diff --git a/Vanshika__241141__neural_network_Q1.ipynb b/Vanshika__241141__neural_network_Q1.ipynb
new file mode 100644
index 0000000..f11ebd7
--- /dev/null
+++ b/Vanshika__241141__neural_network_Q1.ipynb
@@ -0,0 +1