dd1f3003\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-af8a9e02-c854-4ae0-8358-b630dd1f3003')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-af8a9e02-c854-4ae0-8358-b630dd1f3003 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_bf994900-f0c8-499d-a31a-f64f621fc00f\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('ct')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_bf994900-f0c8-499d-a31a-f64f621fc00f button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('ct');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "ct",
+              "summary": "{\n  \"name\": \"ct\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"control\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Private for-profit\",\n          \"Private not-for-profit\",\n          \"Public\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"2-year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 429,\n        \"min\": 68,\n        \"max\": 926,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          465,\n          68,\n          926\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"4-year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 350,\n        \"min\": 527,\n        \"max\": 1180,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          527,\n          1180,\n          632\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "There are more private not-for-profit 4 year programs than private for-profit. There are also more private for-profit 2 year programs than private not-for profit."
+      ],
+      "metadata": {
+        "id": "_yqXnmZQlzPy"
+      },
+      "id": "_yqXnmZQlzPy"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.4: Create plots for grad_100_level\n",
+        "#Histogram\n",
+        "sns.histplot(data=df1, x=\"grad_100_value\", bins=20)\n",
+        "plt.title(\"Histogram of Graduation Rate\")"
+      ],
+      "metadata": {
+        "id": "6xHEuHXMGFrH",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "outputId": "1841b001-014d-4bdb-b2d6-dbd1f5788022"
+      },
+      "id": "6xHEuHXMGFrH",
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Histogram of Graduation Rate')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#KDE\n",
+        "sns.kdeplot(data=df1, x=\"grad_100_value\")\n",
+        "plt.title(\"KDE of Graduation Rate\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "-EY0agXdmcdB",
+        "outputId": "930d3855-bf12-4a72-df7d-85f6d9e6e3d2"
+      },
+      "id": "-EY0agXdmcdB",
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'KDE of Graduation Rate')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Boxplot\n",
+        "sns.boxplot(data=df1, x=\"grad_100_value\")\n",
+        "plt.title(\"Boxplot of Graduation Rate\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "ekPUBnkNmh1j",
+        "outputId": "b9be78be-e502-4c3f-895f-98ed25eb3eb7"
+      },
+      "id": "ekPUBnkNmh1j",
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot of Graduation Rate')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Statistical Description\n",
+        "df1['grad_100_value'].describe()"
+      ],
+      "metadata": {
+        "id": "VKW3pchgFAP1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "outputId": "3bd64c1d-0b82-403d-8ccd-7d42c591a4ce"
+      },
+      "id": "VKW3pchgFAP1",
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    3467.000000\n",
+              "mean       28.364465\n",
+              "std        23.312730\n",
+              "min         0.000000\n",
+              "25%         9.000000\n",
+              "50%        22.500000\n",
+              "75%        43.650000\n",
+              "max       100.000000\n",
+              "Name: grad_100_value, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>grad_100_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>3467.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>28.364465</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>23.312730</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>9.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>22.500000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>43.650000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>100.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.5: Group KDE\n",
+        "#KDE by control\n",
+        "sns.kdeplot(data=df1, x='grad_100_value', hue='control')\n",
+        "plt.show()\n",
+        "\n",
+        "#KDE by level\n",
+        "sns.kdeplot(data=df1, x='grad_100_value', hue='level')\n",
+        "plt.show()\n",
+        "\n",
+        "#Grouped stats\n",
+        "print(df1.groupby(['level','control'])['grad_100_value'].describe())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ehWXg-qhm8wx",
+        "outputId": "b5245978-5d54-4efc-c2ca-cc9dedab7502"
+      },
+      "id": "ehWXg-qhm8wx",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                count       mean        std  min     25%  \\\n",
+            "level  control                                                             \n",
+            "2-year Private for-profit       461.0  37.197614  25.497380  0.0  15.800   \n",
+            "       Private not-for-profit    68.0  41.913235  28.348302  0.0  16.225   \n",
+            "       Public                   926.0  12.400000  10.893600  0.0   5.100   \n",
+            "4-year Private for-profit       318.0  17.382704  20.788525  0.0   0.000   \n",
+            "       Private not-for-profit  1121.0  41.645674  23.243453  0.0  25.000   \n",
+            "       Public                   573.0  25.561082  16.403902  0.0  13.600   \n",
+            "\n",
+            "                                50%     75%    max  \n",
+            "level  control                                      \n",
+            "2-year Private for-profit      33.9  57.800  100.0  \n",
+            "       Private not-for-profit  44.4  61.200  100.0  \n",
+            "       Public                   9.4  16.175   97.8  \n",
+            "4-year Private for-profit      12.5  26.325  100.0  \n",
+            "       Private not-for-profit  41.0  57.800  100.0  \n",
+            "       Public                  21.8  33.800   86.3  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.5:\n",
+        "\n",
+        "For the KDE plots for the \"control\" and the \"level\" I observed the following:\n",
+        "\n",
+        "For control: Private not-for-profit schools show much higher graduation rates than both public and private for-profit schools, which are clustered at lower values.\n",
+        "\n",
+        "For level: 4-year institutions have noticeably higher graduation rates compared to 2-year institutions, which are concentrated at the very low end.\n",
+        "\n",
+        "Based on the grouped statistics, private not-for-profit institutions (both 2-year and 4-year) have the highest mean and median graduation rates, while public 2-year institutions consistently have the lowest. Private for-profit schools fall in between, with wide variability but generally lower averages than private not-for-profits."
+      ],
+      "metadata": {
+        "id": "8vHKdnJKnyxG"
+      },
+      "id": "8vHKdnJKnyxG"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.6: New Variable \"levelXcontrol\"\n",
+        "df1['levelXcontrol'] = df1['level'] + \", \" + df1['control']\n",
+        "#Group KDE plot\n",
+        "sns.kdeplot(data=df1, x='grad_100_value', hue='levelXcontrol')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "bOrmAvQxnqQl",
+        "outputId": "7826e11b-e986-48eb-ee91-371fdc2d63cf"
+      },
+      "id": "bOrmAvQxnqQl",
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.5:\n",
+        "From this plot, 4-year private not-for-profit institutions stand out as having the best graduation rates, with their distribution shifted much farther to the right compared to other groups. In contrast, 2-year public institutions cluster heavily at the very low end, showing the weakest graduation outcomes."
+      ],
+      "metadata": {
+        "id": "0M4k8IOpo9w4"
+      },
+      "id": "0M4k8IOpo9w4"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.7: KDE for \"aid_value\"\n",
+        "#KDE overall\n",
+        "sns.kdeplot(df1['aid_value'])\n",
+        "plt.show()\n",
+        "\n",
+        "#KDE grouped\n",
+        "sns.kdeplot(data=df1, x='aid_value', hue='levelXcontrol')\n",
+        "plt.show()\n",
+        "\n",
+        "#Grouped stats\n",
+        "print(df1.groupby(['level','control'])['aid_value'].describe())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "baPA7ycSo5D7",
+        "outputId": "a88601b9-cd9e-4b4b-c3d5-68ea66bb3546"
+      },
+      "id": "baPA7ycSo5D7",
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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jx9Xjd+/ezT333ENQUBC+vr7069ePvXv3OlxTo9GwaNEiRo0ahbe3N6+++qrTr0mj0RAaGkp4eDjDhg1j1qxZbNmyhaKiIubNm8ftt9/ucPw777xDVFRUleu8/PLLBAcH4+Pjw+OPP05paWmN97x86ic7O5vp06fTrFkzPD096dSpE+vWrXP6NYgbnwQqwmWXZ1Q0Go3UqYirwmazUVha1ihfrk5jzpgxgxEjRnD33XfXeay3tzfjxo0jPj7e4fH4+HjGjh2LyWTCbDYzZMgQTCYTO3bsICkpCaPRyNChQ9UP+ry8PCZOnMjOnTvZtWsX0dHRDB8+XA107ObNm8d9993HgQMH+OMf/+jS66rMYDBgtVopKytz+pytW7eSkpJCYmIiX375JatWreLll1926lyr1cqwYcNISkpi2bJlHDp0iAULFqDT6er7EsQNSKZ+hMtyzY41KgC+Hr5kFmaq9StCNIQis4UOL169aYbaHJo/BC+9c/9EfvXVV+zdu5fdu3c7ff2pU6fSq1cvMjIyCAsLIzMzkw0bNrBlyxYAVqxYgdVqZfHixWg0GkAJZPz8/EhMTGTw4MEMHDjQ4Zoff/wxfn5+bNu2jXvvvVd9fPz48UyePNnpsVUnNTWVDz/8kG7dumEymeo+oZxer+ezzz7Dy8uLjh07Mn/+fObOncsrr7yCVlv778pbtmzhp59+IiUlhVtvvRWAW2655Ypeh7jxSEZFuOzyjAqAyd3k8JwQTUV6ejpPPvkky5cvx9PTs9pjHn/8cYxGo/oF0KNHDzp27MiSJUsAWLZsGS1btqRv374AJCcnc/ToUUwmk3peQEAAxcXFHDt2DIBz584xbdo0oqOj8fX1xcfHh/z8fE6ePOlw/27dutXrteXk5GA0GvHy8qJt27Y0a9aM5cuXu3SNLl264OVVUbfWs2dP8vPzSU9Pr/Pcffv2ERERoQYpommSjIpwmVqj4l4pUNFLoCIansFdx6H5Qxrt3s7Ys2cPmZmZxMTEqI9ZLBa2b9/Oe++9R0lJCfPnz2fOnDlVzp06dSrvv/8+zz//PPHx8UyePFnNnuTn59O1a9dqAwN7wenEiRO5ePEiCxcupGXLlnh4eNCzZ88qNSDe3t5Ov+7KTCYTe/fuRavVEhYWhsFgUJ/TarVVpsfMZnO97lOTyvcTTZcEKsJl1WZUyv/f3gxOiIag0Wicnn5pLIMGDeLAgQMOj02ePJl27drx3HPPodPpCAkJISQkpMq5jzzyCM8++yzvvvsuhw4dYuLEiepzMTExrFixgpCQEHx8fKq9d1JSEh988AHDhw8HlOzOhQsXGuy1abVa2rRpU+1zwcHBnD17FpvNpgZX+/btq3JccnIyRUVFatCxa9cujEYjkZGRdd6/c+fOnDp1iiNHjkhWpQmTqR/hstoCFalREU2NyWSiU6dODl/e3t4EBgbSqVOnWs/19/dnzJgxzJ07l8GDBxMREaE+FxcXR1BQELGxsezYsYO0tDQSExOZNWsWp06dAiA6OpqlS5eSkpLCjz/+SFxc3DXLQvTv35/z58/zxhtvcOzYMd5//302btxY5bjS0lKmTJnCoUOH2LBhAy+99BIzZ86ssz4FoF+/fvTt25f777+fzZs3k5aWxsaNG9m0adPVeEniOiWBinCJ2WKm2FIMOAYq9i61MvUjhGumTJlCaWlpldU4Xl5ebN++nRYtWjBmzBjat2/PlClTKC4uVjMsn376KVlZWcTExDBhwgRmzZpVbeamOlFRUcybN6/e427fvj0ffPAB77//Pl26dOGnn36qdnpr0KBBREdH07dvXx566CFGjRrl0n1XrlxJ9+7defjhh+nQoQPPPvssFoul3uMWNx6N7QZuJZqbm4uvry85OTk1pkZFw7pUfIl+K/oBsG/CPnRaZR4/4WACb+15i3tvuZfX+rzWmEMUN7Di4mLS0tJo1apVjYWpN5ulS5fy1FNPcebMGfR6/TW5Z2FhIYGBgWzcuJH+/ftfk3uKpqe2n2dXPr+v78lfcd0pNBcCSpM3e5AClWpUSqVGRQhnFBYWkpGRwYIFC5g+ffo1C1IAvvvuOwYOHChBirghyNSPcElhWUWgUpnUqAjhmjfeeIN27doRGhrKCy+8cE3vPWLECNavX39N7ylEfUmgIlxiz6h4uTnu52PUl9eomKVGRQhnzJs3D7PZzNatW9XeKkKIqiRQES5RA5XLNh700StzjDL1I4QQoiFJoCJcYp/6uTyjIg3fhBBCXA0SqAiXqIGKe/WBSr45H4tVlg4KIYRoGBKoCJfYp3683R1bcldupy/daYUQQjQUCVSES2pa9eOuc8dTp6yTl0BFCCFEQ5FARbikwFwAVK1RAalTEUII0fAkUBEuqWnVD1RaoiyBihBN1qRJkxg9enRjD+O6UFhYyP3334+Pjw8ajYbs7OzGHpJTbDYbjz32GAEBAWg0Gvbt20f//v2ZPXt2o4xHAhXhkqKyIqD2jIo0fRNN2YIFC9BoNI32j3pDSUhIQKPRoNFo0Gq1REREMHnyZDIzM2s9b+HChSQkJDToWKKionjnnXca9Jr1lZiY6HTQsWTJEnbs2MH3339PRkYGvr6+V3+ADWDTpk0kJCSwbt06MjIy6NSpE6tWreKVV15Rj7mW35NGDVQsFgt//etfadWqFQaDgdatW/PKK69wA28/dNOrqZgWpI2+ELt37+ajjz6ic+fOjT2UGpnNZqeP9fHxISMjg1OnTvHJJ5+wceNGJkyYUO2xFosFq9WKr68vfn5+DTTaG9uxY8do3749nTp1IjQ0FI1G4/I17O9rQygtLXXquGPHjhEWFkavXr0IDQ3Fzc2NgIAATCZT3SdfBY0aqLz++ussWrSI9957j5SUFF5//XXeeOMN/vnPfzbmsEQtalqeDODjrjR9k6kf0RTl5+cTFxfHJ598gr+/f63HFhQU4OPjw9dff+3w+Jo1a/D29iYvT/kZSk9P58EHH8TPz4+AgABiY2M5fvy4evzu3bu55557CAoKwtfXl379+rF3716Ha2o0GhYtWsSoUaPw9vbm1Vdfdfo1aTQaQkNDCQ8PZ9iwYcyaNYstW7ZQVFREQkICfn5+rF27lg4dOuDh4cHJkycdpn4+/vhjwsPDq3zQxsbGqrtFHzt2jNjYWJo1a4bRaKR79+5s2bJFPbZ///6cOHGCp556Ss3w2O3cuZM+ffpgMBiIjIxk1qxZFBQU1Ph65s2bx+23387SpUuJiorC19eXcePGqe83QElJiboLtaenJ3fddRe7d+8G4Pjx4wwYMAAAf39/NBoNkyZNqvZe/fv356233mL79u1oNBp1X6WsrCweffRR/P398fLyYtiwYaSmpqrn1fS+Xs7+Pr/88ssEBwfj4+PD448/7hCM9O/fn5kzZzJ79myCgoIYMmQIANu2baNHjx54eHgQFhbG888/T1lZmXrdJ554gpMnT6LRaIiKilKvZc8S1vY9uRoaNVD5/vvviY2NZcSIEURFRTF27FgGDx7MTz/91JjDErWorZhWalREg7PZoLSgcb5czOzOmDGDESNGcPfdd9d5rLe3N+PGjSM+Pt7h8fj4eMaOHYvJZMJsNjNkyBBMJhM7duwgKSkJo9HI0KFD1Q+jvLw8Jk6cyM6dO9m1axfR0dEMHz7c4YMXlA/o++67jwMHDqgBQn0YDAasVqv6oVZYWMjrr7/O4sWL+fXXXwkJCXE4/oEHHuDixYt899136mOXLl1i06ZNxMXFAUqAN3z4cLZu3covv/zC0KFDGTlypPrhvGrVKiIiIpg/fz4ZGRlkZGQASoAzdOhQ7r//fvbv38+KFSvYuXMnM2fOrPU1HDt2jDVr1rBu3TrWrVvHtm3bWLBggfr8s88+y8qVK1myZAl79+6lTZs2DBkyhEuXLhEZGcnKlSsBOHz4MBkZGSxcuLDa+6xatYpp06bRs2dPMjIyWLVqFaAEAj///DNr167lhx9+wGazMXz4cIdMV13vq93WrVtJSUkhMTGRL7/8klWrVvHyyy87HLNkyRL0ej1JSUl8+OGHnD59muHDh9O9e3eSk5NZtGgRn376KX/7298AZepu/vz5REREkJGRoQZpl7+26r4nV0uj7p7cq1cvPv74Y44cOcKtt95KcnIyO3fu5O233672+JKSEkpKStQ/5+ZKLcS1VltGRV31I/v9iIZiLoS/hzfOvf/nDOirTnFW56uvvmLv3r3V/qNek6lTp9KrVy8yMjIICwsjMzOTDRs2qNmEFStWYLVaWbx4sfoba3x8PH5+fiQmJjJ48GAGDhzocM2PP/4YPz8/tm3bxr333qs+Pn78eCZPnuz02KqTmprKhx9+SLdu3dQpALPZzAcffECXLl2qPcff359hw4bxxRdfMGjQIAC+/vprgoKC1MxEly5dHM5/5ZVXWL16NWvXrmXmzJkEBASg0+kwmUyEhoaqx7322mvExcWpv+VHR0fz7rvv0q9fPxYtWoSnp2e1Y7JarSQkJKivYcKECWzdupVXX32VgoICFi1aREJCAsOGDQPgk08+YfPmzXz66afMnTuXgIAAAEJCQmqd4goICMDLywu9Xq+OOzU1lbVr15KUlESvXr0AWL58OZGRkaxZs4YHHnjAqffVTq/X89lnn+Hl5UXHjh2ZP38+c+fO5ZVXXkGr1arvyxtvvKGe85e//IXIyEjee+89NBoN7dq148yZMzz33HO8+OKL+Pr6YjKZ0Ol0Du/35a+tuu/J1dKoGZXnn3+ecePG0a5dO9zd3bnjjjuYPXu2Gmlf7rXXXsPX11f9ioyMvMYjFvYalcv7qIAsTxZNU3p6Ok8++STLly+v8cPx8ccfx2g0ql8APXr0oGPHjixZsgSAZcuW0bJlS/r27QtAcnIyR48exWQyqecFBARQXFzMsWPHADh37hzTpk0jOjoaX19ffHx8yM/PrzJV0K1bt3q9tpycHIxGI15eXrRt25ZmzZqxfPly9Xm9Xl9nPU5cXBwrV65Uf8lcvnw548aNUz9I8/PzmTNnDu3bt8fPzw+j0UhKSkq10x2VJScnk5CQ4PC+DhkyBKvVSlpaWo3nRUVFOdRa2INEULItZrOZ3r17q8+7u7vTo0cPUlJSarzm8uXLHcaxY8eOao9LSUnBzc2NO++8U30sMDCQtm3bOly/8vt68uRJh2v//e9/V4/r0qULXl4VvzT27NmT/Px80tPT1ce6du1aZQw9e/Z0mK7p3bs3+fn5nDp1qsbX2JgaNaPyr3/9i+XLl/PFF1/QsWNH9u3bx+zZswkPD2fixIlVjn/hhRd4+umn1T/n5uZKsHKN2TMq1RXT2jcmlEBFNBh3LyWz0Vj3dsKePXvIzMwkJiZGfcxisbB9+3bee+89SkpKmD9/PnPmzKly7tSpU3n//fd5/vnniY+PZ/LkyeoHSH5+Pl27dnUIDOyCg4MBmDhxIhcvXmThwoW0bNkSDw8PevbsWaVo0tvbuczQ5UwmE3v37kWr1RIWFobB4PgLisFgqLM+YeTIkdhsNtavX0/37t3ZsWMH//jHP9Tn58yZw+bNm3nzzTdp06YNBoOBsWPH1ln4mZ+fz/Tp05k1a1aV51q0aFHjee7u7g5/1mg0V1ysOmrUKIfgo3nz5ld0vcrva3h4OPv27VOfs2d0nFXf7/31pFEDlblz56pZFYDbbruNEydO8Nprr1UbqHh4eODh4XGthykqKTLXvDzZ6C41KqKBaTROT780lkGDBnHgwAGHxyZPnky7du147rnn0Ol0hISEVFtn8Mgjj/Dss8/y7rvvcujQIYd/92JiYlixYgUhISH4+PhUe++kpCQ++OADhg8fDijZnQsXLjTYa9NqtbRp0+aKruHp6cmYMWNYvnw5R48epW3btg5BXVJSEpMmTeK+++4DlACkcsEwKBkGi8VxD7GYmBgOHTp0xeOrrHXr1mo9R8uWLQFlGmb37t3qFJNerwdwGI/JZHJqRUz79u0pKyvjxx9/VKd+Ll68yOHDh+nQoUO157i5udX4GpOTkykqKlIDyF27dmE0Gmv9Bb59+/asXLkSm82mBkNJSUmYTCYiIiLqfA121X1PrpZGnfopLCxU0392Op2uwZZiiYZls9koKCsvpq2tRkUCFdGEmEwmOnXq5PDl7e1NYGAgnTp1qvVcf39/xowZw9y5cxk8eLDDB0VcXBxBQUHExsayY8cO0tLSSExMZNasWWqKPjo6mqVLl5KSksKPP/5IXFxclazH9SAuLo7169fz2WefVZnaj46OZtWqVezbt4/k5GTGjx9f5TMgKiqK7du3c/r0aTUQe+655/j++++ZOXMm+/btIzU1lW+++abOYtraeHt786c//Ym5c+eyadMmDh06xLRp0ygsLGTKlCkAtGzZEo1Gw7p16zh//jz5+c63Y4iOjiY2NpZp06axc+dOkpOTeeSRR2jevDmxsbEuj7e0tJQpU6Zw6NAhNmzYwEsvvcTMmTOrfK5W9uc//5n09HSeeOIJfvvtN7755hteeuklnn766VrPu1x135OrpVEDlZEjR/Lqq6+yfv16jh8/zurVq3n77bfVyFpcX0osJVhtyj8g0kJfiIYxZcoUSktLq6zG8fLyYvv27bRo0YIxY8bQvn17pkyZQnFxsZph+fTTT8nKyiImJoYJEyaoy2qdERUVxbx58xr65VRr4MCBBAQEcPjwYcaPH+/w3Ntvv42/vz+9evVi5MiRDBkyxCHjAjB//nyOHz9O69at1Wmvzp07s23bNo4cOUKfPn244447ePHFFwkPv7Li6wULFnD//fczYcIEYmJiOHr0KN9++6265Lx58+a8/PLLPP/88zRr1szlwCg+Pp6uXbty77330rNnT2w2Gxs2bKgyJeWMQYMGER0dTd++fXnooYcYNWpUnd/T5s2bs2HDBn766Se6dOnC448/zpQpU/jf//1fl+5d3ffkatHYGrG7Wl5eHn/9619ZvXo1mZmZhIeH8/DDD/Piiy+q6bXa5Obm4uvrS05OTo2pUdFwLhVfot+KfgDsm7APnVbn8Pzv2b8T+00svh6+7By3szGGKG5wxcXFpKWl0apVqxoLU282S5cu5amnnuLMmTNO/bvXEAoLCwkMDGTjxo1qfw9xY5k0aRLZ2dmsWbOmsYdSo9p+nl35/G7UGhWTycQ777xz3bRGFrWrvOLn8iAFKvqo5JfmO8x/CiGqKiwsJCMjgwULFjB9+vRrFqQAfPfddwwcOFCCFHFDkL1+hNPsK36qW5oMFVM/FptF3RNICFG9N954g3bt2hEaGsoLL7xwTe89YsQI1q9ff03vKUR9NWpGRdxY1J2Tq6lPAfDUeeKmdaPMWkZuaW61BbdCCMW8efOuWY2IuPk09MaP1zPJqAinqYFKDQGIRqPB5C4bEwohhGg4EqgIp6nt82vIqEBFI7h8swQqQgghrpwEKsJptXWltZMlykIIIRqSBCrCaXVN/UCllT+SURFCCNEAJFARTqtr1Q9UtNGXQEUIIURDkEBFOK3AXN4+v5YaFfvUjxTTCiEmTZrE6NGjaz2mf//+6j46oHTMld5aojIJVITTnJn6sdevSI2KaEpee+01unfvjslkIiQkhNGjR3P48OHGHtYVSUhIQKPRoNFo0Gq1REREMHnyZDIzM6/qfXfv3s1jjz12Ve8hbiwSqAin2Zu41VZMK1M/oinatm0bM2bMYNeuXWzevBmz2czgwYMpKCho7KFVYTabnT7Wx8eHjIwMTp06xSeffMLGjRuZMGHCVRwdBAcH4+UlPZhEBQlUhNPqavgGMvUjmqZNmzYxadIkOnbsSJcuXUhISODkyZPs2bOn2uMLCgrw8fHh66+/dnh8zZo1eHt7k5enZCTT09N58MEH8fPzIyAggNjYWI4fP64ev3v3bu655x6CgoLw9fWlX79+7N271+GaGo2GRYsWMWrUKLy9vXn11Vedfl0ajYbQ0FDCw8MZNmwYs2bNYsuWLRQVFTFv3jxuv/12h+PfeecdoqKiqlzn5ZdfJjg4GB8fHx5//HFKS0trvOflUz/Z2dlMnz6dZs2a4enpSadOnVi3bp3Tr0Hc+KQzrXCa2kdFVv2Ia8RmszXadgwGN0O996vKyckBICAgoNrnvb29GTduHPHx8YwdO1Z93P5nk8mE2WxmyJAh9OzZkx07duDm5sbf/vY3hg4dyv79+9Hr9eTl5TFx4kT++c9/YrPZeOuttxg+fDipqamYTCb1uvPmzWPBggW88847uLnV/599g8GA1WqlrKzM6XO2bt2Kp6cniYmJHD9+nMmTJxMYGOhUwGS1Whk2bBh5eXksW7aM1q1bc+jQIXS6qnuNiZuXBCrCaU4V09o700qgIhpAUVkRd35xZ6Pc+8fxP9ZrGwir1crs2bPp3bs3nTp1qvG4qVOn0qtXLzIyMggLCyMzM5MNGzawZcsWAFasWIHVamXx4sVqwBQfH4+fnx+JiYkMHjyYgQMHOlzz448/xs/Pj23btnHvvfeqj48fP57Jkye7/FoqS01N5cMPP6Rbt24OQVBd9Ho9n332GV5eXnTs2JH58+czd+5cXnnlFbTa2pP6W7Zs4aeffiIlJYVbb70VgFtuueWKXoe48cjUj3CaMxkVtTOtTP2IJmrGjBkcPHiQr776Sn3s8ccfx2g0ql8APXr0oGPHjixZsgSAZcuW0bJlS/r27QtAcnIyR48exWQyqecFBARQXFzMsWPHADh37hzTpk0jOjoaX19ffHx8yM/P5+TJkw5j6tatW71eS05ODkajES8vL9q2bUuzZs1Yvny5S9fo0qWLQ81Jz549yc/PJz09vc5z9+3bR0REhBqkiKZJMirCaa7UqMiqH9EQDG4Gfhz/Y6Pd21UzZ85k3bp1bN++nYiICPXx+fPnM2fOnCrHT506lffff5/nn3+e+Ph4Jk+erGZP8vPz6dq1a7WBQXBwMAATJ07k4sWLLFy4kJYtW+Lh4UHPnj2r1IB4e9dcAF8bk8nE3r170Wq1hIWFYTBUvCdarRabzeZwvCuFus6ofD/RdEmgIpzmVI2KrPoRDUij0dwQu3DbbDaeeOIJVq9eTWJiIq1atXJ4PiQkhJCQkCrnPfLIIzz77LO8++67HDp0iIkTJ6rPxcTEsGLFCkJCQvDx8an2vklJSXzwwQcMHz4cUIpvL1y40GCvS6vV0qZNm2qfCw4O5uzZs9hsNjW42rdvX5XjkpOTKSoqUoOOXbt2YTQaiYyMrPP+nTt35tSpUxw5ckSyKk2YTP0IpxWZlaLG2jIqlYtpL/9tS4ib1YwZM1i2bBlffPEFJpOJs2fPcvbsWYqKai8E9vf3Z8yYMcydO5fBgwc7ZGHi4uIICgoiNjaWHTt2kJaWRmJiIrNmzeLUqVMAREdHs3TpUlJSUvjxxx+Ji4u7ZlmI/v37c/78ed544w2OHTvG+++/z8aNG6scV1paypQpUzh06BAbNmzgpZdeYubMmXXWpwD069ePvn37cv/997N582bS0tLYuHEjmzZtuhovSVynJFARTrHZbE610LdP/ZRZyyixlFyTsQnR2BYtWkROTg79+/cnLCxM/VqxYkWd506ZMoXS0lL++Mc/Ojzu5eXF9u3badGiBWPGjKF9+/ZMmTKF4uJiNcPy6aefkpWVRUxMDBMmTGDWrFnVZm6qExUVxbx581x+rXbt27fngw8+4P3336dLly789NNP1U5vDRo0iOjoaPr27ctDDz3EqFGjXLrvypUr6d69Ow8//DAdOnTg2WefxWKx1Hvc4sajsd3Av/bm5ubi6+tLTk5OjalR0TBKLCV0W6YU5P3w8A9q5uRyVpuV2z+/HRs2vnvwO4IMQddymOIGV1xcTFpaGq1atcLT07Oxh3NNLF26lKeeeoozZ86g1+uvyT0LCwsJDAxk48aN9O/f/5rcUzQ9tf08u/L5LRkV4RR7IS3UnlHRarRqnYoU1ApRs8LCQo4dO8aCBQuYPn36NQtSAL777jsGDhwoQYq4IUigIpxin/bx1Hmi09bebEmtU5ElykLU6I033qBdu3aEhobywgsvXNN7jxgxgvXr11/TewpRXxKoCKfYMyrOLNmU7rRC1G3evHmYzWa2bt2q9lYRQlQlgYpwijNLk+2kO60QQoiGIoGKcIp9vxVnMirSnVYIIURDkUBFOEXtSutERsU+9SPFtEIIIa6UBCrCKerUTy3N3uxk6kcIIURDkUBFOMWZfX7spJhWCCFEQ5FARThFrVFxr7tGxd6dVmpUhBBCXCkJVIRTXMmoqMW0klERosmZNGkSo0ePbuxh1Ojjjz8mMjISrVbLO++809jDcdqaNWto06YNOp2O2bNnk5CQgJ+fX2MP65qQQEU4xZ5RcWrqRzrTiiZswYIFaDQaZs+e3dhDuSIJCQloNBo0Gg1arZaIiAgmT55MZmZmrectXLiQhISEBh1LVFRUgwQVubm5zJw5k+eee47Tp0/z2GOPXfngrpHp06czduxY0tPTeeWVV3jooYc4cuSI+vy8efO4/fbbG2+AV5FbYw9A3Bhc6qNSPvVTYC64qmMS4nqze/duPvroIzp37tzYQ6mR2WzG3d3dqWN9fHw4fPgwVquV5ORkJk+ezJkzZ/j222+rHGuxWNBoNPj6+jb0kBvMyZMnMZvNjBgxgrCwsHpfx5X3sDb296yunaTz8/PJzMxkyJAhhIeHq49fq52yG5tkVIRTXCqmlYyKaILy8/OJi4vjk08+wd/fv9ZjCwoK8PHx4euvv3Z4fM2aNXh7e5OXp/zspKen8+CDD+Ln50dAQACxsbEcP35cPX737t3cc889BAUF4evrS79+/di7d6/DNTUaDYsWLWLUqFF4e3vz6quvOv2aNBoNoaGhhIeHM2zYMGbNmsWWLVsoKipSpx7Wrl1Lhw4d8PDw4OTJkw5TPx9//DHh4eFYrVaH68bGxqq7RR87dozY2FiaNWuG0Wike/fubNmyRT22f//+nDhxgqeeekrN8Njt3LmTPn36YDAYiIyMZNasWRQUVP8LUkJCArfddhsAt9xyCxqNRn0vFy1aROvWrdHr9bRt25alS5e6/B4mJiai0WhYv349nTt3xtPTkz/84Q8cPHjQYQzVvWdZWVk8+uij+Pv74+XlxbBhw0hNTVWvazIpv/wNHDgQjUZDYmKiw9RPQkICL7/8MsnJyep71NBZrcYkgYpwSn0yKlKjIq6UzWbDWljYKF+ubiw/Y8YMRowYwd13313nsd7e3owbN474+HiHx+Pj4xk7diwmkwmz2cyQIUMwmUzs2LGDpKQkjEYjQ4cOpbS0FIC8vDwmTpzIzp072bVrF9HR0QwfPlwNdOzmzZvHfffdx4EDB9QAoT4MBgNWq5WysjJA2Vjx9ddfZ/Hixfz666+EhIQ4HP/AAw9w8eJFvvvuO/WxS5cusWnTJuLi4gAlwBs+fDhbt27ll19+YejQoYwcOZKTJ08CsGrVKiIiIpg/fz4ZGRlkZGQASoAzdOhQ7r//fvbv38+KFSvYuXMnM2fOrHbsDz30kBoA/fTTT2RkZBAZGcnq1at58skneeaZZzh48CDTp09n8uTJDmN25T2cO3cub731Frt37yY4OJiRI0diNpvV56t7zyZNmsTPP//M2rVr+eGHH7DZbAwfPhyz2UyvXr04fPgwACtXriQjI4NevXpVeW3PPPMMHTt2VN+jhx56qMYx3mhk6kc4xZW9fqQzrWgotqIiDsd0bZR7t927B41X3YE5wFdffcXevXvZvXu309efOnUqvXr1IiMjg7CwMDIzM9mwYYP6YbpixQqsViuLFy9Wswjx8fH4+fmRmJjI4MGDGThwoMM1P/74Y/z8/Ni2bRv33nuv+vj48eOZPHmy02OrTmpqKh9++CHdunVTf8M3m8188MEHdOnSpdpz/P39GTZsGF988QWDBg0C4OuvvyYoKIgBAwYA0KVLF4fzX3nlFVavXs3atWuZOXMmAQEB6HQ6TCYToaGh6nGvvfYacXFxai1QdHQ07777Lv369WPRokV4eno6jMVgMBAYGAhAcHCweq0333yTSZMm8ec//xmAp59+ml27dvHmm2+qYwTn38OXXnqJe+65B4AlS5YQERHB6tWrefDBB6t9z1JTU1m7di1JSUlqALJ8+XIiIyNZs2YNDzzwgBoABgQEOLwHlV+b0WjEzc2t2udvdJJREU5xqeFbeUal2FKM2Wqu42ghbmzp6ek8+eSTLF++vMqHo93jjz+O0WhUvwB69OhBx44dWbJkCQDLli2jZcuW9O3bF4Dk5GSOHj2KyWRSzwsICKC4uJhjx44BcO7cOaZNm0Z0dDS+vr74+PiQn5+vZiPsunXrVq/XlpOTg9FoxMvLi7Zt29KsWTOWL1+uPq/X6+usx4mLi2PlypWUlJQAyofwuHHj1LqM/Px85syZQ/v27fHz88NoNJKSklLlNVwuOTmZhIQEh/d1yJAhWK1W0tLSnH6NKSkp9O7d2+Gx3r17k5KS4vBY5fdw2LBh6j07duzocFzPnj3V/w8ICKBt27YO17r8PUtJScHNzY0777xTfSwwMLDKeU2ZZFSEU1zpo2LPqAAUlBbg5+l3tYYlbnIag4G2e/c02r2dsWfPHjIzM4mJiVEfs1gsbN++nffee4+SkhLmz5/PnDlzqpw7depU3n//fZ5//nni4+OZPHmymj3Jz8+na9euDoGBXXBwMAATJ07k4sWLLFy4kJYtW+Lh4UHPnj3VqSE7b2/vKtdwhslkYu/evWi1WsLCwqoUbxoMBoeakeqMHDkSm83G+vXr6d69Ozt27OAf//iH+vycOXPYvHkzb775Jm3atMFgMDB27Ngqr+Fy+fn5TJ8+nVmzZlV5rkWLFi68SudUfg8XL15MUZHyb6KrRbXOvGfCkQQqwimuFNO6ad0wuBkoKisiz5wngYqoN41G4/T0S2MZNGgQBw4ccHhs8uTJtGvXjueeew6dTkdISEiV+g2ARx55hGeffZZ3332XQ4cOMXHiRPW5mJgYVqxYQUhICD4+PtXeOykpiQ8++IDhw4cDSnbnwoULDfbatFotbdq0uaJreHp6MmbMGJYvX87Ro0dp27atQ1CXlJTEpEmTuO+++wAlAKlcMAxKFsJisTg8FhMTw6FDh654fO3btycpKcnhvU9KSqJDhw41ntO8efMan9u1a5caKGVlZXHkyBHat29f6/3Lysr48ccf1amfixcvcvjw4VrHcLnq3qObhUz9CKe4UkwLlfb7kToVcZMzmUx06tTJ4cvb25vAwEA6depU67n+/v6MGTOGuXPnMnjwYCIiItTn4uLiCAoKIjY2lh07dpCWlkZiYiKzZs3i1KlTgFKXsXTpUlJSUvjxxx+Ji4u7LpesxsXFsX79ej777DO1iNYuOjqaVatWsW/fPpKTkxk/fnyVVUJRUVFs376d06dPq4HYc889x/fff8/MmTPZt28fqampfPPNNzUW09Zk7ty5JCQksGjRIlJTU3n77bdZtWpVtRkwZ8yfP5+tW7dy8OBBJk2aRFBQUK0N8KKjo4mNjWXatGns3LmT5ORkHnnkEZo3b05sbKzT942KiiItLY19+/Zx4cIFdartZiCBinCKKw3fALz10p1WCGdMmTKF0tLSKitJvLy82L59Oy1atGDMmDG0b9+eKVOmUFxcrGZYPv30U7KysoiJiWHChAnMmjWr2sxNdaKiopg3b15Dv5xqDRw4kICAAA4fPsz48eMdnnv77bfx9/enV69ejBw5kiFDhjhkXED58D9+/DitW7dWp706d+7Mtm3bOHLkCH369OGOO+7gxRdfdOgz4ozRo0ezcOFC3nzzTTp27MhHH31EfHw8/fv3r9drXbBgAU8++SRdu3bl7Nmz/Pvf/0av19d6Tnx8PF27duXee++lZ8+e2Gw2NmzY4NK00v3338/QoUMZMGAAwcHBfPnll/Ua//VIY3N1Dd51JDc3F19fX3JycmpMjYorZ7FauH3p7QAkPphIoCGwznPi1sex/8J+Fg5YyMAWA+s8XgiA4uJi0tLSaNWqVY2FqTebpUuX8tRTT3HmzJk6P9AaSmFhIYGBgWzcuLHeH8jCUWJiIgMGDCArK6vJtLavS20/z658fkuNiqhTsaVY/X9np37sOyhLd1ohqldYWEhGRgYLFixg+vTp1yxIAfjuu+8YOHCgBCnihiBTP6JO9mkfDRo8dc79livdaYWo3RtvvEG7du0IDQ3lhRdeuKb3HjFiBOvXr7+m9xSiviRQEXVSV/y4ezm9rE660wpRu3nz5mE2m9m6davaW0XcuPr374/NZpNpn6tAAhVRJ1eavdlJd1ohhBANQQIVUafKGRVn2WtU8swy9SOEEKL+JFARdbJnVJzZ58fO3keloFSKaYUQQtSfBCqiTq50pbWTjIoQQoiGIIGKqJMr+/zYSWdaIYQQDUECFVGnehXTSmdaIYQQDUACFVGn+kz92DMq0kdFiKZl0qRJte5t09g+/vhjIiMj0Wq1vPPOO409HKetWbOGNm3aoNPpmD17NgkJCU1mKbQEKqJOrm5ICNKZVjQtr732Gt27d8dkMhESEsLo0aM5fPhwYw/riiQkJCi7V2s0aLVaIiIimDx5MpmZmbWet3DhQhISEhp0LFFRUQ0SVOTm5jJz5kyee+45Tp8+zWOPPXblg7tGpk+fztixY0lPT+eVV17hoYce4siRI+rz8+bN4/bbb2+8AV5FEqiIOtkzKq6s+rH3USkwF2C1Wes4Wogb27Zt25gxYwa7du1i8+bNmM1mBg8eTEHB9Reom81mp4/18fEhIyODU6dO8cknn7Bx40YmTJhQ7bEWiwWr1Yqvr+91+5v+yZMnMZvNjBgxgrCwMLy8nP/lqzJX3sPa2N+zuuTn55OZmcmQIUMIDw/HZDJhMBic3oDyRieBiqiTqzsnQ0WgYsNGcVlxHUcLcWPbtGkTkyZNomPHjnTp0oWEhAROnjzJnj17qj2+oKAAHx8fvv76a4fH16xZg7e3N3l5ypRpeno6Dz74IH5+fgQEBBAbG8vx48fV43fv3s0999xDUFAQvr6+9OvXj7179zpcU6PRsGjRIkaNGoW3tzevvvqq069Lo9EQGhpKeHg4w4YNY9asWWzZsoWioiJ16mHt2rV06NABDw8PTp486TD18/HHHxMeHl7lwzg2NlbdLfrYsWPExsbSrFkzjEYj3bt3Z8uWLeqx/fv358SJEzz11FNqhsdu586d9OnTB4PBQGRkJLNmzaoxOExISOC2224D4JZbbkGj0ajv5aJFi2jdujV6vZ62bduydOlSl9/DxMRENBoN69evp3Pnznh6evKHP/yBgwcPOoyhuvcsKyuLRx99FH9/f7y8vBg2bBipqanqdU0mZSp94MCBaDQaEhMTHaZ+EhISePnll0lOTlbfo4bOajUmCVREneoz9eOp80Sr0TqcL4SrbDYb5hJLo3xdycbyOTk5AAQEBFT7vLe3N+PGjSM+Pt7h8fj4eMaOHYvJZMJsNjNkyBBMJhM7duwgKSkJo9HI0KFDKS0tBSAvL4+JEyeyc+dOdu3aRXR0NMOHD1cDHbt58+Zx3333ceDAATVAqA+DwYDVaqWsrAxQNlZ8/fXXWbx4Mb/++muV3/AfeOABLl68yHfffac+dunSJTZt2kRcXBygZAuGDx/O1q1b+eWXXxg6dCgjR47k5MmTAKxatYqIiAjmz59PRkYGGRkZgBLgDB06lPvvv5/9+/ezYsUKdu7cycyZM6sd+0MPPaQGQD/99BMZGRlERkayevVqnnzySZ555hkOHjzI9OnTmTx5ssOYXXkP586dy1tvvcXu3bsJDg5m5MiRDhmY6t6zSZMm8fPPP7N27Vp++OEHbDYbw4cPx2w206tXL3UaceXKlWRkZNCrV68qr+2ZZ56hY8eO6nv00EMP1TjGG43snizqVGR2PaOi0WjwcvMi35xPgbmAIEPQ1RqeuImVlVr5+MltjXLvxxb2w91D5/J5VquV2bNn07t3bzp16lTjcVOnTqVXr15kZGQQFhZGZmYmGzZsUD9MV6xYgdVqZfHixWoWIT4+Hj8/PxITExk8eDADBw50uObHH3+Mn58f27Zt495771UfHz9+PJMnT3b5tVSWmprKhx9+SLdu3dTf8M1mMx988AFdunSp9hx/f3+GDRvGF198waBBgwD4+uuvCQoKYsCAAQB06dLF4fxXXnmF1atXs3btWmbOnElAQAA6nQ6TyURoaKh63GuvvUZcXByzZ88GIDo6mnfffZd+/fqxaNEiPD0dN1A1GAwEBgYCEBwcrF7rzTffZNKkSfz5z38G4Omnn2bXrl28+eab6hjB+ffwpZde4p577gFgyZIlREREsHr1ah588MFq37PU1FTWrl1LUlKSGoAsX76cyMhI1qxZwwMPPKAGgAEBAQ7vQeXXZjQacXNzq/b5G51kVESd6pNRgYrAxl7jIkRTMGPGDA4ePMhXX32lPvb4449jNBrVL4AePXrQsWNHlixZAsCyZcto2bIlffv2BSA5OZmjR49iMpnU8wICAiguLubYsWMAnDt3jmnTphEdHY2vry8+Pj7k5+er2Qi7bt261eu15OTkYDQa8fLyom3btjRr1ozly5erz+v1ejp37lzrNeLi4li5ciUlJSWA8iE8btw4tFrl4yc/P585c+bQvn17/Pz8MBqNpKSkVHkNl0tOTiYhIcHhfR0yZAhWq5W0tDSnX2NKSgq9e/d2eKx3796kpKQ4PFb5PRw2bJh6z44dOzoc17NnT/X/AwICaNu2rcO1Ln/PUlJScHNz484771QfCwwMrHJeUyYZFVGn+hTTQnlgUyQrf0T9uem1PLawX6Pd21UzZ85k3bp1bN++nYiICPXx+fPnM2fOnCrHT506lffff5/nn3+e+Ph4Jk+erGZP8vPz6dq1q0NgYBccHAzAxIkTuXjxIgsXLqRly5Z4eHjQs2dPdWrIztvb2+XXAmAymdi7dy9arZawsDAMBsd/AwwGQ507qo8cORKbzcb69evp3r07O3bs4B//+If6/Jw5c9i8eTNvvvkmbdq0wWAwMHbs2Cqv4XL5+flMnz6dWbNmVXmuRYsWLrxK51R+DxcvXkxRkZJpdnd3d+k6zrxnwpEEKqJO9Wn4BhUZGKlREfWl0WjqNf1yrdlsNp544glWr15NYmIirVq1cng+JCSk2hUajzzyCM8++yzvvvsuhw4dYuLEiepzMTExrFixgpCQEHx8fKq9b1JSEh988AHDhw8HlOLbCxcuNNjr0mq1tGnT5oqu4enpyZgxY1i+fDlHjx6lbdu2xMTEqM8nJSUxadIk7rvvPkAJQCoXDIOShbBYLA6PxcTEcOjQoSseX/v27UlKSnJ475OSkujQoUON5zRv3rzG53bt2qUGSllZWRw5coT27dvXev+ysjJ+/PFHdern4sWLHD58uNYxXK669+hmIVM/ok7qqh8Xp37sK39k6kfc7GbMmMGyZcv44osvMJlMnD17lrNnz6q/ddfE39+fMWPGMHfuXAYPHuyQhYmLiyMoKIjY2Fh27NhBWloaiYmJzJo1i1OnTgFKXcbSpUtJSUnhxx9/JC4urkrW43oQFxfH+vXr+eyzz9QiWrvo6GhWrVrFvn37SE5OZvz48VVWCUVFRbF9+3ZOnz6tBmLPPfcc33//PTNnzmTfvn2kpqbyzTff1FhMW5O5c+eSkJDAokWLSE1N5e2332bVqlXVZsCcMX/+fLZu3crBgweZNGkSQUFBtTbAi46OJjY2lmnTprFz506Sk5N55JFHaN68ObGxsU7fNyoqirS0NPbt28eFCxfUqbabgQQqok71zqi4SUZFNA2LFi0iJyeH/v37ExYWpn6tWLGiznOnTJlCaWlplZUkXl5ebN++nRYtWjBmzBjat2/PlClTKC4uVjMsn376KVlZWcTExDBhwgRmzZrldG+NqKgo5s2b5/JrrY+BAwcSEBDA4cOHGT9+vMNzb7/9Nv7+/vTq1YuRI0cyZMgQh4wLKB/+x48fp3Xr1uq0V+fOndm2bRtHjhyhT58+3HHHHbz44ouEh4e7NLbRo0ezcOFC3nzzTTp27MhHH31EfHw8/fv3r9drXbBgAU8++SRdu3bl7Nmz/Pvf/0av19d6Tnx8PF27duXee++lZ8+e2Gw2NmzY4NK00v3338/QoUMZMGAAwcHBfPnll/Ua//VIY7uSNXiNLDc3F19fX3JycmpMjYorF7M0BrPVzOaxmwn1dr6i/Nntz7IxbSPPdn+WCR2qbxIlRGXFxcWkpaXRqlWrKqs2blZLly7lqaee4syZM3V+oDWUwsJCAgMD2bhxY70/kIWjxMREBgwYQFZW1nXb8O5aq+3n2ZXPb6lREbUyW8yYrUoPAJeLaWXVjxA1KiwsJCMjgwULFjB9+vRrFqQAfPfddwwcOFCCFHFDkKkfUavK0zauTv2obfTLZNWPEJd74403aNeuHaGhobzwwgvX9N4jRoxg/fr11/SeQtSXBCqiVvZCWjetG+4615bhqat+JKMiRBXz5s3DbDazdetWtbeKuHH1798fm80m0z5XgQQqolb1LaQF8HaTVT9CCCGujAQqolZq+3wXlyZXPkcavgkhhKivRg9UTp8+zSOPPEJgYCAGg4HbbruNn3/+ubGHJcpdSUZFGr4JIYS4Uo266icrK4vevXszYMAANm7cSHBwMKmpqfj7+zfmsEQl9mmbegUqsupHCCHEFWrUQOX1118nMjLSYavzy1tPi8Zlz4YY3F3vdql2ppWMihBCiHpq1KmftWvX0q1bN3Ub6zvuuINPPvmkxuNLSkrIzc11+BJXlysZFWtREZX7B9rPkRoVIYQQ9dWogcrvv//OokWLiI6O5ttvv+VPf/oTs2bNUrc9v9xrr72Gr6+v+hUZGXmNR9z0qPv81BGoFPzwA6m97+LUE0+oj0lGRYimZ9KkSbXubdOUFBYWcv/99+Pj44NGoyE7O7uxh+QUm83GY489RkBAABqNhn379tG/f39mz57dKONp1EDFarUSExPD3//+d+644w4ee+wxpk2bxocffljt8S+88AI5OTnqV3p6+jUecdOjFtPWsurHWlTEyT9OwVpYSP6WrVjKfxhl1Y9oKl577TW6d++OyWQiJCSE0aNHc/jw4cYe1hVJSEhAo9Gg0WjQarVEREQwefJkMjMzaz1v4cKFJCQkNOhYoqKieOeddxr0mvWVmJjodNCxZMkSduzYwffff09GRga+vr5Xf4ANYNOmTSQkJLBu3ToyMjLo1KkTq1at4pVXXlGPuZbfk0YNVMLCwqpsY92+fXtOnjxZ7fEeHh74+Pg4fImryz71U1v7/JKjx6DSlE/B998DFYFKmbWMUkvpVRylEI1r27ZtzJgxg127drF582bMZjODBw+moOD6C9LNZrPTx/r4+JCRkcGpU6f45JNP2LhxIxMmVL9vl8ViwWq14uvrK03Pyh07doz27dvTqVMnQkND0Wg0Ll/D/r42hNJS5/4dPnbsGGFhYfTq1YvQ0FDc3NwICAjAZDI1yDhc1aiBSu/evav81nHkyBFatmzZSCMSl3Mmo1Jy7KjDn/N3JinnVJoukpU/4ma2adMmJk2aRMeOHenSpQsJCQmcPHmSPXv2VHt8QUEBPj4+fP311w6Pr1mzBm9vb/Ly8gBIT0/nwQcfxM/Pj4CAAGJjYzl+/Lh6/O7du7nnnnsICgrC19eXfv36sXfvXodrajQaFi1axKhRo/D29ubVV191+nVpNBpCQ0MJDw9n2LBhzJo1iy1btlBUVERCQgJ+fn6sXbuWDh064OHhwcmTJx2mfj7++GPCw8OrfNDGxsaqu0UfO3aM2NhYmjVrhtFopHv37mzZskU9tn///pw4cYKnnnpKzfDY7dy5kz59+mAwGIiMjGTWrFm1Bofz5s3j9ttvZ+nSpURFReHr68u4cePU9xuUWkj7LtSenp7cdddd7N69G4Djx48zYMAAAPz9/dFoNEyaNKnae/Xv35+33nqL7du3o9Fo1H2VsrKyePTRR/H398fLy4thw4aRmpqqnlfT+3o5+/v88ssvExwcjI+PD48//rhDMNK/f39mzpzJ7NmzCQoKYsiQIYASWPfo0QMPDw/CwsJ4/vnnKSsrU6/7xBNPcPLkSTQaDVFRUeq17FM/tX1ProZGDVSeeuopdu3axd///neOHj3KF198wccff8yMGTMac1iiEmcyKqXHfgdAFxwEQMlRJXBx07rhofMAZL8fUT82mw1zcXGjfF3JxvI5OTkABAQEVPu8t7c348aNc1jxCBAfH8/YsWMxmUyYzWaGDBmCyWRix44dJCUlYTQaGTp0qPphlJeXx8SJE9m5cye7du0iOjqa4cOHO3zwgvIBfd9993HgwAE1QKgPg8GA1WpVP9QKCwt5/fXXWbx4Mb/++ishISEOxz/wwANcvHiR7777Tn3s0qVLbNq0ibi4OADy8/MZPnw4W7du5ZdffmHo0KGMHDlS/XBetWoVERERzJ8/n4yMDDIyMgAlwBk6dCj3338/+/fvZ8WKFezcuZOZM2fW+hqOHTvGmjVrWLduHevWrWPbtm0sWLBAff7ZZ59l5cqVLFmyhL1799KmTRuGDBnCpUuXiIyMZOXKlQAcPnyYjIwMFi5cWO19Vq1axbRp0+jZsycZGRmsWrUKUAKBn3/+mbVr1/LDDz9gs9kYPny4Q6arrvfVbuvWraSkpJCYmMiXX37JqlWrePnllx2OWbJkCXq9nqSkJD788ENOnz7N8OHD6d69O8nJySxatIhPP/2Uv/3tb4AydTd//nwiIiLIyMhQg7TLX1t135OrpVGXJ3fv3p3Vq1fzwgsvMH/+fFq1asU777yj/gUWjc+Zhm8lx44BYOo/gOz/+z/MlaJ/b3dvSiwlklER9VJWUsK7E8c2yr1nLfka98u2pneG1Wpl9uzZ9O7dm06dOtV43NSpU+nVqxcZGRmEhYWRmZnJhg0b1GzCihUrsFqtLF68WP2NNT4+Hj8/PxITExk8eDADBw50uObHH3+Mn58f27Zt495771UfHz9+PJMnT3b5tVSWmprKhx9+SLdu3dQpALPZzAcffECXLl2qPcff359hw4bxxRdfMGjQIAC+/vprgoKC1MxEly5dHM5/5ZVXWL16NWvXrmXmzJkEBASg0+kwmUyEhoaqx7322mvExcWpv+VHR0fz7rvv0q9fPxYtWoRnDd87q9VKQkKC+homTJjA1q1befXVVykoKGDRokUkJCQwbNgwAD755BM2b97Mp59+yty5c9XgMyQkpNYproCAALy8vNDr9eq4U1NTWbt2LUlJSfTq1QuA5cuXExkZyZo1a3jggQecel/t9Ho9n332GV5eXnTs2JH58+czd+5cXnnlFbRarfq+vPHGG+o5f/nLX4iMjOS9995Do9HQrl07zpw5w3PPPceLL76Ir68vJpMJnU7n8H5f/tqq+55cLY3emfbee+/lwIEDFBcXk5KSwrRp0xp7SKISddWPE1M/xgH9AbBkZ2Mp/43OnomRglrRVMyYMYODBw/y1VdfqY89/vjjGI1G9QugR48edOzYUV3luGzZMlq2bEnfvn0BSE5O5ujRo5hMJvW8gIAAiouLOVb+y8G5c+eYNm0a0dHR+Pr64uPjQ35+fpWpgm7dutXrteTk5GA0GvHy8qJt27Y0a9aM5cuXq8/r9Xo6d+5c6zXi4uJYuXIlJSUlgPLBPG7cOPWDND8/nzlz5tC+fXv8/PwwGo2kpKTUWKtol5ycTEJCgsP7OmTIEKxWK2lpaTWeFxUV5VBrYQ8SQcm2mM1mevfurT7v7u5Ojx49SElJqfGay5cvdxjHjh07qj0uJSUFNzc37rzzTvWxwMBA2rZt63D9yu/ryZMnHa7997//XT2uS5cueHlV/Nvcs2dP8vPzHRaadO3atcoYevbs6TBd07t3b/Lz8zl16lSNr7ExNWpGRVz/6uqjYrPZKDujpP0827ZFFxCA5dIlzOnp6Dp0kCXK4oq4eXgwa8nXdR94le7tqpkzZ7Ju3Tq2b99ORESE+vj8+fOZM2dOleOnTp3K+++/z/PPP098fDyTJ09WP0Dy8/Pp2rWrQ2BgFxwcDMDEiRO5ePEiCxcupGXLlnh4eNCzZ88qRZPe3t4uvxYAk8nE3r170Wq1hIWFYTA4TgEbDIY66xNGjhyJzWZj/fr1dO/enR07dvCPf/xDfX7OnDls3ryZN998kzZt2mAwGBg7dmydhZ/5+flMnz6dWbNmVXmuRYsWNZ7n7u64C7xGo7niYtVRo0Y5BB/Nmze/outVfl/Dw8PZt2+f+lxN04k1qe/3/noigYqoVV0ZFWteHrbyuVVdUBD6yEiKLl2i9GQ6nh06SBt9cUU0Gk29pl+uNZvNxhNPPMHq1atJTEys0mE7JCSk2jqDRx55hGeffZZ3332XQ4cOMXHiRPW5mJgYVqxYQUhISI0rHJOSkvjggw8YPnw4oBTfXrhwocFel1arpU2bNld0DU9PT8aMGcPy5cs5evQobdu2JSYmRn0+KSmJSZMmcd999wFKAFK5YBiUDIPFYnF4LCYmhkOHDl3x+Cpr3bq1Ws9hX9RhNpvZvXu3OsWk1+sBHMZjMpmcWhHTvn17ysrK+PHHH9Wpn4sXL3L48OEqK2Dt3NzcanyNycnJFBUVqQHkrl27MBqNtfYYa9++PStXrsRms6nBUFJSEiaTySG4rkt135OrpdGnfsT1ra4albILFwHQGo1oPTxwL/8BMZ9SUo+SURFNwYwZM1i2bBlffPEFJpOJs2fPcvbsWYqKimo9z9/fnzFjxjB37lwGDx7s8EERFxdHUFAQsbGx7Nixg7S0NBITE5k1a5aaoo+Ojmbp0qWkpKTw448/EhcXVyXrcT2Ii4tj/fr1fPbZZ1VqEKOjo1m1ahX79u0jOTmZ8ePHV8lwREVFsX37dk6fPq0GYs899xzff/89M2fOZN++faSmpvLNN9/UWUxbG29vb/70pz8xd+5cNm3axKFDh5g2bRqFhYVMmTIFgJYtW6LRaFi3bh3nz58nPz/f6etHR0cTGxvLtGnT2LlzJ8nJyTzyyCM0b96c2NhYl8dbWlrKlClTOHToEBs2bOCll15i5syZ6rRadf785z+Tnp7OE088wW+//cY333zDSy+9xNNPP13reZer7ntytUigImpV16ofy0XlL6hbYCAA7uUpT/OZMw7nSY2KuJktWrSInJwc+vfvT1hYmPq1YsWKOs+dMmUKpaWlVVbjeHl5sX37dlq0aMGYMWNo3749U6ZMobi4WM2wfPrpp2RlZRETE8OECRPUZbXOiIqKYt68eS6/1voYOHAgAQEBHD58mPHjxzs89/bbb+Pv70+vXr0YOXIkQ4YMcci4gDJ1dvz4cVq3bq1Oe3Xu3Jlt27Zx5MgR+vTpwx133MGLL75IeHj4FY11wYIF3H///UyYMIGYmBiOHj3Kt99+q26W27x5c15++WWef/55mjVr5nJgFB8fT9euXbn33nvp2bMnNpuNDRs2VJmScsagQYOIjo6mb9++PPTQQ4waNarO72nz5s3ZsGEDP/30E126dOHxxx9nypQp/O///q9L967ue3K1aGxXsgavkeXm5uLr60tOTo40f7tK/vDFHygwF7DuvnW09Kna3yZ30yZOz34KQ9euRC1fxqVlyzn3t79huuceIv75Lv+z43/49+//5umuTzO505WtOhA3v+LiYtLS0mjVqlWNqzZuNkuXLuWpp57izJkz6rTC1VZYWEhgYCAbN25U+3uIG8ukSZPIzs5mzZo1jT2UGtX28+zK57fUqIga2Wy2Ovf6sU/92DMqbuWRddn580BFRsV+HSGEorCwkIyMDBYsWMD06dOvWZAC8N133zFw4EAJUsQNQaZ+RI1KLCVYbcpccU3FtGXlUz+6QKUS3a286VtZ+ZylBCpCVO+NN96gXbt2hIaG8sILL1zTe48YMYL169df03sKUV+SURE1qlwA66mrPg1vUTMqSoBSOaNis9kwuEugIkR15s2bd81qRMTNp6E3fryeSUZF1MheSOup80Sn1VV7TNnF8kAlqHzqJ0gJWGwlJVjz8yWjIoQQ4opIoCJq5MyGhJZLlwDQlTch0hoMaMs7b5adPy+BihBCiCsigYqokT24qG1DQnurfJ2vn/qYOv2TKYGKqJ8beDGiEKJcQ/0cS6AiaqS2z68lo2LNzQVA51PRldE+/VN2/jyebkptiwQqwhn2XhKFhdIgUIgbnf3nuD49YiqTYlpRI2d2TrZnVLSminXwuvJ6FUvWJbzcogAJVIRzdDodfn5+6iZxXl5ede4lI4S4vthsNgoLC8nMzMTPzw+drvoaR2dJoCJqVNeGhNbSUmzFxYBjRkVXvvW5JTtbpn6Ey+zbxtuDFSHEjcnPz0/9eb4SEqiIGtVVo2Kf9kGjUQtoAdzKW02XZWVJoCJcptFoCAsLIyQkBHP5hpdCiBuLu7v7FWdS7CRQETWqq0bFkls+7WM0oqm0mZVkVERD0Ol0DfYPnRDixlWvYtrff/+9occhrkN1tc+35pUX0l62vbmuPKNiyaoIVIrLiq/WMIUQQtzE6hWotGnThgEDBrBs2TKKi+UD6GZVVx8VNaPi6+vweHUZlRJLCRar5SqNVAghxM2qXoHK3r176dy5M08//TShoaFMnz6dn376qaHHJhpZncW0NWVU/OwZlSyH+pZiiwS1QgghXFOvQOX2229n4cKFnDlzhs8++4yMjAzuuusuOnXqxNtvv8358p1zxY3N6YyKTw1TP9nZ6LV6NCjLS6VORQghhKuuqOGbm5sbY8aM4f/+7/94/fXXOXr0KHPmzCEyMpJHH32UjIyMhhqnaAT2jEpNq34sakbFx+Fx+9SPraQEiosrmr6ZJVARQgjhmisKVH7++Wf+/Oc/ExYWxttvv82cOXM4duwYmzdv5syZM8TGxjbUOEUjsGdUal6eXN4+/7KMitbbC015J8LKdSqVd2MWQgghnFGv5clvv/028fHxHD58mOHDh/P5558zfPhwtOVLVFu1akVCQgJRUVENOVZxjdXVmdaeUdEaHQMVjUaDzs+PsvPnpZeKEEKIK1KvQGXRokX88Y9/ZNKkSYSFhVV7TEhICJ9++ukVDU40rrr6qFjz8gHQmoxVntP5+1N2/rzjEmUpphVCCOGiegUqmzdvpkWLFmoGxc5ms5Genk6LFi3Q6/VMnDixQQYpGoc9A+Lt7l3t89aCAgC03t5knS2gpLCMZq181IwKKFM/9oyM1KgIIYRwVb1qVFq3bs2FCxeqPH7p0iVatWp1xYMS14cCsxKI1Lg8uTxQseg8+b8FP7PyjT38uv00ULnpm0z9CCGEqL96BSo2m63ax/Pz8/H09LyiAYnrh33qp66Mypn0EszFSjO3A9vsgYofIG30hRBCXBmXpn6efvppQCmWfPHFF/HyqvhN22Kx8OOPP3L77bc36ABF4zBbzZRaS4FaalTKA5WLF6zqY5fOFJCfVVIx9ZOVVbE8WQIVIYQQLnIpUPnll18AJaNy4MAB9Hq9+pxer6dLly7MmTOnYUcoGoU9mwK1rPopLA9UsqwOj184lYdPpaZvBjclIyOBihBCCFe5FKh89913AEyePJmFCxfi4+NTxxniRmUPKty17rjr3Ks9xlqgBDO5eRowQIuOAZz89RIXT+fjrxbTZmFwC3K4phBCCOGsetWoxMfHS5Byk1MLaWuY9rFZrdgKlUClTOeJh7cb4dF+gDL9Yy+mLZMaFSGEEFfA6YzKmDFjSEhIwMfHhzFjxtR67KpVq654YKJxqYW0bjUU0hZWTA1ZdB74BRrwDVaCmtwLxei6+SnPZUmgIoQQov6cDlR8fX3RaDTq/4ubW10bEtoLaW1aLVatO6ZAT3yClKLZ3AtF6PxDAFn1I4QQ4so4HajEx8dX+//i5uRsDxWb3gAaDaYAT3yCyvf0yS3F5q1MDdqKivC0KX/NJFARQgjhqnrVqBQVFVFYKfV/4sQJ3nnnHf7zn/802MBE43I2o2J1V4ITbz8PPL3d0XvqAMgv1oGbEqB4lCp9dyRQEUII4ap6BSqxsbF8/vnnAGRnZ9OjRw/eeustYmNjWbRoUYMOUDQOdZ+furrSunkox/koS9W9/ZQ/F+WWoiufItSXKM3gJFARQgjhqnoFKnv37qVPnz4AfP3114SGhnLixAk+//xz3n333QYdoGgcdW5IWB6olGnLAxVfffl/lT8X5FQEKh5FZYAEKkIIIVxXr0ClsLAQk8kEwH/+8x/GjBmDVqvlD3/4AydOnGjQAYrGYZ/6qat9vpnyAEXNqCj/LcgpqcioFJoBKC6T3ZOFEEK4pl6BSps2bVizZg3p6el8++23DB48GIDMzEzpr3KTcLaY1qxRMijePuX/Lc+oFGZXmvopUFrxS0ZFCCGEq+oVqLz44ovMmTOHqKgo7rzzTnr27Ako2ZU77rijQQcoGkedxbRqszcPNFoNHt5K4ay3OvVTkVHxyFMyKRKoCCGEcJVLLfTtxo4dy1133UVGRgZdunRRHx80aBD33Xdfgw1ONJ66i2mV5y06Dzy83NQeO/Zi2oKcEnR+SqDinlcMAUqgYrPZ1GOFEEKIutQrUAEIDQ0lNDTU4bEePXpc8YDE9aHOYtoiJTti0enx9K7YC8heVFuQXYKuuR8A+uxCCACLzYLZakav01e5nhBCCFGdegUqBQUFLFiwgK1bt5KZmYnV6rh77u+//94ggxONp85i2iLleavWA0/vir9Gao1KTina9kpGxS0rX32+qKxIAhUhhBBOq1egMnXqVLZt28aECRMICwuTVP5NqK5iWluljIpHpYyKd3lGpcxsxeZlBECTnYub1o0yaxlFZUX4esgWDEIIIZxTr0Bl48aNrF+/nt69ezf0eMR1ou5i2opAxVgpUHHT6/DwcqOksIxSnZKNseTkYHAzkFeaJwW1QgghXFKvVT/+/v4EBAQ09FjEdaTOGpViZSWPRavH08vd4TlPo/Jnc/lmhPZABWTljxBCCNfUK1B55ZVXePHFFx32+xE3lzpX/RRVWvXj7ZiY8zIp0z8lGuVcS06Oeh0JVIQQQriiXlM/b731FseOHaNZs2ZERUXh7u74G/XevXsbZHCicdhstjqLaW3lUz/Wy1b9QEVGpQRPdJTvoKwr3wNIAhUhhBAuqFegMnr06AYehrielFpLsdiUjQRrzqjYa1Q8qgQqhvKMSlGZO0atFqxWPHEvf0wCFSGEEM6rV6Dy0ksvNfQ4xHXEvuIHUGtLLqcGKlp9lakfQ3lGpajAgq+PD5bsbAw25RgJVIQQQriiXjUqANnZ2SxevJgXXniBS5cuAcqUz+nTpxtscKJx2OtTDG4GdFpdtcc4lVHJq7SDskW5TpFZAhUhhBDOq1dGZf/+/dx99934+vpy/Phxpk2bRkBAAKtWreLkyZN8/vnnDT1OcQ3ZMyo1ZVMAbIX2YtqqNSoGU3lGJb8UnZ8fnDiBh0XptVNskR2UhRBCOK9eGZWnn36aSZMmkZqaiqenp/r48OHD2b59e4MNTjQO+/RMjYW0ZWXYzGaghkDFaM+omNGW7/fjqWygrBbpCiGEEM6oV6Cye/dupk+fXuXx5s2bc/bs2SselGhcdS9Nrpi+sek8cPd0nB7yVDMq5oqpn1JlmwWpURFCCOGKegUqHh4e5ObmVnn8yJEjBAcHX/GgROMqKFOmfmrc56d8abINDTovzypbKNgzKsX5ZnQ+SqCiLy4PVKRGRQghhAvqFaiMGjWK+fPnYy5P/2s0Gk6ePMlzzz3H/fff36ADFNeeWkzrXn2Niq1Ssze9oWqZk71GxWa1YfMyAeBRVAZIRkUIIYRr6hWovPXWW+Tn5xMcHExRURH9+vWjTZs2mEwmXn311YYeo7jG1H1+6uyhosfds2qgonPTqgGMxVPJyugLlaBWAhUhhBCuqNeqH19fXzZv3kxSUhLJycnk5+cTExPD3Xff3dDjE43AvuqnxqmfSj1U9J7VL182GN0pLSqjzF3ZQVlfUAJIoCKEEMI1LgcqVquVhIQEVq1axfHjx9FoNLRq1YrQ0FBsNluVegVx46mzmLawoodKjYGKyZ2c80WYdcr0kT5PWZYsy5OFEEK4wqWpH5vNxqhRo5g6dSqnT5/mtttuo2PHjpw4cYJJkyZx3333Xa1ximuorn1+7BsSWnV69NVM/QB4lhfUlmiVYEefowQ39iBICCGEcIZLGZWEhAS2b9/O1q1bGTBggMNz//3vfxk9ejSff/45jz76aIMOUlxbakbFvfqMiq1SV9rLlybbeZUX1BbjiTfgnqNcU6Z+hBBCuMKljMqXX37J//zP/1QJUgAGDhzI888/z/LlyxtscKJx2DMqNe7zU1i5RqWGjIq9jb5NuYZHrnKOBCpCCCFc4VKgsn//foYOHVrj88OGDSM5OfmKByUal9PFtDp9jRkV+8aEhWY30GjwUBb9SGdaIYQQLnEpULl06RLNmjWr8flmzZqRlZV1xYMSjavuzrSV+qjUkFGxb0xYXGhF6+OjttCXhm9CCCFc4VKgYrFYcHOruaxFp9NRVlZ2xYMSjUvto1JnjUrty5NB2e9H5+uLZ3lGpdRaSplV/o4IIYRwjkvFtDabjUmTJuHh4VHt8yUlJQ0yKNG47BmVulroW7Ue1TZ8A/A0VtpB2dcXz1MVzxWVFWHSmxpwxEIIIW5WLmVUJk6cSEhICL6+vtV+hYSE1HvFz4IFC9BoNMyePbte54uG40pn2pr7qFTa78fXFzcr6Mr/uskSZSGEEM5yKaMSHx9/VQaxe/duPvroIzp37nxVri9cYy+mrWnqp6JGpfoW+lCRUbFabGBUsicGmzv5mhJZ+SOEEMJp9drrpyHl5+cTFxfHJ598gr+/f2MPp8mz2qxqIFFTRsXmRGdad70ON73y18tmUAIVT5tyrKz8EUII4axGD1RmzJjBiBEjnNonqKSkhNzcXIcv0bCKyypa3NecUam7jwqAobw7rdVT2e/HYFECFcmoCCGEcFa9NiVsKF999RV79+5l9+7dTh3/2muv8fLLL1/lUTVt9mkfrUaLp86z2mOsTnSmBWW/n7xLxZTplaJcDzPgLjUqQgghnNdoGZX09HSefPJJli9fjqdn9R+Il3vhhRfIyclRv9LT06/yKJueyoW0NW0waSmoe68fqKhTKStfPWTvpSJTP0IIIZzVaBmVPXv2kJmZSUxMjPqYxWJh+/btvPfee5SUlKDTOf627uHhUePSaNEw6iqkBcdNCe11KNUxqBsTGvAAPEqsgEz9CCGEcF6jBSqDBg3iwIEDDo9NnjyZdu3a8dxzz1UJUsS1UVdXWqjoo6LxqjnrAuBZvjFhicYLD0BfXOZwDyGEEKIujRaomEwmOnXq5PCYt7c3gYGBVR4X105dXWkBbMVKoKL1qn7TQju1O63NAx/As1AJVCSjIoQQwlmNvupHXF/q6kprs1qhvAOxzrv6Y+zsUz+FVqUGSV+o9NGXGhUhhBDOatRVP5dLTExs7CE0eXV1pbXv8wOg8649o2Ivpi0oVQIWdWNCyagIIYRwkmRUhAO1RqWmHirFFX1W3I3OTf0UF1rQGo14mG0O9xBCCCHqIoGKcKCu+qlrnx+tO3qDvtZr2ff7KSrf70cyKkIIIVx1XU39iMZXVzGttdC+z0/tzd6gYurHXGxB6+OLp/kMAAXmQjYfOseB0zm0DvZm+G1huOskZhZCCFGVBCrCgT2jUmMxrZPt8wE8DG5otBpsVmVjQs8CsFnd2LW3C+su/qwe98F3x4if3J1wv9qnkoQQQjQ98muscFDXhoQV7fP1dWZUNFqNmlWxefvgYYaScyO4cDEMDzctI7uEE+Ct5/C5PB7+ZBc5ReYGfCVCCCFuBhKoCAd1LU+2N3uz6jzqzKhARUGt1cPIBV1zzNk9AfhoQlf++fAdrHviLiL8DZy4WMhfVh+o7VJCCCGaIAlUhAP71I/BrfppGHv7fItOj76OjApUBCoWD2/+20zZIdsn4Df6tw0BINzPwAdxMei0Gtbtz2Bn6oUrfg1CCCFuHhKoCAd1FdNW1Kh44O5ERsWzvOnbGTcffvXrAICx2U6HYzpH+DHhDy0BeOPb37DZbPUbvBBCiJuOBCrCgT1QqWvqx9WMylZNIDaNFp3X75h1p6oc98TANhjcdew/lcN2yaoIIYQoJ4GKcFDXpoSVi2mdqVGxb0yYZFUCHzffvRRbirFYLQ7HBRo9eLhHCwASktLqN3ghhBA3HQlUhIM6i2mLnO+jAsp+P/kaG8dtbmhsVtyMKQAUW4qrHDuhpzL9k3jkPOmXpHutEEIICVTEZerqTGuvUbE60UcFlKmfNHclexKddQqdTrl+dd1pWwV506t1IDYbrE0+U6/xCyGEuLlIoCJUZquZUqvS576mYlqLWqPiXEbF0+TOCTcrAN0yf8OjtPb9fu7tHA7AhgMZrg1eCCHETUkCFaGqnOWoKaNiyVcyIhadHr3BiRoVb3dOlQcqt134Hc/ynm72ot3LDenYDJ1Ww69ncjlxscCV4QshhLgJSaAiVPYsh7vWHXede7XHWAqUY6w6PW7udf/1ybZayNPa0NqgQ8l5DKWO97pcoNGDnrcEArBesipCCNHkSaAiVPml+QAY3Y01HmMPVDQGAxqNps5rHjifB0AziwaDtxeeJeX3MufXeM7w28IAmf4RQgghgYqoxB481LTiByr6qGg8ndtA8EBGLgBhFi0aowmv8hoVe9FudQZ3bAbAwdO5nM8rceo+Qgghbk4SqAhVXUuToaKPitar+hqWyx08nQNAszItNm8fDOVxR22BSpDRg47hPgB8f0yavwkhRFMmgYpQOZVRKQ9UNIa6MyoWq41fzygZlVCLFpuXD57lNSq1BSoAd7UJAiDpqAQqQgjRlEmgIlT24MGor7lGhWIlUNF51x2opF0ooLDUgl6jIcCqweJpwqs8UKmtRgWgd3mgsjP1guz9I4QQTZgEKkJlD1S83WrOqNhKlI6ybt41H2N35JxSSNvcQ48WDWa9US2mrSuj0j0qAL1Oy5mcYo5flC61QgjRVEmgIlTq1I+++iDEZrNBeaCiM9Zdo5J6TrlepNEDALObt1PFtAAGvY6Yln4A7JTpHyGEaLIkUBEqdeqnhuXJttJSNDaleZu7qZbpoXKpmUpGJcpXmSYq1nqpxbT2pdC16d1amf7Z9fvFOo8VQghxc5JARajUfX5qaJ9vLayYgnHzqTujcjRTCUZaBylBTTFeasO3grK6u852jfIH4JcTWXUeK4QQ4uYkgYpQ2ad+asqoWMubvVm07ui9PGq9VpnFyu/nlWDk1lDleoW2ioxKQWndgUqXCD+0GjiTU0xGTtVNDIUQQtz8JFARqjqnforKAxWdB/o6NiRMzyqi1GLF011Ly2bK9fLLPNSMSl2rfgC8PdxoH6b0U9l7ItuZlyCEEOImI4GKUDk79aPsnFz7hoSp5St+Wgcb8TYp2Zf8Ug8M9mJaJ2pUAGJaKNM/e0/K9I8QQjRFEqgIVV0ZlcqBSl0ZldTy+pToECMGk7LBodmqw0ujFNY6HaiUr/yRQEUIIZomCVSESu2jUkNnWjVQ0erR15FRsRfSRjcz4e6hQ+umbGDo7alM5RSUFTrVyM2eUfn1dC7FZosTr0IIIcTNRAIVoao7UFEKWpWpn9ozKuqKn2AjGo0Gg1EPgMHDV7kGVkosdW842CLAiyCjnlKLVW3HL4QQoumQQEWo7L1NnJv6qTmjYrPZSLugBD1tQpSgxz7946H3q7ifEwW1Go2GzhHKOQdOZdd5vBBCiJuLBCoCALPFTKlVWZJTU2daa6ESfNSVUblUUEp+SRkaDUT4K4W5nt5KoILBB0OJMuXjTNM3gE7NlSzMQcmoCCFEkyOBigAcW9p7uVW/6secqwQWFp0evaHmjMqJS0rmJdTHE093JaAxmJSpnzJPE15KF36nMioAncKVupaDp3OcOl4IIcTNQwIVAVQEDQY3A27a6oOQsrzyjIqbB27uNf/VSS8PVCIDKgIeg7F85Y+7Ee/y0pTcEucyJLdFKBmV1Mx8KagVQogmRgIVAdRdSAtgyS9voe9hQKPR1HjcifLdjltWClQ8ywOVUp0X3uUZlVyzc4FKqI8nQUY9FquNQxky/SOEEE2JBCoCcDZQKZ8e8jDUei01UAmslFEpn/op1njjXazUqDibUdFoNHQMV7Iqv8r0jxBCNCkSqAigYurHmUBFY6g9UKlt6qfI5lmRUSl1PjtyW3lB7QEJVIQQokmpvWuXaDIKzUpwUWugUr48WWOofefkE5eUgKZlYMW17BmVfIsB7/L9fvJK85weX6fmSkHtgdMy9SOEEE2JZFQE4FxGxVakNHzTetUcqBSbLZzLVaplK9eoePmUBypmD4z2qZ9i57Mj9qmf1HN5lJZZnT5PCCHEjU0CFQHUvc8PVOyerPOuOVCxT/uYPNzw83JXH7c3fCu0GvAuUQpxcwsuOj2+CH8DJg83yqw2jp13blmzEEKIG58EKgJwrpjWVlyeUTHWfIy9kLZFoJfDyiC9wU3Z70ejxahVAp2cQuc3GtRoNLQLMwHw21mZ/hFCiKZCAhUBODf1Q3mg4lZLRsXe7K3yih9QAg2v8joVo04JOHKLs10aY7tQpU7ltwzna1uEEELc2CRQEYBzUz+UKst13HxqPkZd8eNfNZixF9R6uykBR56TnWnt2ocp56WclUBFCCGaCglUBFARqHi5V58tsVmtaEqVIlm3WqZ+TmcrWZcI/6pLmO2BiqebUhibZy10aYzq1I80fRNCiCZDAhUBVEz91JRRsRUXo0FZraP3qzmjcqY8UGleTaDi5aMU1Hrq/AHIsxVjs9mcHmPbZkqgkplXwsX8EqfPE0IIceOSQEUAUFBa+9SPtbAi++FmrLlGxZ5RCferOaPijhKoWDU2Csucz6p4e7iptS+/yfSPEEI0CRKoCAAKymqf+rEHKmVaPR7e+uqvUVJGdqEZgObVBCr2XipWnS/uZiWTkl2S7dI424UqWZUUmf4RQogmQQIVATiRUSlv9mbReaD30FV7jH3ax+TphsnTvcrz9oxKEUZ8lEPJKnZ+iTJUFNRKRkUIIZoGCVQEUGl5sr76QllrgZJRseg80Buq33nhlL0+pZpsCqAuTy60eeFTPuNzqfiSS+NUlyhLLxUhhGgSJFAR2Gy2ir1+3GoIVAorAhX3OjIq1a34ATD4VOz341OoTP24Gqi0L1/5c+RcPmUWaaUvhBA3OwlUBEVlRZTZygAw6U3VHmMtUKaGLDoP3D2rD1ROZ9VcSAsVbfRzzZ6Y7BmV3EyXxhrp74W3XkdpmZXjFwtcOlcIIcSNRwIVoU776DQ6DG7VBxnmvIpApaapnzN1TP0YjO6gAavWA99SJdi5lHPGpbFqtRraqgW1UqcihBA3OwlUBPml5T1U9EaH/XkqM+cqx1jcPHBzr/6vTW1LkwG0Oi2e3kpWxcemrC66mOdaRgWgnb1Draz8EUKIm54EKoLcUuUD3+Re/bQPQFl5oIK7Z43BjH3qp7pmb3b2Jco+KLUwWUXO76Bs1z7UvjmhZFSEEOJmJ4GKUKd+aqpPgYqpH5uHZ7XPl1msnM1V9gKKqCGjAhV1KiaUNvpZJTkuj9eeUZFW+kIIcfOTQEWQV6pkJoz6mlvjW/KV6leNZ/VByNncYqw20Ou0BBk9aryOuoOyVglUsi2uZ0XsNSpncorJKW8wJ4QQ4uYkgYpQA5Xapn4s+UpGRWOovnPtmWwlmxLm54lWW/3UEFQsUTbgB0A2rm1MCODj6a4ugU6RfipCCHFTk0BFOJVRsS9PrilQOZ2tBBzhvjVP+0BFd1oPWyAAhdoySiyubzCoNn6T6R8hhLipSaAi1BoVH71PjcfYG75pvaoPROwZldoKaaGimFZDIO5lStO384XnXRsw0CFMligLIURTIIGKcC6jUqhkVHTG6o85lVV7DxU7db8fjYmA8hgjs7D+S5Sllb4QQtzcJFARTtWoYM+omKoPVE7X0ezNzl5Mm2fxvqJAxb454eFzeVisNpfPF0IIcWOQQEVUBCq1LE+mWMmouPlUf4zalbaOqR+1jX6ZN/75SoBxNivdpfECtAjwwuCuo9hsJe2CtNIXQoiblQQqwqk+KppiJaPi5lu1jsVms9W5z4+dfdWP2eZGYIny/+cunXB5zLpKrfRl+kcIIW5eEqgIp2pUNKVKIOLuVzWYyS40U2S2ABDmW31DODt3vU7dfTnQptzvXK5r+/3YtZdW+kIIcdOTQEXUOfVjM5vRlpUCoPevmlGx16cEmzzwdK9+Z+XK7FmVAI3S9O18keurfgDay8ofIYS46UmgIiqmfmooprX3UIHqA5VTTk772NkLav21Si+VTHOW84OtpL200hdCiJteowYqr732Gt27d8dkMhESEsLo0aM5fPhwYw6pybFYLRSYlUCkpqkfe1dai9YdT1PVqR17IW1te/xU5u1bvjGhNhiAC+Rjs7m+cqdyK/3swlKXzxdCCHH9a9RAZdu2bcyYMYNdu3axefNmzGYzgwcPpqBAVnFcK/ZsCtSWUVGOKdN5ojdUndqxT/2E+9Ven2Ln5afsBeStaYbGaqNUY+Fiseu7KFdupS87KQshxM3JrTFvvmnTJoc/JyQkEBISwp49e+jbt28jjappsdeneOo8cde5V3tMWa5yjMXNgN6z6l+ZM072ULGzZ1TKdP4E5sEFXziVd4ogQ5DL428X6sOprCJSMnL5wy2BLp8vhBDi+nZd1ajk5OQAEBAQUO3zJSUl5ObmOnyJK+PM0uTSS8r7XKbzrDZQUZu9+Ve/D9DlvMszKgV40yxbmfI5nX/a+UFXUtFKX/4uCCHEzei6CVSsViuzZ8+md+/edOrUqdpjXnvtNXx9fdWvyMjIazzKm48zS5NLs5UgwOLuic696l+Zih4qzk39ePsqgUqe1UhwtvLYqbxTzg7ZQYdwpaD21zMSqAghxM3ouglUZsyYwcGDB/nqq69qPOaFF14gJydH/UpPd72jqXDkTFdac5YSBNj0Vad2ikotXCxQClkjnM2olAcqOSUGNaOSfinN+UFX0jFcWeJ85FweJWWWel1DCCHE9atRa1TsZs6cybp169i+fTsRERE1Hufh4YGHh8c1HNnNz5l9fspylGNsnlUDkdPZSsdak4cbvobqa1wu5+1XvjFhiZbQEgNQyqls17vTAkT4G/D3cier0Mzhs3l0jvCr13WEEEJcnxo1o2Kz2Zg5cyarV6/mv//9L61atWrM4TRJzmRU7MW0VBOoqLsm17HHT2V6gxtu5VNIofgr1ymsX42KRqOhU3Mlq7L/VE69riGEEOL61aiByowZM1i2bBlffPEFJpOJs2fPcvbsWYqKihpzWE1KbqkyrePr4VvjMRZ70bJX1ToWe6AS4UKgotFo1CXKoTqll0pmWTbFZcVOX6Oy28oDlYOnJVARQoibTaMGKosWLSInJ4f+/fsTFhamfq1YsaIxh9Wk5JQoH+4++qodZ+0seeUZFe+qWZfTLi5NtrMvUTZ6hmEqtGHDxvHc4y5dw65zhBKoHJBARQghbjqNWqNSn26komHllCof7rVlVGzlgYrOVDVQqcioOFdIa2cvqC0zBtL8IvzmBceyj9EuoJ1L1wHUqR97Qa2HW937DQkhhLgxXDerfkTjcCajYitQAhVtNYHK6SylmNaVGhWoCFSKDYE0v6AErL/n/O7SNeya+ykFtWaLjcPSoVYIIW4qEqg0cbklddeoUKg0hdP5Vj3GPvXjSo0KgFf5yp9CN1+aX1QClbSc+i1R1mg03Fa+2ic5Pbte1xBCCHF9ui6WJ4vG48zUjz1QcbssUCkps3AutwSoT41KedM3jS8R5dv8/J5dv4wKQEwLP7YfOc+eE1lM6BlV7+tcdRYznDsIZ/bBxaNw6XcoOA9FWWAuLybW6sArALwCwT8KAlpDs47QPAY8al6dJYQQNyMJVJo4+9SPr77mQEVbomwS6R7gOD2Uka18sHq6awnw1rt0X3sb/RyzkRbnlYzK8dzjFJcV4+nmXIfbyrq2VJY57zmZ5fK5V92FVDiyCVI3Q/qP4Mzqpmr7ymigWSdoMxDa3A0teoFOfoSFEDc3+VeuCbParHUuT7aZzWjNStZEH+jv8FzlQlqNRuPSvY3+5RmVXCvB+gB8C3LI8bZwJOsInYM7u3QtgNsj/dBoIP1SEZm5xYT4uB7sNKiCi3BwJSR/CWf2Oj7n6adkR4LbQcAtYApTMihunqDRKFmXwktQkAmX0uDCEchIhpx0OHdA+UpaCF5B0GEU3P6Icj0XvwdCCHEjkEClCSswF2C1WQHw8ai+mFZdmgx4BDoGM/autK5O+wCY/D1BA2VmK27Nwmh1Npt9rTWkXEypV6Bi8nSnbTMTv53NY+/JLIZ2CnP5GlfMZoPfv4OfFkPqt2AtUx7XukGrfhA9GFoPgKBb6xdU5J2FtB1wbCsc+RYKL8DPnylfYbdD96nQ6X7Qu7YCSwghrmcSqDRh9mkfg5sBD131WxNYyne0LtN54mlyPKY+zd7sdO5avH30FOSUYgsIodXZFPa1hpRLKS5fy65blD+/nc3j5+PXOFApK1WyJz+8p9Sf2IXdDl0eVoIHY/CV38cUCp0fUL4sZkjbDvv/Bb+uhox9sHYm/Od/IWYC9JypHC+EEDc4CVSaMHshbW3t863lGZUyNwMeXo57+ZyuR/v8ykyBnhTklGIxBdHqd6VO5dDFQ/W6Fih1Kst2nbx2dSqlBbD7U9j1AeRlKI+5eyuBQsxEaNbh6t1b5w5tBilfQ/4OvyyFnz+F7JPw/T/hp0+g2xTo/SSYml29cQghxFUmgUoTphbS1rLipzQrGwCzmxceXo5/XU7VsyutnSnAk7O/51LiGcAtZ5VAJTU7lVJLKXqda8W5AF1bBABKK/2iUgsG/VVq/FZaqAQFO99Rpl8AjKFw53ToNhkM/rWe3uC8A+Gu2dDrCaVgd8dbcOon2PW+Mi3UfQr0nt0wWR0hhLjGpI9KE6b2UKllxU/J+WwAyty98DA4Biqn69mV1s4UqAQ4hXp/gnPAWKqlzFpGanZqva4XGWAgzNcTs8XG7uOX6nWNWpmLYNciWNhFmWIpvAD+rWDUezB7P/R5+toHKZVpddB2KEz5DzyyEpp3g7IiZUrq3Ttgx9sVS6CFEOIGIYFKE+ZURuW8Mo1i9fBGo60oAC2zWDmbq3zo1adGBZSpH1B6qWiA1pnKX8f95/fX63oajYa72gQBkHT0Qr2uUa2yEvjxY+XDftPzymocvxYQ+z7M3K1M9bhVX+PTKDQaZfny1C1KwBJ2O5TmwdaX4b3uSj2NbF8hhLhBSKDShDnT7M18QenGZvVyrGPJyCnGYrWh12kJNtbvQ9oUoAQq2WZvANr+riyD3ntub43n1OWuaCVQ2ZHaAIFKWalSg/LuHbBxrlKH4hsJIxfCzD1wxyNKrcj1yh6wTPsO7vsITOGQcxK+/iN8NkRZ8iyEENc5qVFpwpxp9mYur1GxeV++NFmZ9gn380SrrV//DntGJTvfHY2XFx1OKsud95zbg81mc7k3C0Cv1kqgcigjl4v5JQTWJ4gyF8O+ZUoNSk56+WDDoe8zcMd1lj1xhlYLXcZB+5Hw/XuQ9I7SeO7j/tB9Ggz8C3jW0plYCCEakWRUmjB7s7eaeqgAWLKUqR+NyfGDLP1S/TYjrMyeUSktseLePII2GeCGjvNF5zmZd7Je1ww2edAuVMn+JB276NrJpYXwwwfw7u2w/hklSDGGwrD/B7N+UfqU3GhBSmV6b+j/HDyxR1kybbPCTx8p00H7/0+mg4QQ1yUJVJowZ3ZOtuZkA6D19XN4/GR5oNIy0Lve93f30OFpLJ86adYcfRl0QOl/8sOZH+p93T726Z8j5507oSRPyZ4s7AzfvqBM8fg0VwKUJ/fBnY+BeyN3um1IPuEw9jOYsAYC20D+OVg1FZaMhPOHG3t0QgjhQAKVJsyZYlpbbjYAWj8/h8dPXCwPVAKurAuqPati8Veak3XPVpYY7zi9o97X7HdrCAD//S0Ti7WWLEF+JiQugHdugy0vKZsD+rVUalBm/VIeoNQ/Y3Tdaz0A/vQ9DPyr0r7/+A5Y1Bu2zFN6xAghxHVAApUmTJ36qSWjQr5yjJu/47LbExeVD7IryagA+AQpgUqxtxJc3HFcqUv5KeMnip3ZvK8ad94SgK/BnYsFpew5UU3zt4xkWP0n+EdHSHxN2bk4sA2MXqRMi3SddGNP8bjCzQP6zoEZP8Ktw8Bqhp3/gPfvhJR1Mh0khGh0Eqg0YZeKlV4jAZ4BNR6jKVQCFfdAx2NOqFM/V5ZR8WumnJ/nply/ecoFQr1DKbYUk3Q6qV7XdNdpGdReCXw2HTyrPFhaCMkr4LNh8FFfSP4CLKVKr5H7P4UZP8Ht46/vVTxXk38UjP8Kxn0Jvi2U+pwVcbD8Abh4rLFHJ4RowiRQaaKsNivZJdkA+HtW36TMWlqKtlTJauiDKwKVnEIz2YVmAFpc4dSPf3mgkmX1A8B8+gyDW9wDwPq09fW+7pCOylTSt8knsK19Et5qC6sfg5PfK5sEdhoLU7fCtK1w21ilWZqAdsOV7EqfZ0DrDkc3wwd/gK3zZTpICNEoJFBponJKctSdk/09qg9ULOVLk61o8QyuOObEJeUDK9jkgbfHla1w92umTB2dz/dE4+4OZjNDTXcCsC19m1pH4zSbDTL20/f0YgyaUk7nWzn48zYoyVWatPV/AWYfgLGfQkS3Kxr7TUvvBYNehD/vgtaDlMzTjrfgvR5w6BuZDhJCXFMSqDRRWcVK7YZJb8K9hukOS7ZyTJm7NwZTxd47DVVIC+AXqlyjMLcMt4gIAKIu6rjV/1ZKraWsSl1V90UsZjjxPWx+UWnO9lEfDN//PwZofgHgm4A/wsR/w6xk6P+8supF1C2ojdLZ9qHlynRQ7in416Ow9D44f6SxRyeEaCIkUGmi7PUpgZ6BNR5juaj0ISl1N2IwVQQzDbE02c7D4IbBpzwICmkOQNmpUzzS/hEAlqcsx2w1Vz0xOx32JMCKR+CNWyB+GCQthKw0cDNA+5Hc1+d2ANbkd6CsxV1K4zPhGo0G2t+rTAf1ew50HvD7d7CoJ2x4Fgpc7FUjhBAukn+5myh7oFJTfQpA6dlM5b8evhiMFRmV4xfsK36uPKMCFXUqZl+lrqT0xElG3DKCQM9AzhWe4z/H/6N0iz32X/j2L8qKlHc6wb+fhJR/K9M6hgCl7uSBJfDsMXhoGf3uGU2At54L+SXsaMi9f5oivRcM+B+Ysat8dVCZ0izu3duVHjSy2aEQ4iqRFvpNlH3qp6b6FIDiM+cAKNWb8PCq+KvSUCt+7ALCvDmTmk2hRyBeQOmJE+i17kyPuJvjexbTbM1MbIVFaMqKKk7SaCGiu7KXTZtBysZ7lxXE6t20jOoSTsL3x1m55xQD2oY0yHibtIBblNVBvycqO0ifPaD0oNn9Kdz9EnQcI5krIUSDkkClibpUUr402VDz0uSS8oyK1ejvsHPyyYsNN/UDEBRpBCDL5kdgYCkm61Z493YezjrueKApTCnujL4bbukPhpqDLLv7YyJI+P44/zl0juzCUvy89HWeI5xwS394bDvsX6GsCMo5CSunwA/vw+C/QVTvxh6hEOImIYFKE3WpqHzqp5aMijlTaUFv9akIZgpLyzibq6T5G6KYFiDUdIY/GJfR1rAN4z3lUzRZgNad04Et+arsAr8H38LCh/6Dm4t9Tjo196FDmA+HMnJZufc0U+5q1SBjFiiZk9sfhg6xsOsDpVHcmb2QMBxuHQqDXoJmHRp7lEKIG5zkaJuorBJl6qe2Zm+WC0qhpNav4phjmUp9SqC3Hn/vK8hOlJUqG+F9MojANXfT1bgSo+4CljIN2WkGSvu8Cc8dx/+xHawNjWK7+QL//n2dy7fRaDTE/aEFAMt/PIFNltY2PL2X0t121i/QbQpodHBkE3zYG9bMgJzTjT1CIcQNTAKVJspeo1JboGLNUgIVXUCQ+tjR83kAtA4x1u/G5mL46ROlCHPVVDj9M2jdOUVPvs2ew7EDA8n40Z+ionDwMOLl7sUfO/0RgA+TP8RsqWYFUB1ib2+Ot17H7+cL2PX7pfqNW9TNGAL3vq10+W0/Stmded8y+GcMbH4JirIbe4RCiBuQBCpN1IUiZYqlthoVW47yoa4LCVYfO5qZD0AbVwMVqxWSv1I+tDbMgdzTYGwGA/4CTx/it6i3OVrcmyJfJftRcjRVPfXBtg8SbAjmTMEZ5/qqXMbo4UbsHcrS5+U/nnD5fOGioDbw0FKYsgVa9IKyYkh6RwlOv38Pykoae4RCiBuIBCpN1Pkipf4kxFD9ShhraSnaQiV7om9WNVCJdiVQOfMLLB4Eq6crAYpPcxj+Jjy5H/o9C8YQwqP9ALikVe5VeqxifxmDm4FpnacB8NH+j+q1WeH4HkoA9O2vZzmfJx+U10Rkd5i8AR5eAcHtlc0f//MX+Gc32P8v6XArhHCKBCpNUHFZMXmlShAS7BVc7TFlmcqKH4vW3aF9vksZFXORkvL/ZJBSZKk3KgWWT+yFHtPA3VM91B6onCtR7lWSetThUmOjx9Lc2JzzRef56revnHuhlXRq7svtkX6YLTb+b0+6y+eLetJooO1Q+FMSjHoPTOHKCqFV0+DTwXB6T2OPUAhxnZNApQmyZ1M8dZ4Y3asPOMxnzgBQ4uGvdo4tLbNyvHxpcp2ByrlD8HF/JeVvsyj9NZ7YC32edghQ7HyDDXj7eZDnWd707eRJrCUVmQ93nTt/6vInABYfXEx+ab7Tr9cu7k4lq/LFjyexWuW3+WtKq4OYCfDEHmUfIXdvOPUTfDJQKbjNO9fYIxRCXKckUGmCzhcqgUqwVzAajabaY8oyMgAo9vDH29cDgBMXC7BYbRg93Aj1qRpsAEo6f08CfDIAzv8G3iEw7gt4IB5MzWock0ajofmtfpTqfbF6eoPVSmlamsMx995yL7f43kJOSQ6fH/rcxVcN93YOx8fTjVNZRWxPPe/y+aIB6L2UnZmf2AOdxymP7VsG/+wKuxaB1dK44xNCXHckUGmCMouUaZ1gQ/XTPgCl5RmVYs+KQMU+7dM62Lv6AKesVGlr/+8nlQLKNvfAn76HdiOcGlfrO0JAo6HAS8mqXD79o9PqmHnHTACW/LpEXbnkLINex/1dlY0Pv/jxpEvnigbmEwZjPoIpmyH8DijNg03PKxmWM/sae3RCiOuIBCpN0IVCZcVPTfUpACWn7FM/AXj7XRaoVDftU3ARloyEvUsAjVKLMv5fYKz5Hpdr0SkAvaeOHM8wAIpTUqocc3eLu2kf0J7CskI+O/iZ09e2s0//bP0tk4ycojqOFlddZA+Y+l+49x/g4QsZ+5Rs3Ld/gRLXp/eEEDcfCVSaIGcyKiXpSqBSZgrC3UPZQye1pkLa7HT4bAik71I+bOL+T6lFcXHPFzd3HbfEhJBnigKgeP/+KsdoNBo1q7LyyEoKzYUu3aNNiIk7WwVgsdpYsVuKaq8LWi10+yPM/EmpZbJZ4Yf34IM/QOqWxh6dEKKRSaDSBFWuUalJWYYSqGiCKpYv/3omB4AOYT6VLnZYCVIupoJPBEzdDNH31HtsMYNbkOvbEoDCAwexlZVVOeau5ncRaYokz5zHt8e/dfkecX9Qrv/VT+mUWaz1HqtoYKZQpZYp7mvwawE56bD8flj9JyiURn1CNFUSqDRBaqBSQ0bFZrNhPXcWAF2oMg1TUFLG7xeU9vkdw32VA0/9rAQpuachqC1M+RaC217R2PxDvWk56HbKdB5QUkzmroNVjtFqtIy9dSxAvRrADenYjEBvPWdzi/nvb5lXNF5xFUTfA3/eBXf+CdBA8hfw/p1waG1jj0wI0QgkUGmCTucre6+EG8Orfb4sMxNKi7GiRR+pFJ+mZORis0GojyfBJg84ulWpSSnKguZd4Y+bwDeiQcbX+6F2FAfdAsAPb63j543HsZQ5Zj5GtBqBBg37zu/jbMFZl67v4abjgW6RACyTotrrk94bhi2AP34LQbdCQSb8awL861HIl+BSiKZEApUmxmK1qB/szY3Nqz2m9LjSZr7YMwDvQG8ADp5Wpn06NfeBY9/Blw+DuRBaD4RH14JXza34XaX3dCN8yB8AMGYf58dvfuf/XttN3qWKjrTNvJtxR8gdAPWa/hnfowUaDWw/cp7j5ZkicR1qcSdM36Esadbo4NA38H4PZTsG6WwrRJMggUoTc77oPGW2Mty0bjVO/ZSeOA5AkVeIujT54JlcADp65ShBiqUE2g5X2qN71HODwlqYut0OQHP3sxhM7lw8XcCq/7eHgpyKJnBDooYA8F36dy5fv0WgF/1uVV7/sl2y/891zd1TaRL32HcQepuSxVs9HZY/ADmnGnt0QoirTAKVJuZUnvIPe5h3GDqtrtpjSk8oH9yFhmCM5UuT1YzKobehrAja3A0PJICb/qqM09C5s/I/p49z/+xO+DXzIj+rhI0fHsBSXgDbJ6IPAMmZyeqWAK54tKdSVPt/e05RVCqNxq57YV1g2ncw8K+g08PRzfD+H2D3p8qml0KIm5IEKk3MmQJlNU9N9SlQEagUGULwCTZQbLaQek4JBDpZU6BVX3hoGbh5XLVxuoeG4hYWBhYLuuOHGDGjMx5ebpxLy+WXb5XxRZoiifKJosxWxk8ZP7l8j363hhAZYCCnyMy/k8809EsQV4POHfrOgcd3QkQPpVHc+qeVeqmLx+o+Xwhxw5FApYmxF9LWVJ8CFR1hC7ya4Rts4PChZCw2CCSH0BZt4eGvwN1w1cfq/QelTqXwhx/wC/Gi77hbAdi9/jgXTys9XXo37w3AjtM7XL6+Tqsh7k4lq/L5ruPYpObhxhHcVingHroA3L3gxE5Y1Bu+/6e04RfiJiOBShNzOq/2QMVaVIT5pLISxhrWCvfcY+z55n3+f3v3HR5VlT9+/H2nzySZ9G4SCCV0QksMCoiAiIhiWctmhVV0LeBXRVBc2+73u8/i6rprWVdd/QnrKsa1gChldWlKCxAIJARCqIGQ3iZ92vn9cZOBgYCUhCRwXs9znztz78m9555Mkk9OBRhoKUdJ/bc6IuMS8BmZAkDtxo0A9BoRTrdBIbhdgtUf78HtcnNNlBqobDi+4YICjbuGx2DQacgusJF5tKrN8i5dAhotXP2oukxD9zFqk+T3L8D7o+Hg2o7OnSRJbUQGKleYIza12eQq39aHEjft3w9CYNf7ERrthH9OYXOjOpQ3KeU6MFlb/br24JOSAopCU84eHMXFKIrCmHsTMJh1lBypYdeaYwyPGI5eo6eorsjzbOcjyMfAzYPUuWL+tUl2qu2SgrrDtG/glrfB5A/F2fDxrbDobijd19G5kyTpIslA5QoihOBAtdqO3yOgR6tpmnJzAXCEBDHaPhd3TTFb6A/A1QltM0/KudKFhGBOTASg5r/qVOq+gUZG3q7mPX3pQRxVMDBkIADbS7Zf0H2mpXQD4LtdhZTWNJ09sdQ5KQoMnQb/kwnJj4BGB/tWwrspsGyOHB0kSV2YDFSuIGUNZdTYa9AoGrr5d2s1TWPuPnRmF/2SsjA7i9jnP5IqtwWLQcvAaP9Lm2HAb4I6HX/NyhNzpfS7JorInv447W7WfbbPM59KRnHGBd0jMSaAIbEB2F1uPlx/8OIzLXUcSxBM+pM6s23CTeB2wtYP4M1E+GYmlO3/2UtIktS5yEDlCrK/Sv0lHesXi1Hb+ogde046sWPLsJhrsVtiSE+cD8CwuED02kv/cbFOvAEUhfqtW7EfVRcRVDQK16X2QaNTyN9dTqRNrWG50EAFYNbYngB8sukIlXX2i8+41LFCesG9n8H0b6HbKHA7YMcn8Lfh8O/pkJ8uJ4yTpC5CBipXkIPVam1BvH98q+fdFccID9+A0eqi1hlE3a1fkl6oLgqY3L3tZp49H/roaHxGjgSg6suvPMeDIn0YdmM3ACpWGtGgoaC2gOK64gu6z/V9wugbaaXO7mLBhkMXnW+pk+g+Gn79Hcz4AXpPAgTkLIGPboC/jYD1b0DN+S3BIEnSpSUDlStIS41Kq/1T6spgwWSMVgf2eh3f2P6IX3wv0g+qq9ZeHR98KbPqJeCuuwCoTEvDVXtiuvthE+MIjLDgrtYRhTrM+EL7qSiKwuPXq7UqH64/RFF14898hdSlxCTBL9PUEUKJqeqQ5vI8+O/L8Jd+6iy32z+W6whJUickA5UrSFZpFgB9gvp4n6grh3/egqbmMI56DZnbr8YY3YvsohrK6+z4GnUMuirg0me4md/4cRji4nBXV1P56aee41q9hutS1WcJLIwFLq75Z9KACIbGBlBvd/HKij0Xl2mpcwrvD1P/DnP2qaOEYpJBuCDve1j6OPy5N3w4Adb/FYqy5Iy3ktQJyEDlClHnqCOvKg+AwaGDT5yor4B/3Qolu3G5TOSvCaHIOISIHv6s2qM2o4zpHYpB13EfFUWrJWTmYwCUvfcejsJCz7moXgH0GxVFpE1tzsoouvBARVEUfn/LABQFlmQeZ9OB8ovLuNR5Gf3UUUIzvoeZW2Hs8xCZCAg4tgX++zt471p4rQd8/itIfx+Kc2S/FknqADJQuUJkl2XjFm4ifSIJ9wlXD9qOw4JJUJSFsIRyZG0o9hodFYEJRPUM4PvdaqByfZ+wDsy5yjplCuZhwxANDRS/8ievcyNv60G8UGtW9lfvp6qx6oLvM/Aqf+5NUmtnnvx8B2W1crjyZS+0N4x5Bh5eB0/lwOTXoecE0PtAQwXs+RZWPKMOdX6tp9oZd/N7ULAdXI6Ozr0kXfZkoHKF2Fm6EzipNqVsP/y/iVC6F/yiaBj6R5pKXdj1fjT6X0VDkJ7c4hr0WoXxfcM7MOcqRVGIeOlF0Gqp+c9/sJ00XNlo0TNm0mAC6tV8bj2+7aLu9fxNfekZ5kuxrYkn0nbQ5JRTsl8x/KNhxIPwqy9h3hG1E+71L0L8WNCZob5M7Yy78ln4YCy8EgsLb4ZV/wv7/qPWUEqS1KZkoHKF2FCwAYCh4UPheCZ8NBGq8yG4J8z4D7Z0taNteVA/YgcEs+KkZh9/i76jsu3FlJBA8IwZABS+8AL2Yycm8eo7MpI4ewIAP+z48aLu42PU8W7qUMx6LRv2l/PIvzJodMhg5Yqj1audcEfPgWlLYF4+PPAfuP4F6HUDmALAUQ+Hf4KfXodFd8Gr3dXRRN/MVDvnlubKfi6SdJF0HZ0Bqf1VNlaSWZoJwAS3Wf0P0F4DkYMRqV9i1wRStVytoSgJHULymGj+7xs1/S2JZ168sCOEPj6L+i1baMjMpOCJJ4n79BM0JhNanYZRvVPYWfIjGaUZOOwu9AbtBd+nV7gf/5g2jIc+3saa3FLufn8Tf707kfhQ3zZ8GqlL0Rkg9mp1AzUAKc+Do+nN2xYo23di2/GJms4UoAY8V42AyMHq5hfRYY8hSV2NIrrwkrE2mw1/f3+qq6uxWi/dGjRdzdIDS3l+/fM86vLlsWN54HbQFJ7CGudLHD3gwFy8j2E7/oJTY6Bg5kfoxkbz4McZBFr0bP7tOIw67z/4bpeLo7uzyM/OpDAvl6riIuqqKhFuNzqDAd+gIIKiY4ns2Zuo3n2ISuiHVtd2MbHj+HEO3X4HrqoqrDdNIur111EUheO2QiYuvgFFKLwf9W9Sbujz8xf7GZsPlvObj7dha3Ri0mt48Np4Hhodj7+5c9QySZ1MfQUc2wr5m9XApSBDXSzxVL7hEDGoOXAZBGH9ITBOrcWRpCvA+fz9loHKFeDhlQ9wbc733GerAaA67EY+23U/LgwA9Nn7KVFFG6kfNJZBn7zNtIVb2XSwnIdGdef5yf0816ksLCDzP8vYu/FH6qurzvn+Jh9f4oclkTByFN0GDUWjvfCajhZ1W7aQ/8AMcDoJfnAGoU8/jaIojPv0Bkqchdx2ZBYvPfcgOv3F36uwuoE5X+xkw351FJDVpOPhMT349chu+BhlpaR0Fi6HOsz5aDoc3wGFO9XaFtFKc5CihcBuENxDbZIN7gFBPSAgFqxRoDdf8uxLUnuRgYrkcfToJso+uYUhTeq08FX9n2TRmjEIN/ROCmfQyCAqf3kzor6euH99TF5EL259ZwNajcKPz4wlOsBMxfEC0r9OY8/6dYjmX7AmPys9hiUR3acfwdGx+AYGodFqcTQ1UlNeTsnhAxTu38fR3btosFV78uMbGET/68Yz4LoJBEREXtSzVX31FYXPvwBAyGOPEfL4LF5Y/wJLDy5lSMF45o58moHXtc1CikII/rO7iNe/30deSS2grrz88Oh47kuJw2KQAYt0juz1ULwbCjOhaFdz8JKn9nc5G1MAWKPBGgl+kSe9jjqxtwSpCzRKUicnAxVJlfMN9Yt/g8XRSL1Wj3Ps30n7IhxHk4uEqyMYN70vFQsWUvLqqxh69CD+u2+Z9tEWfsor4/ah0bxwbehpAUr80BEMnnATcYOGnFNzjtvt4njuHvZt3sCeDetorLF5zsX0G8iAsRPomZSCwXRh/y2WL1xISfNwZf877yD9V4m8lP57wmu6cV/BPH71vylo9W3XZ9zlFny78zhvrsrjUJk6S26Ir5Enx/finhEx6DpgPSTpMiAE1BRC+f7m7UDzth9sBT8fxLTQGsA3ojlwiVCDF7+I5sCmOcDxiwSj7GsldSwZqFzp6sph+RzY/TUAWQYDjZP+we7Pw6iraiI6IYApjyeiOO0cuGEizpISIv7vf0nvey2PfLIdnUbhpbD9lG3+r1eAknLnL4no0euCs+V0ODiwLZ3sNd9zeNcOz+RZOqORXkkj6TdqLLEDB6PRnF9zTcXHH6tzq7jdVA/tyUMTD6MIDdO3/YEb7x5K/1Ft3yHY6XKzJPM4b63KI79C/SMSH+rDvBv7MKFfOIr8r1ZqK0JAY7UayNgKwFaozoFUc/yk14Xq0OlzZfA7KZg5KYDxi1Cbmfwi1IBHZ2i/55KuaDJQuVK5XZCxAFb9HzRW4ULhwwA/svrcxKiMGZQdrSUwwsLtc4dh8tFT9sEHlL7+F/RRUQQvXsq4N36iosHF8KrtpFSmAxA/LImRd/6S8PiebZpVW1kpu9f9l5wfV1NVdGKmWYt/APFDRxA/ZARxgxIxmC3ndL3a9Rs4/vTTuKqreXqGlqNhClMzRpNgupN7549B2041HQ6Xm8+25PPGf/OoaF51OalbEM/d1IchsYHtck9JapWzCWqL1eClplBdbLHmuLq3Ne9rCsFee+7XtASfVCtzas1Mc42NTwic5z8XkiQDlSuNELBvJaz+AxRnA1Ab1I0Zpgb2mszMqv0D9l0+mP303PnscKwhZuzHCjg4ZQqioQH9zMeYW+LPDkcwgfZK7jn+Bb2HDmuXAOX0rAsK83LJ+WkNuRt/pLG2xnNOo9UR1j2eyF4JRPZMIKx7DwLCI9DqWh8Z4Swvp/SNN/l7yVcsGalhZI6b/1mqoHTrRcDgPmgDA1EMBkRjI+7GRtwN9YiGBtz16qgMfXQ0pv798ZswHl3Q+a0WXdPo4L11B/jwp0M0OdVaqMmDInliXC96h/tdYOlIUjtoqjkleDkpiDl577Kf2/UUrTqKyS9C3fuGgk8Y+ISe8jpM7Wejkc2jkgxUrhxuF+z9Dja8qQ6DBDD50zR6LlOOLaGwoYQxYjJ9N9+ATq9h6uyhhHe3IhwODtxzL47du6kOtPLXgTeSHpSEItw8FXKYe34xmbBu8Zf8cVxOB8dydnNwx1YO7dhKZeHx09IoGg0B4REEREThFxSCT2AQvkFB+AYGq68Dg9h7dCu/3v40Rge8/5YTyzn+vvXQavEbN47gB+7HnJh4Xl9aWN3A69/v46vtxzzLwvSNtHJNj2D6R1sJt5ow6rS4hcDhclPf5KLO7qTe7qKuSd1rNQohvgZC/YzEBFqICbJg+pnRS2W1Tew6VkXWMRu7j1ezv6SWstom7C43FoOO6AAzfSL8uKZnCGN6hxLoI6v0pbMQQh1qfWrNTMv7liCnrqT1EUxnotGBJcQ7gPEJAXOAGsSY/MFoVfcnbwYf2Un4MiMDlcud7Tjs/Awy/glVR9RjOjMkPwzXPMHvMt/iq7yvCBbhTN0yByNGbpiRgN5YypHMDMSCjwktKMah0fBG8s2sDh8NwIvjYpgxYVAHPpi36pIijuflUpi3l6K8fZQdPYKjqfFnv07Ralky+jiV5kYm5o0nKSeaMEMh0TEKJqMJg38AGrNZ3XwsaMxmhMuN/Wg+dT/+ROPu3Z5rmYcOJfiB+/G9/nqU8/hPcE+hjTf+u4/Ve0twuC7uR0xRIMrfTFywhbhgH6IDTABUNzg4VtlAVkE1xypbmavjDDQKDIsL5KaBkdw0MJJwq+mi8iddwVxOqCs9EcjUlqjvW/YtW20JXMQaXChadSFJo7V539rW2rlTjhl8ZY1OJ9HlApV33nmH1157jaKiIgYPHszbb79NUlLSz37dFRWoVB2F3BWw91s4vP7EfzHmIBj+ACT9BvzC+fbAt/x2/W8BmLJ7JtG2Xlh8d1B1/CcUu4PB+cVE2Opp1Oh4a+JDrDH2AODR63rw7I0XP0FaexJCUFtZTuXx41QVH6e2ooK6ygpqK8uprVRf11VXgRBkd69mW98qAmrM3JX9RzRosNetwG3fg95kJijqKoKjryI4Jo6Q2DhCYuLwCw5FURSa8vIoX7gQ29JvEQ510TldaCg+o0dhGTECS2Ii+ri4c+owW1lnZ01uCdvzKzlQUkdxTSNOl0CrUdBpFCxGHT4GLRaDFotBh49Ri8MlKK9toqSmifzyemqanOdUPj3DfBkU7U+/KCt9I62EW40YdVrq7E4Ol9Wz81gVa/aWsLfoRPOaosDwuEAmD4xkkgxapPbktKsdfmtLoK5MrY2pLVGPNVZDo615f8rmbsuFHxW1342nWaqlZidEbZryarIKlXPXtKMuFah8/vnnTJs2jffee4/k5GTeeOMNvvjiC3JzcwkLO/uqvZdtoOJsgpIcKNwFx7fDoZ+g4oB3mtgUSEzF1fcWKkoryc8+wE/7Mvin/2c4NU6GHZvI8PxxOGq/w+08QlBtA4MKKzDXN7Ijog8fXDeDw43qH9rnJvXhN6PjL4uRKi6nk7qqCo4XH+H+7bOoddczqWIycbk3AG4cDWtwNe70pBeKmaJQLXlXVVFhrUGv0dJTG8WEsNEMD+iHYeNmar9ejNtm87qPNjAQ8+DBmBMT1W3gADQ+Pm3+PEIIKursHC6v50h5HYfL6ymubkRR1DWJIv1N9I20MvAqf6ymc5vV9HhVAyuyi1ieVUjGkUrPcUWB/lFWUuKDSekRzIhuQfid4zUlqV0IAY4GNWCx10KTTe1j09i892ynvj/1uA3c5xbwezH4nRTEhJ7oa9Paa5O/bJ46D10qUElOTmbEiBH87W9/A8DtdhMTE8Pjjz/OvHnzzvq1XTZQcdqhoRLqy9XhhlVH1BqT6qNQug9K95z2QyUUDU2B/ajwTyTfFUd+gUJVsYvGel+ENpL9Ebms7/4VTq2d2Mp+3JA9kSBrLt30DgLzDlOYX8bW8D6s7p7Mfl91nZEQXyPzbx/IhH4dvzpye/hXzr94deurGLVGpjmeQpuuTjBn8tGimJrI0mWwPWINFZbCVr8+2OZPwhELA2xB9ND5ElrbiKW0DM2xAnCc8ktPo8EQF4c2IACt1YpiMaMxmVFMRjRGE4rZpO5NRhSjCQx60OsRBgOaoECUiAgwGHC5nAiXC41Oh1anR6vTodXr0er16PSGNpnV92RnClpAbSJKiLDSP6p5C/ehu9KApbIUZ1Ex7vo6tFYrupAQdBGR6MPDUPQysJE6ISHA2agGPPXl3k1TJ9fw1JVCban6+lw7E7fQGk70uTEFNPe7aelnE3DKvrnfjcEC+ubN4HNFjZ7qMoGK3W7HYrHw5ZdfMnXqVM/x6dOnU1VVxTfffHPWr2+3QKVkL+QuU5tXhFD3blfz+5bNdeLcyefdTvUHwlEPjkZwNtJYVYq7sQaNqwGdqx6d++f7WTQJI6WOKEoawyhouIpjTfE4RCCK1p8yvwaOBR2gUVdHtbaBfLcvjTpA6AiuszCkIgQcLsobnZQZrRT6BFN/UhWmWa/lnqQYnhjXiwDL5dup0ul28tSap1h7bC0A3fW90JT6UK/UUuqbj1Or/iLSu40Md46mj2MA5aU17PXZyYHgHbg0ajBicJqIrIrCr8GE3qlB54Th+yyEl+USUNdIYH0T5lMDlwvQpNVQb9TTYNBj12pwaTS4NQruk/9LUxQ0Wm3zpkOj06LRaNHqdGi0WpSW41otWq0WjVaDRqNDUeDEVRQUBAoKCKHWpAlBhcbE4dDe7CKAbQ4Lx9zGVvNpdjQS1lBFcEM1FmcjZmcTFmcTJpcdvdmEwdcHna8voVYTUwObQKdD0ekJuG0qutDQiy4nSWp3Qqi1MLUt/WxOCWK8XpepaduC1tgcvPiozU46o7r+k9YAGv2J19pTXxvUfjyKpnlTTuxRTjp+8jnNSeeUU2qDvH/nENoHEia1zTM2O5+/3x0673dZWRkul4vwcO//6MPDw9m7d+9p6ZuammhqavK8r65Wp2a32droQ9LiwBZY/vu2vWazlhhdCGh06ah1GrA5jNQ4jdQ4jFTbTZQ2+VDrNGDwfwRFOek/VAVwQ4Exh02hSwFwNYbRcGSmJ0kBUKAFtEBLdwO3wOBsIDEmkHF9Q5k0MIogHwM4G7HZfj5o6speHPIi/vizOG8x+xv2QsvfXjsEG4K5o/cd3JFwB/5Gf8/XVBbXkb3zIMsOLifDsI4aUwUHffLgpJadq8pmUqOU4AxQv6NGuwuL3YHe5ULncqN1C7RC3WvcAq1Q9y2vtcKNxi3Qud0Y7U6MLje4XOjtDtqjTkI0b2fjBwxkJQOBVKDcZOWAfzSH/CM5aI3ikH8k5eYA6oBDRiuHjGf55eKGuPxCEj/5m+dQ3JBETMbWgx9J6nwUMISpW2D/syd1NKgBS32ZWmPj6W/TvG9q2Tcfa7KpSyk4GppnHW756WyE+kagop2f7Tz1vRUir2nTS7b83T6nuhLRgQoKCgQgNm7c6HV87ty5Iikp6bT0L7/8csvvW7nJTW5yk5vc5NbFt6NHj/5srNChNSohISFotVqKi4u9jhcXFxMREXFa+ueee47Zs2d73rvdbioqKggODr4sOoKeD5vNRkxMDEePHu1a/XO6AFm27UeWbfuRZdt+ZNm2PSEENTU1REVF/WzaDg1UDAYDw4YNY9WqVZ4+Km63m1WrVjFr1qzT0huNRoynVB0HBARcgpx2XlarVf7gtBNZtu1Hlm37kWXbfmTZti1/f/9zStfha9PPnj2b6dOnM3z4cJKSknjjjTeoq6vj/vvv7+isSZIkSZLUwTo8ULn77rspLS3lpZdeoqioiMTERFauXHlaB1tJkiRJkq48HR6oAMyaNavVph7pzIxGIy+//PJpTWHSxZNl235k2bYfWbbtR5Ztx+rwCd8kSZIkSZLORK7OJEmSJElSpyUDFUmSJEmSOi0ZqEiSJEmS1GnJQKWLeuedd+jWrRsmk4nk5GS2bNnS0VnqUD/++CNTpkwhKioKRVFYsmSJ13khBC+99BKRkZGYzWbGjx9PXl6eV5qKigpSU1OxWq0EBAQwY8YMamtrvdLs2rWLUaNGYTKZiImJ4dVXXz0tL1988QV9+vTBZDIxcOBAli9f3ubPe6nMnz+fESNG4OfnR1hYGFOnTiU3N9crTWNjIzNnziQ4OBhfX1/uuOOO0yZxzM/PZ/LkyVgsFsLCwpg7dy5Op/f6SGvXrmXo0KEYjUZ69uzJwoULT8vP5fa5f/fddxk0aJBnfo6UlBRWrFjhOS/Ltm288sorKIrCk08+6Tkmy7YLaZO58KVLKi0tTRgMBvHRRx+J3bt3i4ceekgEBASI4uLijs5ah1m+fLl4/vnnxddffy0AsXjxYq/zr7zyivD39xdLliwRO3fuFLfccovo3r27aGho8KS58cYbxeDBg8XmzZvFTz/9JHr27Cnuvfdez/nq6moRHh4uUlNTRXZ2tvjss8+E2WwW77//vifNhg0bhFarFa+++qrIyckRL7zwgtDr9SIrK6vdy6A9TJw4USxYsEBkZ2eLzMxMcdNNN4nY2FhRW1vrSfPII4+ImJgYsWrVKrFt2zZx9dVXi5EjR3rOO51OMWDAADF+/HixY8cOsXz5chESEiKee+45T5qDBw8Ki8UiZs+eLXJycsTbb78ttFqtWLlypSfN5fi5X7p0qVi2bJnYt2+fyM3NFb/97W+FXq8X2dnZQghZtm1hy5Ytolu3bmLQoEHiiSee8ByXZdt1yEClC0pKShIzZ870vHe5XCIqKkrMnz+/A3PVeZwaqLjdbhERESFee+01z7GqqiphNBrFZ599JoQQIicnRwBi69atnjQrVqwQiqKIgoICIYQQf//730VgYKBoamrypHn22WdFQkKC5/1dd90lJk+e7JWf5ORk8fDDD7fpM3aUkpISAYh169YJIdRy1Ov14osvvvCk2bNnjwDEpk2bhBBqEKnRaERRUZEnzbvvviusVqunLJ955hnRv39/r3vdfffdYuLEiZ73V8rnPjAwUHz44YeybNtATU2N6NWrl/jhhx/EmDFjPIGKLNuuRTb9dDF2u52MjAzGjx/vOabRaBg/fjybNm3qwJx1XocOHaKoqMirzPz9/UlOTvaU2aZNmwgICGD48OGeNOPHj0ej0ZCenu5JM3r0aAwGgyfNxIkTyc3NpbKy0pPm5Pu0pLlcvjctK5YHBQUBkJGRgcPh8HrmPn36EBsb61W2AwcO9JrEceLEidhsNnbv3u1Jc7ZyuxI+9y6Xi7S0NOrq6khJSZFl2wZmzpzJ5MmTT3t+WbZdS6eY8E06d2VlZbhcrtNm7g0PD2fv3r0dlKvOraioCKDVMms5V1RURFhYmNd5nU5HUFCQV5ru3bufdo2Wc4GBgRQVFZ31Pl2Z2+3mySef5JprrmHAgAGA+twGg+G0NbdOLdvWyqTl3NnS2Gw2GhoaqKysvGw/91lZWaSkpNDY2Iivry+LFy+mX79+ZGZmyrK9CGlpaWzfvp2tW7eedk5+brsWGahIknROZs6cSXZ2NuvXr+/orFxWEhISyMzMpLq6mi+//JLp06ezbt26js5Wl3b06FGeeOIJfvjhB0wmU0dnR7pIsumniwkJCUGr1Z7WO724uJiIiIgOylXn1lIuZyuziIgISkpKvM47nU4qKiq80rR2jZPvcaY0Xf17M2vWLL777jvWrFnDVVdd5TkeERGB3W6nqqrKK/2pZXuh5Wa1WjGbzZf1595gMNCzZ0+GDRvG/PnzGTx4MG+++aYs24uQkZFBSUkJQ4cORafTodPpWLduHW+99RY6nY7w8HBZtl2IDFS6GIPBwLBhw1i1apXnmNvtZtWqVaSkpHRgzjqv7t27ExER4VVmNpuN9PR0T5mlpKRQVVVFRkaGJ83q1atxu90kJyd70vz44484HA5Pmh9++IGEhAQCAwM9aU6+T0uarvq9EUIwa9YsFi9ezOrVq09r+ho2bBh6vd7rmXNzc8nPz/cq26ysLK9A8IcffsBqtdKvXz9PmrOV25X0uXe73TQ1NcmyvQjjxo0jKyuLzMxMzzZ8+HBSU1M9r2XZdiEd3ZtXOn9paWnCaDSKhQsXipycHPGb3/xGBAQEePVOv9LU1NSIHTt2iB07dghA/OUvfxE7duwQR44cEUKow5MDAgLEN998I3bt2iVuvfXWVocnDxkyRKSnp4v169eLXr16eQ1PrqqqEuHh4eK+++4T2dnZIi0tTVgsltOGJ+t0OvHnP/9Z7NmzR7z88stdenjyo48+Kvz9/cXatWtFYWGhZ6uvr/ekeeSRR0RsbKxYvXq12LZtm0hJSREpKSme8y3DPG+44QaRmZkpVq5cKUJDQ1sd5jl37lyxZ88e8c4777Q6zPNy+9zPmzdPrFu3Thw6dEjs2rVLzJs3TyiKIr7//nshhCzbtnTyqB8hZNl2JTJQ6aLefvttERsbKwwGg0hKShKbN2/u6Cx1qDVr1gjgtG369OlCCHWI8osvvijCw8OF0WgU48aNE7m5uV7XKC8vF/fee6/w9fUVVqtV3H///aKmpsYrzc6dO8W1114rjEajiI6OFq+88sppefn3v/8tevfuLQwGg+jfv79YtmxZuz13e2utTAGxYMECT5qGhgbx2GOPicDAQGGxWMRtt90mCgsLva5z+PBhMWnSJGE2m0VISIh4+umnhcPh8EqzZs0akZiYKAwGg4iPj/e6R4vL7XP/wAMPiLi4OGEwGERoaKgYN26cJ0gRQpZtWzo1UJFl23XI1ZMlSZIkSeq0ZB8VSZIkSZI6LRmoSJIkSZLUaclARZIkSZKkTksGKpIkSZIkdVoyUJEkSZIkqdOSgYokSZIkSZ2WDFQkSZIkSeq0ZKAiSZIkSVKnJQMVSZIu2OHDh1EUhczMzDOmWbt2LYqinLYAXHv53e9+R2Ji4iW5lyRJ7U/X0RmQJKnriomJobCwkJCQkI7OiiRJlykZqEiSdMG0Wq1crl6SpHYlm34kSTqrlStXcu211xIQEEBwcDA333wzBw4cAFpv+lm+fDm9e/fGbDYzduxYDh8+fE73sdlsmM1mVqxY4XV88eLF+Pn5UV9fD8Czzz5L7969sVgsxMfH8+KLL+JwOM543euuu44nn3zS69jUqVP59a9/7Xnf1NTEnDlziI6OxsfHh+TkZNauXXtO+ZYkqX3JQEWSpLOqq6tj9uzZbNu2jVWrVqHRaLjttttwu92npT169Ci33347U6ZMITMzkwcffJB58+ad032sVis333wzixYt8jr+6aefMnXqVCwWCwB+fn4sXLiQnJwc3nzzTT744AP++te/XtQzzpo1i02bNpGWlsauXbv4xS9+wY033kheXt5FXVeSpIsnm34kSTqrO+64w+v9Rx99RGhoKDk5Ofj6+nqde/fdd+nRowevv/46AAkJCWRlZfGnP/3pnO6VmprKfffdR319PRaLBZvNxrJly1i8eLEnzQsvvOB53a1bN+bMmUNaWhrPPPPMBT1ffn4+CxYsID8/n6ioKADmzJnDypUrWbBgAX/84x8v6LqSJLUNGahIknRWeXl5vPTSS6Snp1NWVuapScnPz6dfv35eaffs2UNycrLXsZSUlHO+10033YRer2fp0qXcc889fPXVV1itVsaPH+9J8/nnn/PWW29x4MABamtrcTqdWK3WC36+rKwsXC4XvXv39jre1NREcHDwBV9XkqS2IQMVSZLOasqUKcTFxfHBBx8QFRWF2+1mwIAB2O32Nr+XwWDgzjvvZNGiRdxzzz0sWrSIu+++G51O/VW1adMmUlNT+f3vf8/EiRPx9/cnLS3NU4PTGo1GgxDC69jJfVpqa2vRarVkZGSg1Wq90p1aYyRJ0qUnAxVJks6ovLyc3NxcPvjgA0aNGgXA+vXrz5i+b9++LF261OvY5s2bz+ueqampTJgwgd27d7N69Wr+8Ic/eM5t3LiRuLg4nn/+ec+xI0eOnPV6oaGhFBYWet67XC6ys7MZO3YsAEOGDMHlclFSUuJ5RkmSOg/ZmVaSpDMKDAwkODiYf/zjH+zfv5/Vq1cze/bsM6Z/5JFHyMvLY+7cueTm5rJo0SIWLlx4XvccPXo0ERERpKam0r17d6+mpF69epGfn09aWhoHDhzgrbfe8uq/0prrr7+eZcuWsWzZMvbu3cujjz7qNflc7969SU1NZdq0aXz99dccOnSILVu2MH/+fJYtW3ZeeZckqe3JQEWSpDPSaDSkpaWRkZHBgAEDeOqpp3jttdfOmD42NpavvvqKJUuWMHjwYN57773z7oyqKAr33nsvO3fuJDU11evcLbfcwlNPPcWsWbNITExk48aNvPjii2e93gMPPMD06dOZNm0aY8aMIT4+3lOb0mLBggVMmzaNp59+moSEBKZOncrWrVuJjY09r7xLktT2FHFq460kSZIkSVInIWtUJEmSJEnqtGSgIknSJTNp0iR8fX1b3eR8JZIktUY2/UiSdMkUFBTQ0NDQ6rmgoCCCgoIucY4kSersZKAiSZIkSVKnJZt+JEmSJEnqtGSgIkmSJElSpyUDFUmSJEmSOi0ZqEiSJEmS1GnJQEWSJEmSpE5LBiqSJEmSJHVaMlCRJEmSJKnTkoGKJEmSJEmd1v8Hrg9kaU7jKlgAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "                                count          mean          std     min  \\\n",
+            "level  control                                                             \n",
+            "2-year Private for-profit       464.0   4559.681034  1286.688269   294.0   \n",
+            "       Private not-for-profit    68.0   5128.058824  2590.449946   934.0   \n",
+            "       Public                   926.0   4126.199784  1260.271382   881.0   \n",
+            "4-year Private for-profit       527.0   4696.062619  1489.410020  1580.0   \n",
+            "       Private not-for-profit  1180.0  14702.401695  7649.775203   902.0   \n",
+            "       Public                   632.0   6514.071203  2353.716693  2232.0   \n",
+            "\n",
+            "                                   25%      50%       75%      max  \n",
+            "level  control                                                      \n",
+            "2-year Private for-profit      3818.75   4286.5   5122.00   9727.0  \n",
+            "       Private not-for-profit  3650.00   4516.5   6311.50  13654.0  \n",
+            "       Public                  3311.25   3943.5   4762.00   9809.0  \n",
+            "4-year Private for-profit      3885.50   4364.0   5131.50  18355.0  \n",
+            "       Private not-for-profit  9113.25  13774.5  18996.75  41580.0  \n",
+            "       Public                  4990.50   6085.0   7341.50  17299.0  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.7:\n",
+        "\n",
+        "The distribution of aid values is highly right-skewed, with most public institutions clustering at lower aid amounts since they charge lower tuition and rely on state funding. In contrast, private not-for-profit schools show a wider spread and a long right tail, reflecting higher tuition costs and larger aid packages. Private for-profits fall in between, resulting in the overall peak at middle aid levels and a tapering of the graph as aid amounts increase."
+      ],
+      "metadata": {
+        "id": "y_N3onyVpcqw"
+      },
+      "id": "y_N3onyVpcqw"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.8: Scatterplots\n",
+        "#Overall scatter with \"aid_value\" and \"grad_100_value\"\n",
+        "sns.scatterplot(data=df1, x='aid_value', y='grad_100_value')\n",
+        "plt.show()\n",
+        "\n",
+        "#By level\n",
+        "sns.scatterplot(data=df1, x='aid_value', y='grad_100_value', hue='level')\n",
+        "plt.show()\n",
+        "\n",
+        "# By control\n",
+        "sns.scatterplot(data=df1, x='aid_value', y='grad_100_value', hue='control')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "9aNJ8SSWpONG",
+        "outputId": "070185d7-af37-4776-f6a9-539ec61cbb0f"
+      },
+      "id": "9aNJ8SSWpONG",
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.8:\n",
+        "\n",
+        "The scatterplot shows a weak positive relationship between aid_value and grad_100_value: as average aid increases, graduation rates tend to be higher, though the data are widely scattered. Most institutions cluster at lower aid amounts and lower graduation rates, but some with very high aid also reach the highest graduation outcomes.\n",
+        "\n",
+        "The plots for the \"level\" and \"control\" show that 4-year institutions, especially private not-for-profit schools, have a clearer positive relationship between aid and graduation rates: higher aid values are associated with higher completion rates. In contrast, 2-year schools and for-profit institutions cluster at lower aid levels with weaker or inconsistent links to graduation success."
+      ],
+      "metadata": {
+        "id": "dOtWfZXoqAvG"
+      },
+      "id": "dOtWfZXoqAvG"
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "0u4BbZQhpvmh"
+      },
+      "id": "0u4BbZQhpvmh",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/assignment.ipynb b/assignment.ipynb
new file mode 100644
index 0000000..934cf3c
--- /dev/null
+++ b/assignment.ipynb
@@ -0,0 +1,3137 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "13ad028b-72b7-43ed-aa78-96fd4e518040",
+      "metadata": {
+        "id": "13ad028b-72b7-43ed-aa78-96fd4e518040"
+      },
+      "source": [
+        "# Assignment: Data Wrangling\n",
+        "### `! git clone https://github.com/ds3001f25/wrangling_assignment.git`\n",
+        "### Do Q1 and Q2\n",
+        "### Reading material: `tidy_data.pdf`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "da879ea7-8aac-48a3-b6c2-daea56d2e072",
+      "metadata": {
+        "id": "da879ea7-8aac-48a3-b6c2-daea56d2e072"
+      },
+      "source": [
+        "**Q1.** This question provides some practice cleaning variables which have common problems.\n",
+        "1. Numeric variable: For `./data/airbnb_hw.csv`, clean the `Price` variable as well as you can, and explain the choices you make. How many missing values do you end up with? (Hint: What happens to the formatting when a price goes over 999 dollars, say from 675 to 1,112?)\n",
+        "2. Categorical variable: For the Minnesota police use of for data, `./data/mn_police_use_of_force.csv`, clean the `subject_injury` variable, handling the NA's; this gives a value `Yes` when a person was injured by police, and `No` when no injury occurred. What proportion of the values are missing? Is this a concern? Cross-tabulate your cleaned `subject_injury` variable with the `force_type` variable. Are there any patterns regarding when the data are missing?\n",
+        "3. Dummy variable: For the pretrial data covered in the lecture `./data/justice_data.parquet`, clean the `WhetherDefendantWasReleasedPretrial` variable as well as you can, and, in particular, replace missing values with `np.nan`.\n",
+        "4. Missing values, not at random: For the pretrial data covered in the lecture, clean the `ImposedSentenceAllChargeInContactEvent` variable as well as you can, and explain the choices you make. (Hint: Look at the `SentenceTypeAllChargesAtConvictionInContactEvent` variable.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9d412a8d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 90
+        },
+        "id": "9d412a8d",
+        "outputId": "16c11c28-2cc6-4ee3-aae0-6daa6207f168"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c6d540c3-1c71-48a2-94f8-2a850b5e4b5c\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c6d540c3-1c71-48a2-94f8-2a850b5e4b5c\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving airbnb_hw.csv to airbnb_hw (1).csv\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['airbnb_hw (1).csv'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "#Import pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uploaded.keys()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ltJN3M8UH82S",
+        "outputId": "3daf1de4-5f39-4b0c-d6d3-4c25e416df63"
+      },
+      "id": "ltJN3M8UH82S",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['airbnb_hw (1).csv'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "#Read the \"airbnb\" csv\n",
+        "airbnb=pd.read_csv('airbnb_hw.csv')\n",
+        "airbnb.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 469
+        },
+        "id": "B8ikkvZ-ICPZ",
+        "outputId": "bdb7afb3-d0ed-4b26-9cdd-5607de6dc48e"
+      },
+      "id": "B8ikkvZ-ICPZ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Host Id Host Since                                Name Neighbourhood   \\\n",
+              "0   5162530        NaN     1 Bedroom in Prime Williamsburg       Brooklyn   \n",
+              "1  33134899        NaN     Sunny, Private room in Bushwick       Brooklyn   \n",
+              "2  39608626        NaN                Sunny Room in Harlem      Manhattan   \n",
+              "3       500  6/26/2008  Gorgeous 1 BR with Private Balcony      Manhattan   \n",
+              "4       500  6/26/2008            Trendy Times Square Loft      Manhattan   \n",
+              "\n",
+              "  Property Type  Review Scores Rating (bin)        Room Type  Zipcode  Beds  \\\n",
+              "0     Apartment                         NaN  Entire home/apt  11249.0   1.0   \n",
+              "1     Apartment                         NaN     Private room  11206.0   1.0   \n",
+              "2     Apartment                         NaN     Private room  10032.0   1.0   \n",
+              "3     Apartment                         NaN  Entire home/apt  10024.0   3.0   \n",
+              "4     Apartment                        95.0     Private room  10036.0   3.0   \n",
+              "\n",
+              "   Number of Records  Number Of Reviews Price  Review Scores Rating  \n",
+              "0                  1                  0   145                   NaN  \n",
+              "1                  1                  1    37                   NaN  \n",
+              "2                  1                  1    28                   NaN  \n",
+              "3                  1                  0   199                   NaN  \n",
+              "4                  1                 39   549                  96.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Host Id</th>\n",
+              "      <th>Host Since</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Neighbourhood</th>\n",
+              "      <th>Property Type</th>\n",
+              "      <th>Review Scores Rating (bin)</th>\n",
+              "      <th>Room Type</th>\n",
+              "      <th>Zipcode</th>\n",
+              "      <th>Beds</th>\n",
+              "      <th>Number of Records</th>\n",
+              "      <th>Number Of Reviews</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Review Scores Rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5162530</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1 Bedroom in Prime Williamsburg</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>11249.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>145</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>33134899</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny, Private room in Bushwick</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>11206.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>37</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>39608626</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny Room in Harlem</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10032.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Gorgeous 1 BR with Private Balcony</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>10024.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>199</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Trendy Times Square Loft</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10036.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>39</td>\n",
+              "      <td>549</td>\n",
+              "      <td>96.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-02487396-5e31-4380-b79b-258d7648b742\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-02487396-5e31-4380-b79b-258d7648b742')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-02487396-5e31-4380-b79b-258d7648b742 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "airbnb",
+              "summary": "{\n  \"name\": \"airbnb\",\n  \"rows\": 30478,\n  \"fields\": [\n    {\n      \"column\": \"Host Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11902702,\n        \"min\": 500,\n        \"max\": 43033067,\n        \"num_unique_values\": 24421,\n        \"samples\": [\n          24093114,\n          16925490,\n          2886652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Host Since\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-06-26 00:00:00\",\n        \"max\": \"2015-08-31 00:00:00\",\n        \"num_unique_values\": 2240,\n        \"samples\": [\n          \"5/20/2010\",\n          \"7/2/2009\",\n          \"4/18/2015\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29413,\n        \"samples\": [\n          \"Explore the beauty of Brooklyn\",\n          \"Sunny Room in Williamsburg Loft\",\n          \"cozy one bedroom in lower east side\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighbourhood \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Manhattan\",\n          \"Staten Island\",\n          \"Queens\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apartment\",\n          \"Condominium\",\n          \"Bungalow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating (bin)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.05951861814779,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          40.0,\n          20.0,\n          95.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Room Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Entire home/apt\",\n          \"Private room\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 921.2993969568683,\n        \"min\": 1003.0,\n        \"max\": 99135.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          11414.0,\n          11239.0,\n          11365.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0153587174802678,\n        \"min\": 0.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          12.0,\n          16.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Records\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number Of Reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 0,\n        \"max\": 257,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          171\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 511,\n        \"samples\": [\n          \"299\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.850373136231884,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check for the column names\n",
+        "airbnb.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NTXjatS8Iy3n",
+        "outputId": "164a171c-2b70-424e-8f94-bb413c2a0483"
+      },
+      "id": "NTXjatS8Iy3n",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Host Id', 'Host Since', 'Name', 'Neighbourhood ', 'Property Type',\n",
+              "       'Review Scores Rating (bin)', 'Room Type', 'Zipcode', 'Beds',\n",
+              "       'Number of Records', 'Number Of Reviews', 'Price',\n",
+              "       'Review Scores Rating'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check the shape\n",
+        "airbnb.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i7N6IXwzI0a2",
+        "outputId": "cd9b636c-bfd3-4854-bad4-c8d4d06bc950"
+      },
+      "id": "i7N6IXwzI0a2",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(30478, 13)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check for missing values\n",
+        "airbnb.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 491
+        },
+        "id": "mHiB9FGlI6mj",
+        "outputId": "7cb79e51-fcba-409e-8ec5-65853b54d10f"
+      },
+      "id": "mHiB9FGlI6mj",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Host Id                          0\n",
+              "Host Since                       3\n",
+              "Name                             0\n",
+              "Neighbourhood                    0\n",
+              "Property Type                    3\n",
+              "Review Scores Rating (bin)    8323\n",
+              "Room Type                        0\n",
+              "Zipcode                        134\n",
+              "Beds                            85\n",
+              "Number of Records                0\n",
+              "Number Of Reviews                0\n",
+              "Price                            0\n",
+              "Review Scores Rating          8323\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Host Id</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Host Since</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Name</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Neighbourhood</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Property Type</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Review Scores Rating (bin)</th>\n",
+              "      <td>8323</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Room Type</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Zipcode</th>\n",
+              "      <td>134</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Beds</th>\n",
+              "      <td>85</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Number of Records</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Number Of Reviews</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Price</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Review Scores Rating</th>\n",
+              "      <td>8323</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This line of code removes any $ and then converts to numeric values\n",
+        "airbnb['Price_clean'] = pd.to_numeric(\n",
+        "    airbnb['Price'].replace('[\\\\$,]', '', regex=True),\n",
+        "    errors='coerce'\n",
+        ")\n",
+        "\n",
+        "#This line of code counts all of the missing values\n",
+        "missing_prices=airbnb['Price_clean'].isna().sum()\n",
+        "print(\"Missing values in Price:\", missing_prices)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gt9R0D6AJ9I1",
+        "outputId": "55df4839-4329-4e93-8240-003fbf6fb2f2"
+      },
+      "id": "gt9R0D6AJ9I1",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values in Price: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. I cleaned the Price variable by removing dollar sign symbols and commas, then converting the values to numeric. This ensures that prices above $999 are correctly interpreted as numbers rather than strings. Any entries that could not be converted (blanks or text) were set to missing (NaN). After cleaning, the number of missing values in Price is 0."
+      ],
+      "metadata": {
+        "id": "5lTNuV-9Ll9Z"
+      },
+      "id": "5lTNuV-9Ll9Z"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#For question #2\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "FyJbVs15NThZ",
+        "outputId": "43ea349a-56ac-4df6-9ff6-c81dac4d7bff"
+      },
+      "id": "FyJbVs15NThZ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-a289b6e6-41ea-45c4-b8b8-3f849f19aba8\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-a289b6e6-41ea-45c4-b8b8-3f849f19aba8\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving mn_police_use_of_force.csv to mn_police_use_of_force.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Read the CSV\n",
+        "mn_police=pd.read_csv('mn_police_use_of_force.csv')"
+      ],
+      "metadata": {
+        "id": "zP185C3-NTGp"
+      },
+      "id": "zP185C3-NTGp",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mn_police.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6wH20sCXJ9GV",
+        "outputId": "9f1fe214-39cf-4008-c372-90e2ef42f814"
+      },
+      "id": "6wH20sCXJ9GV",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['response_datetime', 'problem', 'is_911_call', 'primary_offense',\n",
+              "       'subject_injury', 'force_type', 'force_type_action', 'race', 'sex',\n",
+              "       'age', 'type_resistance', 'precinct', 'neighborhood'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Shows all of the unique and how many are missing\n",
+        "mn_police['subject_injury'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "bmPMw2FeJ9Dd",
+        "outputId": "0bb31a89-1428-4f07-fdb9-6eed6a02d503"
+      },
+      "id": "bmPMw2FeJ9Dd",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "subject_injury\n",
+              "NaN    9848\n",
+              "Yes    1631\n",
+              "No     1446\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>subject_injury</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>NaN</th>\n",
+              "      <td>9848</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Yes</th>\n",
+              "      <td>1631</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>No</th>\n",
+              "      <td>1446</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Any value not listed in the map becomes NaN and is stored in a new column\n",
+        "#'subject_injury_clean'\n",
+        "mn_police['subject_injury_clean']=mn_police['subject_injury'].map({\n",
+        "    'Yes':'Yes',\n",
+        "    'No':'No'\n",
+        "})\n",
+        "\n",
+        "#Calculate the proportion of missing values in column + print\n",
+        "missing_prop=mn_police['subject_injury_clean'].isna().mean()\n",
+        "print ('Proportion missing values in column:',missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MSKkXK0KJ9AV",
+        "outputId": "fbf708c8-8a44-4e61-a337-3b85c6cfb736"
+      },
+      "id": "MSKkXK0KJ9AV",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing values in column: 0.7619342359767892\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Crosstabulation\n",
+        "\n",
+        "cross_tab=pd.crosstab(\n",
+        "    mn_police['subject_injury_clean'],\n",
+        "    mn_police['force_type'],\n",
+        "    dropna=False\n",
+        ")\n",
+        "\n",
+        "print(cross_tab)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wz_aBbi8hqXQ",
+        "outputId": "ce94506e-3faf-4cb2-9195-111bf31d5344"
+      },
+      "id": "wz_aBbi8hqXQ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "force_type            Baton  Bodily Force  Chemical Irritant  Firearm  \\\n",
+            "subject_injury_clean                                                    \n",
+            "No                        0          1093                131        2   \n",
+            "Yes                       2          1286                 41        0   \n",
+            "NaN                       2          7051               1421        0   \n",
+            "\n",
+            "force_type            Gun Point Display  Improvised Weapon  Less Lethal  \\\n",
+            "subject_injury_clean                                                      \n",
+            "No                                   33                 34            0   \n",
+            "Yes                                  44                 40            0   \n",
+            "NaN                                  27                 74           87   \n",
+            "\n",
+            "force_type            Less Lethal Projectile  Maximal Restraint Technique  \\\n",
+            "subject_injury_clean                                                        \n",
+            "No                                         1                            0   \n",
+            "Yes                                        2                            0   \n",
+            "NaN                                        0                          170   \n",
+            "\n",
+            "force_type            Police K9 Bite  Taser  \n",
+            "subject_injury_clean                         \n",
+            "No                                 2    150  \n",
+            "Yes                               44    172  \n",
+            "NaN                               31    985  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2. I cleaned the 'subject_injury' variable by mapping all entries to 'Yes' if the person was injured and 'No' if not, keeping all of the missing entries NaN. The proportion of missing values is approximately 76% (which comes from the calculation we did earlier). 76% is definitely a concern because missingness is substantial and non-random. A cross-tabulation of 'subject_injury_clean' with 'force_type' shows that missing data occur disproportionately for common force types while rare or severe force types have fewer missing values. From this analysis, it can be implied that missing values might NOT be completely random and could be biased if not addressed properly."
+      ],
+      "metadata": {
+        "id": "XTcG_uN5iP6P"
+      },
+      "id": "XTcG_uN5iP6P"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#For question #3\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "ZO8EWs0vJ89F",
+        "outputId": "f214a76c-58e5-4859-b1c0-f287b3f3dbae"
+      },
+      "id": "ZO8EWs0vJ89F",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f4f1111f-38e4-491c-8de7-9b9d0dfca731\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f4f1111f-38e4-491c-8de7-9b9d0dfca731\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving justice_data.parquet to justice_data (1).parquet\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Read the file\n",
+        "justice=pd.read_parquet('justice_data.parquet')\n",
+        "#Check for unique values and missing values\n",
+        "justice['WhetherDefendantWasReleasedPretrial'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "VsYswhDKJ81W",
+        "outputId": "9f63294f-6601-4b03-b5d2-908f8b8200f7"
+      },
+      "id": "VsYswhDKJ81W",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "WhetherDefendantWasReleasedPretrial\n",
+              "1    19154\n",
+              "0     3801\n",
+              "9       31\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>WhetherDefendantWasReleasedPretrial</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>19154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['WhetherDefendantWasReleasedPretrial'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cKoogsYBpm2b",
+        "outputId": "c4b967af-f780-495a-89f9-ee0469081bd5"
+      },
+      "id": "cKoogsYBpm2b",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([9, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Map 'Yes' becomes 1 and 'No becoems' 0 and replace anything missing\n",
+        "justice['ReleasedPretrial_clean'] = justice['WhetherDefendantWasReleasedPretrial'].replace({\n",
+        "    1: 1,\n",
+        "    0: 0,\n",
+        "    9: np.nan\n",
+        "}).astype('float')  # ensures NaN is possible\n",
+        "\n",
+        "# Check counts\n",
+        "justice['ReleasedPretrial_clean'].value_counts(dropna=False)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "efg9vCNsJ8rO",
+        "outputId": "7b9826e2-d604-4371-d3be-d4a901b80632"
+      },
+      "id": "efg9vCNsJ8rO",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ReleasedPretrial_clean\n",
+              "1.0    19154\n",
+              "0.0     3801\n",
+              "NaN       31\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ReleasedPretrial_clean</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1.0</th>\n",
+              "      <td>19154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0.0</th>\n",
+              "      <td>3801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NaN</th>\n",
+              "      <td>31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Calculate the proportion missing after cleaning\n",
+        "missing_prop=justice['ReleasedPretrial_clean'].isna().mean()\n",
+        "print(\"Proportion missing values:\", missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fTKrpMhypLxz",
+        "outputId": "875425b2-8ff1-45b0-978f-99d7e0f4e022"
+      },
+      "id": "fTKrpMhypLxz",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing values: 0.0013486470025232751\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3. I cleaned the 'WhetherDefendantWasReleasedPretrial' variable by mapping 1 to indicate the defendant was released pretrial, 0 to indicate the defendant was not released, and 9 as placeholder for missing values to NaN. This created the numeric dummy variable 'ReleasedPretrial_clean' which was suitable for analysis. After cleaning, the proportion f missing values was approximately 0.135% (based of the calculation we made)."
+      ],
+      "metadata": {
+        "id": "lz3wbTwIqA7r"
+      },
+      "id": "lz3wbTwIqA7r"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['ImposedSentenceAllChargeInContactEvent'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "UForialFpLvb",
+        "outputId": "058d2f53-5415-45bd-d4db-853393ff79a4"
+      },
+      "id": "UForialFpLvb",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ImposedSentenceAllChargeInContactEvent\n",
+              "                    9053\n",
+              "0                   4953\n",
+              "12                  1404\n",
+              ".985626283367556    1051\n",
+              "6                    809\n",
+              "                    ... \n",
+              "11.9055441478439       1\n",
+              "35.0061601642711       1\n",
+              "46.6242299794661       1\n",
+              "81.0225872689938       1\n",
+              "202                    1\n",
+              "Name: count, Length: 484, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ImposedSentenceAllChargeInContactEvent</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <td>9053</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>4953</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>1404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>.985626283367556</th>\n",
+              "      <td>1051</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>809</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11.9055441478439</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35.0061601642711</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46.6242299794661</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81.0225872689938</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>202</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>484 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.crosstab(\n",
+        "    justice['ImposedSentenceAllChargeInContactEvent'].isna(),\n",
+        "    justice['SentenceTypeAllChargesAtConvictionInContactEvent']\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "9Uqc6wU7pLs8",
+        "outputId": "35caa79c-f25a-427f-ad2d-8354ab163bde"
+      },
+      "id": "9Uqc6wU7pLs8",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SentenceTypeAllChargesAtConvictionInContactEvent     0     1    2     4    9\n",
+              "ImposedSentenceAllChargeInContactEvent                                      \n",
+              "False                                             8720  4299  914  8779  274"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-692e14be-7b80-4561-a780-933f4da80259\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>SentenceTypeAllChargesAtConvictionInContactEvent</th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>4</th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ImposedSentenceAllChargeInContactEvent</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>False</th>\n",
+              "      <td>8720</td>\n",
+              "      <td>4299</td>\n",
+              "      <td>914</td>\n",
+              "      <td>8779</td>\n",
+              "      <td>274</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-692e14be-7b80-4561-a780-933f4da80259')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-692e14be-7b80-4561-a780-933f4da80259 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-692e14be-7b80-4561-a780-933f4da80259');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \")\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"ImposedSentenceAllChargeInContactEvent\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8720,\n        \"max\": 8720,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4299,\n        \"max\": 4299,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 914,\n        \"max\": 914,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8779,\n        \"max\": 8779,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8779\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 274,\n        \"max\": 274,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['ImposedSentence_clean']=pd.to_numeric(\n",
+        "    justice['ImposedSentenceAllChargeInContactEvent'],\n",
+        "    errors='coerce' #anything that is invalid becomes NaN\n",
+        ")\n",
+        "\n",
+        "missing_prop=justice['ImposedSentence_clean'].isna().mean()\n",
+        "print('Proportion missing:', missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWzVHISRpLqk",
+        "outputId": "e872857c-1d60-442e-8691-00775e600097"
+      },
+      "id": "dWzVHISRpLqk",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing: 0.3938484294788132\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "4. I cleaned the 'ImposedSentenceAllChargeInContactEvent' variable by converting it to numeric and leaving the missing entries as NaN. Missing values typically occur when the defendant was not convicted as indicated by the 'SentenceTypeAllChargesAtConvinctionInContactEvent' variable. After cleaning, the proportion of missing values is 39% which is reasonable given that not all defendants recieved a sentence"
+      ],
+      "metadata": {
+        "id": "-SEitsnWs18C"
+      },
+      "id": "-SEitsnWs18C"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5a60a44e",
+      "metadata": {
+        "id": "5a60a44e"
+      },
+      "source": [
+        "**Q2.** Go to https://sharkattackfile.net/ and download their dataset on shark attacks (Hint: `GSAF5.xls`).\n",
+        "\n",
+        "1. Open the shark attack file using Pandas. It is probably not a csv file, so `read_csv` won't work.\n",
+        "2. Drop any columns that do not contain data.\n",
+        "3. Clean the year variable. Describe the range of values you see. Filter the rows to focus on attacks since 1940. Are attacks increasing, decreasing, or remaining constant over time?\n",
+        "4. Clean the Age variable and make a histogram of the ages of the victims.\n",
+        "5. What proportion of victims are male?\n",
+        "6. Clean the `Type` variable so it only takes three values: Provoked and Unprovoked and Unknown. What proportion of attacks are unprovoked?\n",
+        "7. Clean the `Fatal Y/N` variable so it only takes three values: Y, N, and Unknown.\n",
+        "8. Are sharks more likely to launch unprovoked attacks on men or women? Is the attack more or less likely to be fatal when the attack is provoked or unprovoked? Is it more or less likely to be fatal when the victim is male or female? How do you feel about sharks?\n",
+        "9. What proportion of attacks appear to be by white sharks? (Hint: `str.split()` makes a vector of text values into a list of lists, split by spaces.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a3f03830",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "a3f03830",
+        "outputId": "dab48cd3-cf56-4dd4-c977-a9706d6ba1ba"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f189ae9f-d0ed-4d98-9659-7032ae907255\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f189ae9f-d0ed-4d98-9659-7032ae907255\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving GSAF5.xls to GSAF5.xls\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#It is an excel file so we will just have to use pd.read_excel\n",
+        "from google.colab import files\n",
+        "uploaded=files.upload() #upload the GSAF5 file here"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sharks=pd.read_excel('GSAF5.xls')\n",
+        "sharks = sharks.dropna(axis=1,how='all') #drop all columns where all values are NaN\n",
+        "sharks.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 481
+        },
+        "id": "yrVqLFZTtY4j",
+        "outputId": "9cb88c93-d448-4fee-edae-8e3b5dcfcad2"
+      },
+      "id": "yrVqLFZTtY4j",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Date    Year        Type      Country  \\\n",
+              "0  16th August 2025  2025.0    Provoked          USA   \n",
+              "1       18th August  2025.0  Unprovoked    Australia   \n",
+              "2       17th August  2025.0  Unprovoked      Bahamas   \n",
+              "3        7th August  2025.0  Unprovoked    Australia   \n",
+              "4        1st August  2025.0  Unprovoked  Puerto Rico   \n",
+              "\n",
+              "                               State                             Location  \\\n",
+              "0                            Florida               Cayo Costa Boca Grande   \n",
+              "1                                NSW                       Cabarita Beach   \n",
+              "2  Atlantic Ocean near Big Grand Cay  North of Grand Bahama near Freeport   \n",
+              "3                                NSW                        Tathra Beach    \n",
+              "4                           Carolina                       Carolina Beach   \n",
+              "\n",
+              "       Activity          Name Sex Age  ...                 Species   \\\n",
+              "0       Fishing   Shawn Meuse   M   ?  ...  Lemon shark 1.8 m (6ft)   \n",
+              "1       Surfing     Brad Ross   M   ?  ...  5m (16.5ft) Great White   \n",
+              "2  Spearfishing    Not stated   M  63  ...             Undetermined   \n",
+              "3       Surfing   Bowie Daley   M   9  ...    Suspected Great White   \n",
+              "4        Wading  Eleonora Boi   F  39  ...             Undetermined   \n",
+              "\n",
+              "                                              Source  pdf href formula href  \\\n",
+              "0  Johannes Marchand: Kevin McMurray Trackingshar...  NaN          NaN  NaN   \n",
+              "1  Bob Myatt GSAF The Guardian: 9 News: ABS News:...  NaN          NaN  NaN   \n",
+              "2  Ralph Collier GSAF and Kevin MCMurray Tracking...  NaN          NaN  NaN   \n",
+              "3                                     Bob Myatt GSAF  NaN          NaN  NaN   \n",
+              "4         Kevin McMurray Trackingsharks.com: NY Post  NaN          NaN  NaN   \n",
+              "\n",
+              "  Case Number Case Number.1 original order Unnamed: 21 Unnamed: 22  \n",
+              "0         NaN           NaN            NaN         NaN         NaN  \n",
+              "1         NaN           NaN            NaN         NaN         NaN  \n",
+              "2         NaN           NaN            NaN         NaN         NaN  \n",
+              "3         NaN           NaN            NaN         NaN         NaN  \n",
+              "4         NaN           NaN            NaN         NaN         NaN  \n",
+              "\n",
+              "[5 rows x 23 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2c777cdc-0a2c-41a0-b9ac-e8aabd18856a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Type</th>\n",
+              "      <th>Country</th>\n",
+              "      <th>State</th>\n",
+              "      <th>Location</th>\n",
+              "      <th>Activity</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Sex</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Species</th>\n",
+              "      <th>Source</th>\n",
+              "      <th>pdf</th>\n",
+              "      <th>href formula</th>\n",
+              "      <th>href</th>\n",
+              "      <th>Case Number</th>\n",
+              "      <th>Case Number.1</th>\n",
+              "      <th>original order</th>\n",
+              "      <th>Unnamed: 21</th>\n",
+              "      <th>Unnamed: 22</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>16th August 2025</td>\n",
+              "      <td>2025.0</td>\n",
+              "      <td>Provoked</td>\n",
+              "      <td>USA</td>\n",
+              "      <td>Florida</td>\n",
+              "      <td>Cayo Costa Boca Grande</td>\n",
+              "      <td>Fishing</td>\n",
+              "      <td>Shawn Meuse</td>\n",
+              "      <td>M</td>\n",
+              "      <td>?</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Lemon shark 1.8 m (6ft)</td>\n",
+              "      <td>Johannes Marchand: Kevin McMurray Trackingshar...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18th August</td>\n",
+              "      <td>2025.0</td>\n",
+              "      <td>Unprovoked</td>\n",
+              "      <td>Australia</td>\n",
+              "      <td>NSW</td>\n",
+              "      <td>Cabarita Beach</td>\n",
+              "      <td>Surfing</td>\n",
+              "      <td>Brad Ross</td>\n",
+              "      <td>M</td>\n",
+              "      <td>?</td>\n",
+              "      <td>...</td>\n",
+              "      <td>5m (16.5ft) Great White</td>\n",
+              "      <td>Bob Myatt GSAF The Guardian: 9 News: ABS News:...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>17th August</td>\n",
+              "      <td>2025.0</td>\n",
+              "      <td>Unprovoked</td>\n",
+              "      <td>Bahamas</td>\n",
+              "      <td>Atlantic Ocean near Big Grand Cay</td>\n",
+              "      <td>North of Grand Bahama near Freeport</td>\n",
+              "      <td>Spearfishing</td>\n",
+              "      <td>Not stated</td>\n",
+              "      <td>M</td>\n",
+              "      <td>63</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Undetermined</td>\n",
+              "      <td>Ralph Collier GSAF and Kevin MCMurray Tracking...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7th August</td>\n",
+              "      <td>2025.0</td>\n",
+              "      <td>Unprovoked</td>\n",
+              "      <td>Australia</td>\n",
+              "      <td>NSW</td>\n",
+              "      <td>Tathra Beach</td>\n",
+              "      <td>Surfing</td>\n",
+              "      <td>Bowie Daley</td>\n",
+              "      <td>M</td>\n",
+              "      <td>9</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Suspected Great White</td>\n",
+              "      <td>Bob Myatt GSAF</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1st August</td>\n",
+              "      <td>2025.0</td>\n",
+              "      <td>Unprovoked</td>\n",
+              "      <td>Puerto Rico</td>\n",
+              "      <td>Carolina</td>\n",
+              "      <td>Carolina Beach</td>\n",
+              "      <td>Wading</td>\n",
+              "      <td>Eleonora Boi</td>\n",
+              "      <td>F</td>\n",
+              "      <td>39</td>\n",
+              "      <td>...</td>\n",
+              "      <td>Undetermined</td>\n",
+              "      <td>Kevin McMurray Trackingsharks.com: NY Post</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 23 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2c777cdc-0a2c-41a0-b9ac-e8aabd18856a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2c777cdc-0a2c-41a0-b9ac-e8aabd18856a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2c777cdc-0a2c-41a0-b9ac-e8aabd18856a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6bbb877c-a938-4cc0-9946-2f1da2ae643c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6bbb877c-a938-4cc0-9946-2f1da2ae643c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "    90% {\n",
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+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6bbb877c-a938-4cc0-9946-2f1da2ae643c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "sharks"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3. Clean the 'Year' Variable\n",
+        "\n",
+        "df['Year'] = pd.to_numeric(df['Year'], errors='coerce')\n",
+        "print(\"Year range:\", df['Year'].min(), \"to\", df['Year'].max())\n",
+        "\n",
+        "# Filter for attacks since 1940\n",
+        "df = df[df['Year'] >= 1940]\n",
+        "\n",
+        "# Plot attacks over time\n",
+        "attacks_by_year = df['Year'].value_counts().sort_index()\n",
+        "attacks_by_year.plot(kind='line', figsize=(10,5))\n",
+        "plt.title(\"Shark Attacks Since 1940\")\n",
+        "plt.xlabel(\"Year\")\n",
+        "plt.ylabel(\"Number of Attacks\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "id": "dlgfO3WwtYxS",
+        "outputId": "c4c9f66d-f5c9-4a3c-97b7-e8d81aad0852"
+      },
+      "id": "dlgfO3WwtYxS",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'df' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3589014280.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 3. Clean the 'Year' Variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Year range:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3. The 'Year' variable ranges from 0 to 2026, with a mean of 1935.6 and a standard deviation of 271.2. After filtering to include only attacks since 1940, the data show that shark attacks generally increase over time, although part of this trend may be due to imporved reporting and more people engaging in water activities over recent years."
+      ],
+      "metadata": {
+        "id": "WDtte9XVw5dx"
+      },
+      "id": "WDtte9XVw5dx"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 4. Clean the 'Age' Variable and make a histogram\n",
+        "\n",
+        "#Convert variable to numeric\n",
+        "sharks['Age'] = pd.to_numeric(sharks['Age'], errors='coerce')\n",
+        "\n",
+        "# Plot histogram\n",
+        "plt.hist(sharks['Age'].dropna(), bins=20, edgecolor='black')\n",
+        "plt.title(\"Histogram of Victim Ages\")\n",
+        "plt.xlabel(\"Age\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "JY0MmjE_tYu6",
+        "outputId": "9914241f-faa6-4afc-90c8-5b1bd83e7d7d"
+      },
+      "id": "JY0MmjE_tYu6",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 5. Calculate the proportion of victims that are male\n",
+        "\n",
+        "sharks['Sex']=sharks['Sex'].str.strip().str.upper() #this is just meant to standardize the data\n",
+        "#I had to do this because without it, the calculation was not correct\n",
+        "\n",
+        "prop_male=(sharks['Sex']=='M').mean()\n",
+        "print(\"Proportion of male victims:\",prop_male)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "upjOlk23tYsU",
+        "outputId": "9dc90b4b-3fc4-4a0c-829f-f9884b0b7dcd"
+      },
+      "id": "upjOlk23tYsU",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion of male victims: 0.8026128940641863\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "5. The proportion of male victims is 80.26%"
+      ],
+      "metadata": {
+        "id": "RRFD90vQymoI"
+      },
+      "id": "RRFD90vQymoI"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 6.Clean the Type Variable\n",
+        "\n",
+        "#Standardize all of the text\n",
+        "sharks['Type']=sharks['Type'].str.strip().str.capitalize()\n",
+        "\n",
+        "#Keep only the Provoked and Unprovoked categories and make everything else unknown\n",
+        "sharks['Type_clean']=sharks['Type'].where(\n",
+        "    sharks['Type'].isin(['Provoked','Unprovoked']),\n",
+        "    'Unknown'\n",
+        ")\n",
+        "\n",
+        "#Proportion of unprovoked attacks\n",
+        "prop_unprovoked=(sharks['Type_clean']=='Unprovoked').mean()\n",
+        "print('Proportion of Unprovoked Attacks:',prop_unprovoked)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-GMvnQYbymO7",
+        "outputId": "3fcc4240-7798-4b53-dfaf-f06d2ce85f22"
+      },
+      "id": "-GMvnQYbymO7",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion of Unprovoked Attacks: 0.7380005680204488\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "6. The proportion of unprovoked attacks is 73.8%"
+      ],
+      "metadata": {
+        "id": "wQl_Gj820XPS"
+      },
+      "id": "wQl_Gj820XPS"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 7. Clean the Fatal Y/N Variable\n",
+        "\n",
+        "#Standardize the text\n",
+        "sharks['Fatal_YN'] = sharks['Fatal Y/N'].str.strip().str.upper()\n",
+        "\n",
+        "# Keep only Y/N, everything else = Unknown\n",
+        "sharks['Fatal_clean'] = sharks['Fatal_YN'].where(\n",
+        "    sharks['Fatal_YN'].isin(['Y','N']),\n",
+        "    'Unknown'\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "58VuDSd8tYnC"
+      },
+      "id": "58VuDSd8tYnC",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 8. Unprovoked attacks by gender\n",
+        "\n",
+        "#Standard the variable\n",
+        "sharks['Sex'] = sharks['Sex'].str.strip().str.upper()\n",
+        "\n",
+        "# Cross-tabulation\n",
+        "unprovoked_by_sex = pd.crosstab(\n",
+        "    sharks['Type_clean'],\n",
+        "    sharks['Sex']\n",
+        ")\n",
+        "print(unprovoked_by_sex)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cwr8wbD2tYfi",
+        "outputId": "f7d3e464-d80e-4d41-e1d7-803f019708a7"
+      },
+      "id": "cwr8wbD2tYfi",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sex         .    F  LLI     M  M X 2  N\n",
+            "Type_clean                             \n",
+            "Provoked    0   31    0   550      0  1\n",
+            "Unknown     1   92    0   757      1  1\n",
+            "Unprovoked  0  683    1  4345      0  0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "8. Sharks are morely likely to launch unrpovoked attacks on men than women. Sharks are far more likely to launch unprovoked attacks on men than on women, with men making up around 80% of victims, likely due to greater exposure through activities such as surfing, diving, and fishing. When comparing outcomes, attacks are more likely to be fatal when provoked, since these usually involve close contact with the shark, while unprovoked attacks are more common overall but less often deadly. Fatality rates are also slightly higher among men than women, though this mostly reflects the fact that men are more frequently victims of the attacks. Looking at species, about 17–20% of recorded attacks are attributed to white sharks, making them one of the most frequent species involved. For the most part, sharks tend to attack only when there is an incentive involve and I have heard that sharks (at the worst) will take a \"bite\" out of a human to figure out what it is and then immediately dislike the taste; hence, I would say I feel pretty neutral - swimming at the beach is a pretty optional choice so I would say it is an avoidable threat if you are truly concerned."
+      ],
+      "metadata": {
+        "id": "dtR81yVP2u6W"
+      },
+      "id": "dtR81yVP2u6W"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Make sure the Species column is strings\n",
+        "species = df_time['Species '].dropna().astype(str).str.lower()\n",
+        "\n",
+        "# Split into words\n",
+        "split_species = species.str.split()\n",
+        "\n",
+        "# Flatten safely into one long list of words\n",
+        "all_words = [word for sublist in split_species for word in sublist]\n",
+        "\n",
+        "# Count all words\n",
+        "word_counts = pd.Series(all_words).value_counts()\n",
+        "\n",
+        "# Proportion of \"white\"\n",
+        "white_count = word_counts.get('white', 0)\n",
+        "total_words = word_counts.sum()\n",
+        "white_proportion = white_count / total_words\n",
+        "\n",
+        "print(\"Most common words:\\n\", word_counts.head(20))\n",
+        "print(\"\\nProportion of 'white':\", white_proportion)\n"
+      ],
+      "metadata": {
+        "id": "W-5ytTrA2elt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "outputId": "352c8239-40fd-4458-835a-9ce9130a54a4"
+      },
+      "id": "W-5ytTrA2elt",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'df_time' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-4071283946.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Make sure the Species column is strings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mspecies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_time\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Species '\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Split into words\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0msplit_species\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'df_time' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "EPstX8ust1oc"
+      },
+      "id": "EPstX8ust1oc",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/assignment_knn.ipynb b/assignment_knn.ipynb
new file mode 100644
index 0000000..a77f597
--- /dev/null
+++ b/assignment_knn.ipynb
@@ -0,0 +1,752 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/assignment_knn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f7ef20f0-722f-4240-8a79-437d4a3b8832",
+      "metadata": {
+        "id": "f7ef20f0-722f-4240-8a79-437d4a3b8832"
+      },
+      "source": [
+        "## Assignment 3: $k$ Nearest Neighbor\n",
+        "\n",
+        "`! git clone https://github.com/ds3001f25/knn_assignment.git`\n",
+        "\n",
+        "**Do two questions in total: \"Q1+Q2\" or \"Q1+Q3\"**\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5d9212c0",
+      "metadata": {
+        "id": "5d9212c0"
+      },
+      "source": [
+        "**Q1.**\n",
+        "1. What is the difference between regression and classification?\n",
+        "2. What is a confusion table? What does it help us understand about a model's performance?\n",
+        "3. What does the SSE quantify about a particular model?\n",
+        "4. What are overfitting and underfitting?\n",
+        "5. Why does splitting the data into training and testing sets, and choosing $k$ by evaluating accuracy or SSE on the test set, improve model performance?\n",
+        "6. With classification, we can report a class label as a prediction or a probability distribution over class labels. Please explain the strengths and weaknesses of each approach."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. Regression is used when the output variable is continuous and numerical, such as predicting stock prices, temperatures, or sales figures. For example, in predicting a house price, regression will output a specific value. On the other hand, classification is used when the output variable is categorical, meaning it belongs to one of several classes or categories. For instance, classifying emails as \"spam\" or \"not spam,\" or predicting whether a patient has a certain disease (yes or no).\n",
+        "\n",
+        "2. A confusion table is used to evaluate classification models by comparing predicted labels to true labels. It includes four metrics: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN). The confusion matrix helps identify where the model makes errors and assess performance balance across classes. Metrics like precision, recall, F1-score, and accuracy are derived from it.\n",
+        "\n",
+        "3. SSE quantifies the total error in a model’s predictions by summing the squared differences between the observed values (real data) and the predicted values (model’s output). The larger the SSE, the worse the model is at fitting the data. SSE is commonly used in regression tasks to assess the fit of the model to the data, with lower values indicating better model performance. It penalizes larger errors more heavily due to the squaring of difference.\n",
+        "\n",
+        "4. Overfitting occurs when the model learns the training data too well, including noise and outliers, and performs very well on training data but poorly on unseen test data. It means the model has become too complex and is not generalizing well to new data. Likewise, underfitting happens when the model is too simple to capture the underlying patterns in the data. It may have high bias and may not perform well even on training data, let alone test data. Underfitting often indicates that the model is not complex enough to capture the true relationships between variables.\n",
+        "\n",
+        "5. Splitting the data into training and testing sets is crucial for evaluating a model’s generalizability. The training set is used to fit the model, and the testing set is used to assess its performance on unseen data. This helps to detect overfitting. Evaluating performance, such as accuracy or SSE, on the test set provides an unbiased estimate of how the model will perform in real-world scenarios. Selecting the optimal model and parameters (like k in k-nearest neighbors) based on test set performance ensures the model is not just memorizing the data (overfitting) but can generalize well.\n",
+        "\n",
+        "6. Class label prediction provides a clear, binary or multiclass output where the model assigns one specific class to an observation. This is more intuitive but doesn’t capture the uncertainty in the model’s decision-making process.\n",
+        "Meanwhile, probability distribution outputs probabilities for each class, offering insight into the model's uncertainty about its predictions. For example, a classifier might predict that an email is 80% likely to be spam and 20% likely to be non-spam. This approach can be more informative for decision-making, especially when the cost of making an incorrect decision varies depending on the class probabilities. However, it is more complex and might be harder to interpret, especially for non-technical users."
+      ],
+      "metadata": {
+        "id": "DRBStuxW3oZQ"
+      },
+      "id": "DRBStuxW3oZQ"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "194455fa",
+      "metadata": {
+        "id": "194455fa"
+      },
+      "source": [
+        "**Q2.** This question is a case study for $k$ nearest neighbor regression, using the `USA_cars_datasets.csv` data.\n",
+        "\n",
+        "The target variable `y` is `price` and the features are `year` and `mileage`.\n",
+        "\n",
+        "1. Load the `./data/USA_cars_datasets.csv`. Keep the following variables and drop the rest: `price`, `year`, `mileage`. Are there any `NA`'s to handle? Look at the head and dimensions of the data.\n",
+        "2. Maxmin normalize `year` and `mileage`.\n",
+        "3. Split the sample into ~80% for training and ~20% for evaluation.\n",
+        "4. Use the $k$NN algorithm and the training data to predict `price` using `year` and `mileage` for the test set for $k=3,10,25,50,100,300$. For each value of $k$, compute the mean squared error and print a scatterplot showing the test value plotted against the predicted value. What patterns do you notice as you increase $k$?\n",
+        "5. Determine the optimal $k$ for these data.\n",
+        "6. Describe what happened in the plots of predicted versus actual prices as $k$ varied, taking your answer into part 6 into account. (Hint: Use the words \"underfitting\" and \"overfitting\".)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "739292a2",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "739292a2",
+        "outputId": "80c63792-aef7-4c0a-99e8-c9c6bd9b5985"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Cloning into 'knn_assignment'...\n",
+            "remote: Enumerating objects: 9, done.\u001b[K\n",
+            "remote: Counting objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (2/2), done.\u001b[K\n",
+            "remote: Total 9 (delta 0), reused 0 (delta 0), pack-reused 7 (from 1)\u001b[K\n",
+            "Receiving objects: 100% (9/9), 854.33 KiB | 6.47 MiB/s, done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Cloned the assignment\n",
+        "! git clone https://github.com/ds3001f25/knn_assignment.git"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.1: Load the Data\n",
+        "\n",
+        "#Import packages\n",
+        "import pandas as pd\n",
+        "#Upload files\n",
+        "from google.colab import files\n",
+        "uploaded = files.upload()\n",
+        "\n",
+        "#Read the file\n",
+        "df = pd.read_csv('./USA_cars_datasets.csv')\n",
+        "\n",
+        "#Keep only the specified columns and drop others\n",
+        "df = df[['price', 'year', 'mileage']]\n",
+        "\n",
+        "#Check for missing values (NA's)\n",
+        "na_check = df.isna().sum()\n",
+        "\n",
+        "#Check the dimensions and shape\n",
+        "df_head = df.head()\n",
+        "df_dimensions = df.shape\n",
+        "\n",
+        "#Print out\n",
+        "print(\"DataFrame Head:\\n\", df_head)\n",
+        "print(\"\\nDataFrame Dimensions:\", df_dimensions)\n",
+        "print(\"\\nMissing Values (NA's):\\n\", na_check)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "R3MHMM4g4hj6",
+        "outputId": "bd0a418d-d534-41ec-a620-b601a4ea477b"
+      },
+      "id": "R3MHMM4g4hj6",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-462cd2e4-9d82-4781-ba6f-cc7ff04dd9b4\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-462cd2e4-9d82-4781-ba6f-cc7ff04dd9b4\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving USA_cars_datasets.csv to USA_cars_datasets (2).csv\n",
+            "DataFrame Head:\n",
+            "    price  year  mileage\n",
+            "0   6300  2008   274117\n",
+            "1   2899  2011   190552\n",
+            "2   5350  2018    39590\n",
+            "3  25000  2014    64146\n",
+            "4  27700  2018     6654\n",
+            "\n",
+            "DataFrame Dimensions: (2499, 3)\n",
+            "\n",
+            "Missing Values (NA's):\n",
+            " price      0\n",
+            "year       0\n",
+            "mileage    0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.1:\n",
+        "\n",
+        "There are no more NAs to handle!"
+      ],
+      "metadata": {
+        "id": "pS0Ubqel5pek"
+      },
+      "id": "pS0Ubqel5pek"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.2: Maxmin Year and Mileage\n",
+        "\n",
+        "#Max-Min normalization using formala for \"year\" and \"mileage\" variables\n",
+        "df['year'] = (df['year'] - df['year'].min()) / (df['year'].max() - df['year'].min())\n",
+        "df['mileage'] = (df['mileage'] - df['mileage'].min()) / (df['mileage'].max() - df['mileage'].min())\n",
+        "\n",
+        "#Check the first few rows and make sure the dimensions look okay for everything\n",
+        "df_head = df.head()\n",
+        "df_dimensions = df.shape\n",
+        "\n",
+        "#Print out\n",
+        "print(\"DataFrame Head:\\n\", df_head)\n",
+        "print(\"\\nDataFrame Dimensions:\", df_dimensions)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "foPlvuL55GsS",
+        "outputId": "adf1b565-bffd-485a-e46c-b66080c92b16"
+      },
+      "id": "foPlvuL55GsS",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "DataFrame Head:\n",
+            "    price      year   mileage\n",
+            "0   6300  0.744681  0.269287\n",
+            "1   2899  0.808511  0.187194\n",
+            "2   5350  0.957447  0.038892\n",
+            "3  25000  0.872340  0.063016\n",
+            "4  27700  0.957447  0.006537\n",
+            "\n",
+            "DataFrame Dimensions: (2499, 3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.3: Split Sample\n",
+        "\n",
+        "#Import the packages\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "#Split the dataset into training (80%) and evaluation (20%) sets\n",
+        "train_df, eval_df = train_test_split(df, test_size=0.2, random_state=42)\n",
+        "\n",
+        "#Display the dimensions and shapes\n",
+        "print(\"Training Set Dimensions:\", train_df.shape)\n",
+        "print(\"Evaluation Set Dimensions:\", eval_df.shape)\n",
+        "\n",
+        "#Print out\n",
+        "print(\"\\nTraining Set Head:\\n\", train_df.head())\n",
+        "print(\"\\nEvaluation Set Head:\\n\", eval_df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wQVACgGi6Zlf",
+        "outputId": "0d307c27-22ce-425e-e565-972f6412d0a2"
+      },
+      "id": "wQVACgGi6Zlf",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training Set Dimensions: (1999, 3)\n",
+            "Evaluation Set Dimensions: (500, 3)\n",
+            "\n",
+            "Training Set Head:\n",
+            "       price      year   mileage\n",
+            "109   23100  0.957447  0.048624\n",
+            "2296   8000  0.957447  0.053728\n",
+            "354       0  0.765957  0.177186\n",
+            "266   17100  0.978723  0.015502\n",
+            "2102  16300  0.957447  0.068256\n",
+            "\n",
+            "Evaluation Set Head:\n",
+            "       price      year   mileage\n",
+            "2319  11390  0.893617  0.037284\n",
+            "1865  27900  0.978723  0.028412\n",
+            "902    2500  0.851064  0.160383\n",
+            "2240  10900  0.978723  0.030490\n",
+            "1285  23600  0.978723  0.013229\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.4: K\n",
+        "\n",
+        "#Import some more packages\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.neighbors import KNeighborsRegressor\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "\n",
+        "#Prepare training and evaluation data\n",
+        "X_train = train_df[['year', 'mileage']]\n",
+        "y_train = train_df['price']\n",
+        "X_test = eval_df[['year', 'mileage']]\n",
+        "y_test = eval_df['price']\n",
+        "\n",
+        "#These are the values of k to test\n",
+        "k_values = [3, 10, 25, 50, 100, 30]\n",
+        "\n",
+        "#Initialize lists to store the MSE values and predictions for each k\n",
+        "mse_values = []\n",
+        "predictions = []\n",
+        "\n",
+        "#Loop over the different k values\n",
+        "for k in k_values:\n",
+        "    #Create the model + Train\n",
+        "    knn = KNeighborsRegressor(n_neighbors=k)\n",
+        "    knn.fit(X_train, y_train)\n",
+        "\n",
+        "    #Make predictions on the test set\n",
+        "    y_pred = knn.predict(X_test)\n",
+        "\n",
+        "    #Compute the mean squared error\n",
+        "    mse = mean_squared_error(y_test, y_pred)\n",
+        "    mse_values.append(mse)\n",
+        "    predictions.append(y_pred)\n",
+        "\n",
+        "    #Plot the actual vs predicted values for each k\n",
+        "    plt.figure(figsize=(6, 6))\n",
+        "    plt.scatter(y_test, y_pred, label=f'k={k}')\n",
+        "    plt.title(f\"Test vs Predicted (k={k})\")\n",
+        "    plt.xlabel(\"Actual Price\")\n",
+        "    plt.ylabel(\"Predicted Price\")\n",
+        "    plt.plot([min(y_test), max(y_test)], [min(y_test), max(y_test)], color='red', linestyle='--')  # 45-degree line\n",
+        "    plt.legend()\n",
+        "    plt.show()\n",
+        "\n",
+        "#Print the MSE values for each k\n",
+        "for k, mse in zip(k_values, mse_values):\n",
+        "    print(f'Mean Squared Error for k={k}: {mse}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ji2Fl6Vj7H11",
+        "outputId": "330929e8-b0a3-4757-faa9-839d0b4a2e44"
+      },
+      "id": "ji2Fl6Vj7H11",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Squared Error for k=3: 148065935.51955554\n",
+            "Mean Squared Error for k=10: 117399126.10604002\n",
+            "Mean Squared Error for k=25: 112576160.82390079\n",
+            "Mean Squared Error for k=50: 110202549.3014296\n",
+            "Mean Squared Error for k=100: 112253932.8184272\n",
+            "Mean Squared Error for k=30: 111841978.69289334\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.4:\n",
+        "\n",
+        "Something I noticed that was that as I increased the value of k in k-NN, the model becomes smoother, reducing overfitting for smaller values of k. However, for very large k, the model starts to underfit by oversmoothing the predictions, leading to less accurate results. The optimal k strikes a balance between capturing the data's patterns while avoiding both overfitting and underfitting.\n"
+      ],
+      "metadata": {
+        "id": "4MpjJ6_g7pfI"
+      },
+      "id": "4MpjJ6_g7pfI"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Question 2.5: Optimal K\n",
+        "\n",
+        "#The best method that I had in mind here was checking the Mean Squared Error for each k in the test set\n",
+        "#Here the approach I used was that the k with the lowest MSE was the most optimal k [but I am not sure if this is completely correct!!]\n",
+        "# Store the MSE values for each k\n",
+        "optimal_k = None\n",
+        "lowest_mse = float('inf')\n",
+        "\n",
+        "#Loop over the different k values\n",
+        "for k in k_values:\n",
+        "    #Create + train model\n",
+        "    knn = KNeighborsRegressor(n_neighbors=k)\n",
+        "    knn.fit(X_train, y_train)\n",
+        "\n",
+        "    #Make predictions on the test set\n",
+        "    y_pred = knn.predict(X_test)\n",
+        "\n",
+        "    #Compute the mean squared error\n",
+        "    mse = mean_squared_error(y_test, y_pred)\n",
+        "\n",
+        "    #Update the optimal k if a lower MSE is found\n",
+        "    if mse < lowest_mse:\n",
+        "        lowest_mse = mse\n",
+        "        optimal_k = k\n",
+        "\n",
+        "#Print the optimal K + the corresponding MSE value\n",
+        "print(f\"The optimal value of k is {optimal_k} with a MSE of {lowest_mse}\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NBkGihQM8Sh5",
+        "outputId": "b1bbbe8d-f3f6-4bc8-899e-3d43dc5d95a0"
+      },
+      "id": "NBkGihQM8Sh5",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The optimal value of k is 50 with a MSE of 110202549.3014296\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Question 2.6:\n",
+        "\n",
+        "As the number of neighbors (k) in the K-nearest neighbors algorithm increases, the model transitions from overfitting to underfitting. For smaller values of k (k = 3), the model tends to overfit the training data, capturing noise and small fluctuations. This results in high variance, with predictions deviating significantly from the actual values, as seen in the scatter plots. As k increases, the model begins to generalize better, reducing the overfitting and bringing the predictions closer to the actual values. However, at larger values of k (k = 50 or 100), the model starts to underfit, becoming too simplistic and failing to capture the underlying patterns of the data, leading to higher bias. The optimal k value for this dataset appears to be around 50, where the model achieves the lowest MSE, balancing bias and variance effectively.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Mt2Yub6v88Rh"
+      },
+      "id": "Mt2Yub6v88Rh"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8d193de6",
+      "metadata": {
+        "vscode": {
+          "languageId": "plaintext"
+        },
+        "id": "8d193de6"
+      },
+      "source": [
+        "**Q3.** This is a case study on $k$ nearest neighbor regression and imputation, using the `airbnb_hw.csv` data.\n",
+        "\n",
+        "There are 30,478 observations, but only 22,155 ratings. We're going to build a kNN regressor to impute missing values. This is a common task, and illustrates one way you can use kNN in the future even when you have more advanced models available.\n",
+        "\n",
+        "1. Load the `airbnb_hw.csv` data with Pandas. We're only going to use `Review Scores Rating`, `Price`, and `Beds`, so use `.loc` to reduce the dataframe to those variables.\n",
+        "2. Set use `.isnull()` to select the subset of the dataframe with missing review values. Set those aside in a different dataframe. We'll make predictions about them later.\n",
+        "3. Use `df = df.dropna(axis = 0, how = 'any')` to eliminate any observations with missing values/NA's from the dataframe.\n",
+        "4. For the complete cases, create a $k$-NN model that uses the variables `Price` and `Beds` to predict `Review Scores Rating`. How do you choose $k$? (Hint: Train/test split, iterate over reasonable values of $k$ and find a value that minimizes SSE on the test split using predictions from the training set.)\n",
+        "5. Predict the missing ratings.\n",
+        "6. Do a kernel density plot of the training ratings and the predicted missing ratings. Do they look similar or not? Explain why."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b362aa0d",
+      "metadata": {
+        "id": "b362aa0d"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    },
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/wrangling_assignment_hw.ipynb b/wrangling_assignment_hw.ipynb
new file mode 100644
index 0000000..e6b1fb8
--- /dev/null
+++ b/wrangling_assignment_hw.ipynb
@@ -0,0 +1,2876 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/mrunalkute/DS-Machine-Learning-Assignments/blob/Homework/wrangling_assignment_hw.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "13ad028b-72b7-43ed-aa78-96fd4e518040",
+      "metadata": {
+        "id": "13ad028b-72b7-43ed-aa78-96fd4e518040"
+      },
+      "source": [
+        "# Assignment: Data Wrangling\n",
+        "### `! git clone https://github.com/ds3001f25/wrangling_assignment.git`\n",
+        "### Do Q1 and Q2\n",
+        "### Reading material: `tidy_data.pdf`"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "da879ea7-8aac-48a3-b6c2-daea56d2e072",
+      "metadata": {
+        "id": "da879ea7-8aac-48a3-b6c2-daea56d2e072"
+      },
+      "source": [
+        "**Q1.** This question provides some practice cleaning variables which have common problems.\n",
+        "1. Numeric variable: For `./data/airbnb_hw.csv`, clean the `Price` variable as well as you can, and explain the choices you make. How many missing values do you end up with? (Hint: What happens to the formatting when a price goes over 999 dollars, say from 675 to 1,112?)\n",
+        "2. Categorical variable: For the Minnesota police use of for data, `./data/mn_police_use_of_force.csv`, clean the `subject_injury` variable, handling the NA's; this gives a value `Yes` when a person was injured by police, and `No` when no injury occurred. What proportion of the values are missing? Is this a concern? Cross-tabulate your cleaned `subject_injury` variable with the `force_type` variable. Are there any patterns regarding when the data are missing?\n",
+        "3. Dummy variable: For the pretrial data covered in the lecture `./data/justice_data.parquet`, clean the `WhetherDefendantWasReleasedPretrial` variable as well as you can, and, in particular, replace missing values with `np.nan`.\n",
+        "4. Missing values, not at random: For the pretrial data covered in the lecture, clean the `ImposedSentenceAllChargeInContactEvent` variable as well as you can, and explain the choices you make. (Hint: Look at the `SentenceTypeAllChargesAtConvictionInContactEvent` variable.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9d412a8d",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 90
+        },
+        "id": "9d412a8d",
+        "outputId": "16c11c28-2cc6-4ee3-aae0-6daa6207f168"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c6d540c3-1c71-48a2-94f8-2a850b5e4b5c\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c6d540c3-1c71-48a2-94f8-2a850b5e4b5c\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving airbnb_hw.csv to airbnb_hw (1).csv\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['airbnb_hw (1).csv'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "#Import pandas\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "uploaded.keys()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ltJN3M8UH82S",
+        "outputId": "3daf1de4-5f39-4b0c-d6d3-4c25e416df63"
+      },
+      "id": "ltJN3M8UH82S",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['airbnb_hw (1).csv'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "#Read the \"airbnb\" csv\n",
+        "airbnb=pd.read_csv('airbnb_hw.csv')\n",
+        "airbnb.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 469
+        },
+        "id": "B8ikkvZ-ICPZ",
+        "outputId": "bdb7afb3-d0ed-4b26-9cdd-5607de6dc48e"
+      },
+      "id": "B8ikkvZ-ICPZ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Host Id Host Since                                Name Neighbourhood   \\\n",
+              "0   5162530        NaN     1 Bedroom in Prime Williamsburg       Brooklyn   \n",
+              "1  33134899        NaN     Sunny, Private room in Bushwick       Brooklyn   \n",
+              "2  39608626        NaN                Sunny Room in Harlem      Manhattan   \n",
+              "3       500  6/26/2008  Gorgeous 1 BR with Private Balcony      Manhattan   \n",
+              "4       500  6/26/2008            Trendy Times Square Loft      Manhattan   \n",
+              "\n",
+              "  Property Type  Review Scores Rating (bin)        Room Type  Zipcode  Beds  \\\n",
+              "0     Apartment                         NaN  Entire home/apt  11249.0   1.0   \n",
+              "1     Apartment                         NaN     Private room  11206.0   1.0   \n",
+              "2     Apartment                         NaN     Private room  10032.0   1.0   \n",
+              "3     Apartment                         NaN  Entire home/apt  10024.0   3.0   \n",
+              "4     Apartment                        95.0     Private room  10036.0   3.0   \n",
+              "\n",
+              "   Number of Records  Number Of Reviews Price  Review Scores Rating  \n",
+              "0                  1                  0   145                   NaN  \n",
+              "1                  1                  1    37                   NaN  \n",
+              "2                  1                  1    28                   NaN  \n",
+              "3                  1                  0   199                   NaN  \n",
+              "4                  1                 39   549                  96.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Host Id</th>\n",
+              "      <th>Host Since</th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Neighbourhood</th>\n",
+              "      <th>Property Type</th>\n",
+              "      <th>Review Scores Rating (bin)</th>\n",
+              "      <th>Room Type</th>\n",
+              "      <th>Zipcode</th>\n",
+              "      <th>Beds</th>\n",
+              "      <th>Number of Records</th>\n",
+              "      <th>Number Of Reviews</th>\n",
+              "      <th>Price</th>\n",
+              "      <th>Review Scores Rating</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>5162530</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1 Bedroom in Prime Williamsburg</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>11249.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>145</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>33134899</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny, Private room in Bushwick</td>\n",
+              "      <td>Brooklyn</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>11206.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>37</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>39608626</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Sunny Room in Harlem</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10032.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>28</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Gorgeous 1 BR with Private Balcony</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Entire home/apt</td>\n",
+              "      <td>10024.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>199</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>500</td>\n",
+              "      <td>6/26/2008</td>\n",
+              "      <td>Trendy Times Square Loft</td>\n",
+              "      <td>Manhattan</td>\n",
+              "      <td>Apartment</td>\n",
+              "      <td>95.0</td>\n",
+              "      <td>Private room</td>\n",
+              "      <td>10036.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>39</td>\n",
+              "      <td>549</td>\n",
+              "      <td>96.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-52f4fdac-f2cb-4821-a8c6-257ccb058f2d');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-02487396-5e31-4380-b79b-258d7648b742\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-02487396-5e31-4380-b79b-258d7648b742')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-02487396-5e31-4380-b79b-258d7648b742 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "airbnb",
+              "summary": "{\n  \"name\": \"airbnb\",\n  \"rows\": 30478,\n  \"fields\": [\n    {\n      \"column\": \"Host Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11902702,\n        \"min\": 500,\n        \"max\": 43033067,\n        \"num_unique_values\": 24421,\n        \"samples\": [\n          24093114,\n          16925490,\n          2886652\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Host Since\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2008-06-26 00:00:00\",\n        \"max\": \"2015-08-31 00:00:00\",\n        \"num_unique_values\": 2240,\n        \"samples\": [\n          \"5/20/2010\",\n          \"7/2/2009\",\n          \"4/18/2015\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 29413,\n        \"samples\": [\n          \"Explore the beauty of Brooklyn\",\n          \"Sunny Room in Williamsburg Loft\",\n          \"cozy one bedroom in lower east side\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Neighbourhood \",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Manhattan\",\n          \"Staten Island\",\n          \"Queens\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Property Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 19,\n        \"samples\": [\n          \"Apartment\",\n          \"Condominium\",\n          \"Bungalow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating (bin)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.05951861814779,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          40.0,\n          20.0,\n          95.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Room Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Entire home/apt\",\n          \"Private room\",\n          \"Shared room\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zipcode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 921.2993969568683,\n        \"min\": 1003.0,\n        \"max\": 99135.0,\n        \"num_unique_values\": 188,\n        \"samples\": [\n          11414.0,\n          11239.0,\n          11365.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0153587174802678,\n        \"min\": 0.0,\n        \"max\": 16.0,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          12.0,\n          16.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Records\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number Of Reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 0,\n        \"max\": 257,\n        \"num_unique_values\": 205,\n        \"samples\": [\n          171\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 511,\n        \"samples\": [\n          \"299\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Review Scores Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.850373136231884,\n        \"min\": 20.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          58.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check for the column names\n",
+        "airbnb.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NTXjatS8Iy3n",
+        "outputId": "164a171c-2b70-424e-8f94-bb413c2a0483"
+      },
+      "id": "NTXjatS8Iy3n",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['Host Id', 'Host Since', 'Name', 'Neighbourhood ', 'Property Type',\n",
+              "       'Review Scores Rating (bin)', 'Room Type', 'Zipcode', 'Beds',\n",
+              "       'Number of Records', 'Number Of Reviews', 'Price',\n",
+              "       'Review Scores Rating'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check the shape\n",
+        "airbnb.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "i7N6IXwzI0a2",
+        "outputId": "cd9b636c-bfd3-4854-bad4-c8d4d06bc950"
+      },
+      "id": "i7N6IXwzI0a2",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(30478, 13)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check for missing values\n",
+        "airbnb.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 491
+        },
+        "id": "mHiB9FGlI6mj",
+        "outputId": "7cb79e51-fcba-409e-8ec5-65853b54d10f"
+      },
+      "id": "mHiB9FGlI6mj",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Host Id                          0\n",
+              "Host Since                       3\n",
+              "Name                             0\n",
+              "Neighbourhood                    0\n",
+              "Property Type                    3\n",
+              "Review Scores Rating (bin)    8323\n",
+              "Room Type                        0\n",
+              "Zipcode                        134\n",
+              "Beds                            85\n",
+              "Number of Records                0\n",
+              "Number Of Reviews                0\n",
+              "Price                            0\n",
+              "Review Scores Rating          8323\n",
+              "dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Host Id</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Host Since</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Name</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Neighbourhood</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Property Type</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Review Scores Rating (bin)</th>\n",
+              "      <td>8323</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Room Type</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Zipcode</th>\n",
+              "      <td>134</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Beds</th>\n",
+              "      <td>85</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Number of Records</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Number Of Reviews</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Price</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Review Scores Rating</th>\n",
+              "      <td>8323</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#This line of code removes any $ and then converts to numeric values\n",
+        "airbnb['Price_clean'] = pd.to_numeric(\n",
+        "    airbnb['Price'].replace('[\\\\$,]', '', regex=True),\n",
+        "    errors='coerce'\n",
+        ")\n",
+        "\n",
+        "#This line of code counts all of the missing values\n",
+        "missing_prices=airbnb['Price_clean'].isna().sum()\n",
+        "print(\"Missing values in Price:\", missing_prices)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gt9R0D6AJ9I1",
+        "outputId": "55df4839-4329-4e93-8240-003fbf6fb2f2"
+      },
+      "id": "gt9R0D6AJ9I1",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing values in Price: 0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "1. I cleaned the Price variable by removing dollar sign symbols and commas, then converting the values to numeric. This ensures that prices above $999 are correctly interpreted as numbers rather than strings. Any entries that could not be converted (blanks or text) were set to missing (NaN). After cleaning, the number of missing values in Price is 0."
+      ],
+      "metadata": {
+        "id": "5lTNuV-9Ll9Z"
+      },
+      "id": "5lTNuV-9Ll9Z"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#For question #2\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "FyJbVs15NThZ",
+        "outputId": "43ea349a-56ac-4df6-9ff6-c81dac4d7bff"
+      },
+      "id": "FyJbVs15NThZ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-a289b6e6-41ea-45c4-b8b8-3f849f19aba8\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-a289b6e6-41ea-45c4-b8b8-3f849f19aba8\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving mn_police_use_of_force.csv to mn_police_use_of_force.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Read the CSV\n",
+        "mn_police=pd.read_csv('mn_police_use_of_force.csv')"
+      ],
+      "metadata": {
+        "id": "zP185C3-NTGp"
+      },
+      "id": "zP185C3-NTGp",
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mn_police.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6wH20sCXJ9GV",
+        "outputId": "9f1fe214-39cf-4008-c372-90e2ef42f814"
+      },
+      "id": "6wH20sCXJ9GV",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['response_datetime', 'problem', 'is_911_call', 'primary_offense',\n",
+              "       'subject_injury', 'force_type', 'force_type_action', 'race', 'sex',\n",
+              "       'age', 'type_resistance', 'precinct', 'neighborhood'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Shows all of the unique and how many are missing\n",
+        "mn_police['subject_injury'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "bmPMw2FeJ9Dd",
+        "outputId": "0bb31a89-1428-4f07-fdb9-6eed6a02d503"
+      },
+      "id": "bmPMw2FeJ9Dd",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "subject_injury\n",
+              "NaN    9848\n",
+              "Yes    1631\n",
+              "No     1446\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>subject_injury</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>NaN</th>\n",
+              "      <td>9848</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Yes</th>\n",
+              "      <td>1631</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>No</th>\n",
+              "      <td>1446</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Any value not listed in the map becomes NaN and is stored in a new column\n",
+        "#'subject_injury_clean'\n",
+        "mn_police['subject_injury_clean']=mn_police['subject_injury'].map({\n",
+        "    'Yes':'Yes',\n",
+        "    'No':'No'\n",
+        "})\n",
+        "\n",
+        "#Calculate the proportion of missing values in column + print\n",
+        "missing_prop=mn_police['subject_injury_clean'].isna().mean()\n",
+        "print ('Proportion missing values in column:',missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MSKkXK0KJ9AV",
+        "outputId": "fbf708c8-8a44-4e61-a337-3b85c6cfb736"
+      },
+      "id": "MSKkXK0KJ9AV",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing values in column: 0.7619342359767892\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Crosstabulation\n",
+        "\n",
+        "cross_tab=pd.crosstab(\n",
+        "    mn_police['subject_injury_clean'],\n",
+        "    mn_police['force_type'],\n",
+        "    dropna=False\n",
+        ")\n",
+        "\n",
+        "print(cross_tab)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wz_aBbi8hqXQ",
+        "outputId": "ce94506e-3faf-4cb2-9195-111bf31d5344"
+      },
+      "id": "wz_aBbi8hqXQ",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "force_type            Baton  Bodily Force  Chemical Irritant  Firearm  \\\n",
+            "subject_injury_clean                                                    \n",
+            "No                        0          1093                131        2   \n",
+            "Yes                       2          1286                 41        0   \n",
+            "NaN                       2          7051               1421        0   \n",
+            "\n",
+            "force_type            Gun Point Display  Improvised Weapon  Less Lethal  \\\n",
+            "subject_injury_clean                                                      \n",
+            "No                                   33                 34            0   \n",
+            "Yes                                  44                 40            0   \n",
+            "NaN                                  27                 74           87   \n",
+            "\n",
+            "force_type            Less Lethal Projectile  Maximal Restraint Technique  \\\n",
+            "subject_injury_clean                                                        \n",
+            "No                                         1                            0   \n",
+            "Yes                                        2                            0   \n",
+            "NaN                                        0                          170   \n",
+            "\n",
+            "force_type            Police K9 Bite  Taser  \n",
+            "subject_injury_clean                         \n",
+            "No                                 2    150  \n",
+            "Yes                               44    172  \n",
+            "NaN                               31    985  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "2. I cleaned the 'subject_injury' variable by mapping all entries to 'Yes' if the person was injured and 'No' if not, keeping all of the missing entries NaN. The proportion of missing values is approximately 76% (which comes from the calculation we did earlier). 76% is definitely a concern because missingness is substantial and non-random. A cross-tabulation of 'subject_injury_clean' with 'force_type' shows that missing data occur disproportionately for common force types while rare or severe force types have fewer missing values. From this analysis, it can be implied that missing values might NOT be completely random and could be biased if not addressed properly."
+      ],
+      "metadata": {
+        "id": "XTcG_uN5iP6P"
+      },
+      "id": "XTcG_uN5iP6P"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#For question #3\n",
+        "from google.colab import files #Google colab because I could not figure out GitHub Desktop\n",
+        "uploaded = files.upload()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "ZO8EWs0vJ89F",
+        "outputId": "f214a76c-58e5-4859-b1c0-f287b3f3dbae"
+      },
+      "id": "ZO8EWs0vJ89F",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f4f1111f-38e4-491c-8de7-9b9d0dfca731\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f4f1111f-38e4-491c-8de7-9b9d0dfca731\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving justice_data.parquet to justice_data (1).parquet\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Read the file\n",
+        "justice=pd.read_parquet('justice_data.parquet')\n",
+        "#Check for unique values and missing values\n",
+        "justice['WhetherDefendantWasReleasedPretrial'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "VsYswhDKJ81W",
+        "outputId": "9f63294f-6601-4b03-b5d2-908f8b8200f7"
+      },
+      "id": "VsYswhDKJ81W",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "WhetherDefendantWasReleasedPretrial\n",
+              "1    19154\n",
+              "0     3801\n",
+              "9       31\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>WhetherDefendantWasReleasedPretrial</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>19154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 57
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['WhetherDefendantWasReleasedPretrial'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cKoogsYBpm2b",
+        "outputId": "c4b967af-f780-495a-89f9-ee0469081bd5"
+      },
+      "id": "cKoogsYBpm2b",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([9, 0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Map 'Yes' becomes 1 and 'No becoems' 0 and replace anything missing\n",
+        "justice['ReleasedPretrial_clean'] = justice['WhetherDefendantWasReleasedPretrial'].replace({\n",
+        "    1: 1,\n",
+        "    0: 0,\n",
+        "    9: np.nan\n",
+        "}).astype('float')  # ensures NaN is possible\n",
+        "\n",
+        "# Check counts\n",
+        "justice['ReleasedPretrial_clean'].value_counts(dropna=False)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "efg9vCNsJ8rO",
+        "outputId": "7b9826e2-d604-4371-d3be-d4a901b80632"
+      },
+      "id": "efg9vCNsJ8rO",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ReleasedPretrial_clean\n",
+              "1.0    19154\n",
+              "0.0     3801\n",
+              "NaN       31\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ReleasedPretrial_clean</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1.0</th>\n",
+              "      <td>19154</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0.0</th>\n",
+              "      <td>3801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NaN</th>\n",
+              "      <td>31</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Calculate the proportion missing after cleaning\n",
+        "missing_prop=justice['ReleasedPretrial_clean'].isna().mean()\n",
+        "print(\"Proportion missing values:\", missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fTKrpMhypLxz",
+        "outputId": "875425b2-8ff1-45b0-978f-99d7e0f4e022"
+      },
+      "id": "fTKrpMhypLxz",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing values: 0.0013486470025232751\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3. I cleaned the 'WhetherDefendantWasReleasedPretrial' variable by mapping 1 to indicate the defendant was released pretrial, 0 to indicate the defendant was not released, and 9 as placeholder for missing values to NaN. This created the numeric dummy variable 'ReleasedPretrial_clean' which was suitable for analysis. After cleaning, the proportion f missing values was approximately 0.135% (based of the calculation we made)."
+      ],
+      "metadata": {
+        "id": "lz3wbTwIqA7r"
+      },
+      "id": "lz3wbTwIqA7r"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['ImposedSentenceAllChargeInContactEvent'].value_counts(dropna=False)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "UForialFpLvb",
+        "outputId": "058d2f53-5415-45bd-d4db-853393ff79a4"
+      },
+      "id": "UForialFpLvb",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ImposedSentenceAllChargeInContactEvent\n",
+              "                    9053\n",
+              "0                   4953\n",
+              "12                  1404\n",
+              ".985626283367556    1051\n",
+              "6                    809\n",
+              "                    ... \n",
+              "11.9055441478439       1\n",
+              "35.0061601642711       1\n",
+              "46.6242299794661       1\n",
+              "81.0225872689938       1\n",
+              "202                    1\n",
+              "Name: count, Length: 484, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ImposedSentenceAllChargeInContactEvent</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <td>9053</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>4953</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>1404</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>.985626283367556</th>\n",
+              "      <td>1051</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>809</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11.9055441478439</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>35.0061601642711</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46.6242299794661</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>81.0225872689938</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>202</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>484 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.crosstab(\n",
+        "    justice['ImposedSentenceAllChargeInContactEvent'].isna(),\n",
+        "    justice['SentenceTypeAllChargesAtConvictionInContactEvent']\n",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "9Uqc6wU7pLs8",
+        "outputId": "35caa79c-f25a-427f-ad2d-8354ab163bde"
+      },
+      "id": "9Uqc6wU7pLs8",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SentenceTypeAllChargesAtConvictionInContactEvent     0     1    2     4    9\n",
+              "ImposedSentenceAllChargeInContactEvent                                      \n",
+              "False                                             8720  4299  914  8779  274"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-692e14be-7b80-4561-a780-933f4da80259\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
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+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>SentenceTypeAllChargesAtConvictionInContactEvent</th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>4</th>\n",
+              "      <th>9</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ImposedSentenceAllChargeInContactEvent</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>False</th>\n",
+              "      <td>8720</td>\n",
+              "      <td>4299</td>\n",
+              "      <td>914</td>\n",
+              "      <td>8779</td>\n",
+              "      <td>274</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-692e14be-7b80-4561-a780-933f4da80259')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-692e14be-7b80-4561-a780-933f4da80259 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-692e14be-7b80-4561-a780-933f4da80259');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \")\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"ImposedSentenceAllChargeInContactEvent\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8720,\n        \"max\": 8720,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8720\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4299,\n        \"max\": 4299,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 914,\n        \"max\": 914,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8779,\n        \"max\": 8779,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8779\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 9,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 274,\n        \"max\": 274,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          274\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "justice['ImposedSentence_clean']=pd.to_numeric(\n",
+        "    justice['ImposedSentenceAllChargeInContactEvent'],\n",
+        "    errors='coerce' #anything that is invalid becomes NaN\n",
+        ")\n",
+        "\n",
+        "missing_prop=justice['ImposedSentence_clean'].isna().mean()\n",
+        "print('Proportion missing:', missing_prop)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dWzVHISRpLqk",
+        "outputId": "e872857c-1d60-442e-8691-00775e600097"
+      },
+      "id": "dWzVHISRpLqk",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion missing: 0.3938484294788132\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "4. I cleaned the 'ImposedSentenceAllChargeInContactEvent' variable by converting it to numeric and leaving the missing entries as NaN. Missing values typically occur when the defendant was not convicted as indicated by the 'SentenceTypeAllChargesAtConvinctionInContactEvent' variable. After cleaning, the proportion of missing values is 39% which is reasonable given that not all defendants recieved a sentence"
+      ],
+      "metadata": {
+        "id": "-SEitsnWs18C"
+      },
+      "id": "-SEitsnWs18C"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5a60a44e",
+      "metadata": {
+        "id": "5a60a44e"
+      },
+      "source": [
+        "**Q2.** Go to https://sharkattackfile.net/ and download their dataset on shark attacks (Hint: `GSAF5.xls`).\n",
+        "\n",
+        "1. Open the shark attack file using Pandas. It is probably not a csv file, so `read_csv` won't work.\n",
+        "2. Drop any columns that do not contain data.\n",
+        "3. Clean the year variable. Describe the range of values you see. Filter the rows to focus on attacks since 1940. Are attacks increasing, decreasing, or remaining constant over time?\n",
+        "4. Clean the Age variable and make a histogram of the ages of the victims.\n",
+        "5. What proportion of victims are male?\n",
+        "6. Clean the `Type` variable so it only takes three values: Provoked and Unprovoked and Unknown. What proportion of attacks are unprovoked?\n",
+        "7. Clean the `Fatal Y/N` variable so it only takes three values: Y, N, and Unknown.\n",
+        "8. Are sharks more likely to launch unprovoked attacks on men or women? Is the attack more or less likely to be fatal when the attack is provoked or unprovoked? Is it more or less likely to be fatal when the victim is male or female? How do you feel about sharks?\n",
+        "9. What proportion of attacks appear to be by white sharks? (Hint: `str.split()` makes a vector of text values into a list of lists, split by spaces.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a3f03830",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "a3f03830",
+        "outputId": "dab48cd3-cf56-4dd4-c977-a9706d6ba1ba"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f189ae9f-d0ed-4d98-9659-7032ae907255\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f189ae9f-d0ed-4d98-9659-7032ae907255\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving GSAF5.xls to GSAF5.xls\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#It is an excel file so we will just have to use pd.read_excel\n",
+        "from google.colab import files\n",
+        "uploaded=files.upload() #upload the GSAF5 file here"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = pd.read_excel('GSAF5.xls')\n",
+        "#I want to see the data frame\n",
+        "print(df.head())\n",
+        "print(df.info())\n",
+        "#Drop the empty rows\n",
+        "df = df.dropna(axis=1, how='all')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yrVqLFZTtY4j",
+        "outputId": "a27c72b7-2e6d-43e8-8cde-8fcd585e1dfe"
+      },
+      "id": "yrVqLFZTtY4j",
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "               Date    Year        Type      Country  \\\n",
+            "0  16th August 2025  2025.0    Provoked          USA   \n",
+            "1       18th August  2025.0  Unprovoked    Australia   \n",
+            "2       17th August  2025.0  Unprovoked      Bahamas   \n",
+            "3        7th August  2025.0  Unprovoked    Australia   \n",
+            "4        1st August  2025.0  Unprovoked  Puerto Rico   \n",
+            "\n",
+            "                               State                             Location  \\\n",
+            "0                            Florida               Cayo Costa Boca Grande   \n",
+            "1                                NSW                       Cabarita Beach   \n",
+            "2  Atlantic Ocean near Big Grand Cay  North of Grand Bahama near Freeport   \n",
+            "3                                NSW                        Tathra Beach    \n",
+            "4                           Carolina                       Carolina Beach   \n",
+            "\n",
+            "       Activity          Name Sex Age  ...                 Species   \\\n",
+            "0       Fishing   Shawn Meuse   M   ?  ...  Lemon shark 1.8 m (6ft)   \n",
+            "1       Surfing     Brad Ross   M   ?  ...  5m (16.5ft) Great White   \n",
+            "2  Spearfishing    Not stated   M  63  ...             Undetermined   \n",
+            "3       Surfing   Bowie Daley   M   9  ...    Suspected Great White   \n",
+            "4        Wading  Eleonora Boi   F  39  ...             Undetermined   \n",
+            "\n",
+            "                                              Source  pdf href formula href  \\\n",
+            "0  Johannes Marchand: Kevin McMurray Trackingshar...  NaN          NaN  NaN   \n",
+            "1  Bob Myatt GSAF The Guardian: 9 News: ABS News:...  NaN          NaN  NaN   \n",
+            "2  Ralph Collier GSAF and Kevin MCMurray Tracking...  NaN          NaN  NaN   \n",
+            "3                                     Bob Myatt GSAF  NaN          NaN  NaN   \n",
+            "4         Kevin McMurray Trackingsharks.com: NY Post  NaN          NaN  NaN   \n",
+            "\n",
+            "  Case Number Case Number.1 original order Unnamed: 21 Unnamed: 22  \n",
+            "0         NaN           NaN            NaN         NaN         NaN  \n",
+            "1         NaN           NaN            NaN         NaN         NaN  \n",
+            "2         NaN           NaN            NaN         NaN         NaN  \n",
+            "3         NaN           NaN            NaN         NaN         NaN  \n",
+            "4         NaN           NaN            NaN         NaN         NaN  \n",
+            "\n",
+            "[5 rows x 23 columns]\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 7042 entries, 0 to 7041\n",
+            "Data columns (total 23 columns):\n",
+            " #   Column          Non-Null Count  Dtype  \n",
+            "---  ------          --------------  -----  \n",
+            " 0   Date            7042 non-null   object \n",
+            " 1   Year            7040 non-null   float64\n",
+            " 2   Type            7024 non-null   object \n",
+            " 3   Country         6992 non-null   object \n",
+            " 4   State           6557 non-null   object \n",
+            " 5   Location        6475 non-null   object \n",
+            " 6   Activity        6457 non-null   object \n",
+            " 7   Name            6823 non-null   object \n",
+            " 8   Sex             6463 non-null   object \n",
+            " 9   Age             4048 non-null   object \n",
+            " 10  Injury          7007 non-null   object \n",
+            " 11  Fatal Y/N       6481 non-null   object \n",
+            " 12  Time            3516 non-null   object \n",
+            " 13  Species         3911 non-null   object \n",
+            " 14  Source          7022 non-null   object \n",
+            " 15  pdf             6799 non-null   object \n",
+            " 16  href formula    6794 non-null   object \n",
+            " 17  href            6796 non-null   object \n",
+            " 18  Case Number     6798 non-null   object \n",
+            " 19  Case Number.1   6797 non-null   object \n",
+            " 20  original order  6799 non-null   float64\n",
+            " 21  Unnamed: 21     1 non-null      object \n",
+            " 22  Unnamed: 22     2 non-null      object \n",
+            "dtypes: float64(2), object(21)\n",
+            "memory usage: 1.2+ MB\n",
+            "None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 3. Clean the 'Year' Variable\n",
+        "df['Year'] = pd.to_numeric(df['Year'], errors='coerce')\n",
+        "print(\"Year summary:\")\n",
+        "print(df['Year'].describe())\n",
+        "#Filter the attacks since 1940 and show that\n",
+        "df_post1940 = df[df['Year'] >= 1940]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dlgfO3WwtYxS",
+        "outputId": "446a05ae-8bdb-4c34-a04f-7b8144339ba5"
+      },
+      "id": "dlgfO3WwtYxS",
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Year summary:\n",
+            "count    7040.000000\n",
+            "mean     1935.621449\n",
+            "std       271.221061\n",
+            "min         0.000000\n",
+            "25%      1948.000000\n",
+            "50%      1986.000000\n",
+            "75%      2010.000000\n",
+            "max      2026.000000\n",
+            "Name: Year, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "3. The 'Year' variable ranges from 0 to 2026, with a mean of 1935.6 and a standard deviation of 271.2. After filtering to include only attacks since 1940, the data show that shark attacks generally increase over time, although part of this trend may be due to imporved reporting and more people engaging in water activities over recent years."
+      ],
+      "metadata": {
+        "id": "WDtte9XVw5dx"
+      },
+      "id": "WDtte9XVw5dx"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 4. Clean the 'Age' Variable and make a histogram\n",
+        "\n",
+        "df_post1940['Age'] = pd.to_numeric(df_post1940['Age'], errors='coerce')\n",
+        "plt.hist(df_post1940['Age'].dropna(), bins=20, edgecolor='black')\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Number of Victims')\n",
+        "plt.title('Histogram of Shark Attack Victims Age')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 596
+        },
+        "id": "JY0MmjE_tYu6",
+        "outputId": "944e307b-752c-4638-dc0f-a64052f0c81d"
+      },
+      "id": "JY0MmjE_tYu6",
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-1248900290.py:3: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_post1940['Age'] = pd.to_numeric(df_post1940['Age'], errors='coerce')\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 5. Calculate the proportion of victims that are male\n",
+        "# Ensure column names are clean\n",
+        "df_post1940.columns = df_post1940.columns.str.strip()\n",
+        "\n",
+        "# Count male and female victims\n",
+        "male_count = df_post1940['Sex'].str.upper().value_counts().get('M', 0)\n",
+        "female_count = df_post1940['Sex'].str.upper().value_counts().get('F', 0)\n",
+        "total_known = male_count + female_count\n",
+        "\n",
+        "# Proportion of male victims\n",
+        "proportion_male = male_count / total_known\n",
+        "print(\"Proportion of male victims:\", proportion_male)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "upjOlk23tYsU",
+        "outputId": "c38ea7b3-a16d-42ed-af3f-6e4a14fe5019"
+      },
+      "id": "upjOlk23tYsU",
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion of male victims: 0.8577372696651476\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "5. The proportion of male victims is 85.77%"
+      ],
+      "metadata": {
+        "id": "RRFD90vQymoI"
+      },
+      "id": "RRFD90vQymoI"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 6.Clean the Type Variable\n",
+        "def clean_attack_type(x):\n",
+        "    if pd.isna(x):\n",
+        "        return 'Unknown'\n",
+        "    x = x.lower()\n",
+        "    if 'unprovoked' in x:\n",
+        "        return 'Unprovoked'\n",
+        "    elif 'provoked' in x:\n",
+        "        return 'Provoked'\n",
+        "    else:\n",
+        "        return 'Unknown'\n",
+        "\n",
+        "df_post1940['Type_clean'] = df_post1940['Type'].apply(clean_attack_type)\n",
+        "print(\"Proportion of unprovoked attacks:\", (df_post1940['Type_clean'] == 'Unprovoked').mean())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-GMvnQYbymO7",
+        "outputId": "ec112483-3c1b-4a07-9c66-07a67ea53e59"
+      },
+      "id": "-GMvnQYbymO7",
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Proportion of unprovoked attacks: 0.7441438169602325\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-389889844.py:13: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_post1940['Type_clean'] = df_post1940['Type'].apply(clean_attack_type)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "6. The proportion of unprovoked attacks is 74.41%"
+      ],
+      "metadata": {
+        "id": "wQl_Gj820XPS"
+      },
+      "id": "wQl_Gj820XPS"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Just to check column names\n",
+        "print (df_post1940.columns)\n",
+        "df_post1940.columns = df_post1940.columns.str.strip()"
+      ],
+      "metadata": {
+        "id": "58VuDSd8tYnC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "59be216a-48c0-43ec-816f-52497bc66fed"
+      },
+      "id": "58VuDSd8tYnC",
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Index(['Date', 'Year', 'Type', 'Country', 'State', 'Location', 'Activity',\n",
+            "       'Name', 'Sex', 'Age', 'Injury', 'Fatal Y/N', 'Time', 'Species',\n",
+            "       'Source', 'pdf', 'href formula', 'href', 'Case Number', 'Case Number.1',\n",
+            "       'original order', 'Unnamed: 21', 'Unnamed: 22', 'Type_clean'],\n",
+            "      dtype='object')\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-877399682.py:15: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_post1940['Fatal_clean'] = df_post1940['Fatal Y/N'].apply(clean_fatal)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 7. Clean the Fatal Y/N Variable\n",
+        "def clean_fatal(x):\n",
+        "    if pd.isna(x):\n",
+        "        return 'Unknown'\n",
+        "    x = str(x).upper().strip()  # convert to string, uppercase, remove spaces\n",
+        "    if x in ['Y', 'N']:\n",
+        "        return x\n",
+        "    return 'Unknown'\n",
+        "\n",
+        "df_post1940['Fatal_clean'] = df_post1940['Fatal Y/N'].apply(clean_fatal)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lRKbiiSK3Ee6",
+        "outputId": "c6b6f0b9-a9b9-4a05-b5d3-556677742d47"
+      },
+      "id": "lRKbiiSK3Ee6",
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-3445792209.py:9: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  df_post1940['Fatal_clean'] = df_post1940['Fatal Y/N'].apply(clean_fatal)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 8. Unprovoked attacks by gender\n",
+        "\n",
+        "print(\"Fatality by attack type:\")\n",
+        "print(pd.crosstab(df_post1940['Type_clean'], df_post1940['Fatal_clean'], normalize='index'))\n",
+        "\n",
+        "print(\"Fatality by sex:\")\n",
+        "print(pd.crosstab(df_post1940['Sex'].str.upper(), df_post1940['Fatal_clean'], normalize='index'))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cwr8wbD2tYfi",
+        "outputId": "139e9e56-f7fe-447d-c5f1-c718c6a6f891"
+      },
+      "id": "cwr8wbD2tYfi",
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Fatality by attack type:\n",
+            "Fatal_clean         N   Unknown         Y\n",
+            "Type_clean                               \n",
+            "Provoked     0.957692  0.017308  0.025000\n",
+            "Unknown      0.411699  0.449944  0.138358\n",
+            "Unprovoked   0.819180  0.012933  0.167887\n",
+            "Fatality by sex:\n",
+            "Fatal_clean         N   Unknown         Y\n",
+            "Sex                                      \n",
+            " M           1.000000  0.000000  0.000000\n",
+            "F            0.793872  0.082173  0.123955\n",
+            "F            1.000000  0.000000  0.000000\n",
+            "LLI          1.000000  0.000000  0.000000\n",
+            "M            0.773389  0.074613  0.151998\n",
+            "M            0.666667  0.000000  0.333333\n",
+            "M X 2        0.000000  1.000000  0.000000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "8. Sharks are morely likely to launch unrpovoked attacks on men than women. Sharks are far more likely to launch unprovoked attacks on men than on women, with men making up around 80% of victims, likely due to greater exposure through activities such as surfing, diving, and fishing. When comparing outcomes, attacks are more likely to be fatal when provoked, since these usually involve close contact with the shark, while unprovoked attacks are more common overall but less often deadly. Fatality rates are also slightly higher among men than women, though this mostly reflects the fact that men are more frequently victims of the attacks. Looking at species, about 17–20% of recorded attacks are attributed to white sharks, making them one of the most frequent species involved. For the most part, sharks tend to attack only when there is an incentive involve and I have heard that sharks (at the worst) will take a \"bite\" out of a human to figure out what it is and then immediately dislike the taste; hence, I would say I feel pretty neutral - swimming at the beach is a pretty optional choice so I would say it is an avoidable threat if you are truly concerned."
+      ],
+      "metadata": {
+        "id": "dtR81yVP2u6W"
+      },
+      "id": "dtR81yVP2u6W"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Proportion of attacks by white sharks\n",
+        "df_post1940.columns = df_post1940.columns.str.strip() #strip the whitespace\n",
+        "\n",
+        "#drop rows where species is missing\n",
+        "species_col = df_post1940['Species'].dropna()\n",
+        "\n",
+        "species_split = species_col.str.split()  # each row becomes a list of words\n",
+        "all_species_words = species_split.sum()  # flatten to a single list of all words\n",
+        "\n",
+        "all_species_series = pd.Series(all_species_words)\n",
+        "white_shark_count = all_species_series.str.contains('White', case=False).sum()\n",
+        "total_attacks = len(df_post1940)  # total number of attacks after 1940\n",
+        "proportion_white = white_shark_count / total_attacks\n",
+        "\n",
+        "print(\"Number of words with 'White':\", white_shark_count)\n",
+        "print(\"Total attacks:\", total_attacks)\n",
+        "print(\"Proportion of attacks by white sharks:\", proportion_white)"
+      ],
+      "metadata": {
+        "id": "W-5ytTrA2elt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a2c9ac6c-787b-46ac-b13a-3e3615b5e27f"
+      },
+      "id": "W-5ytTrA2elt",
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Number of words with 'White': 713\n",
+            "Total attacks: 5507\n",
+            "Proportion of attacks by white sharks: 0.12947158162338843\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The proportion of attacks by white sharks is 12.94%"
+      ],
+      "metadata": {
+        "id": "Odpn769W3_FL"
+      },
+      "id": "Odpn769W3_FL"
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "MgmYAsi04AWU"
+      },
+      "id": "MgmYAsi04AWU",
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.18"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file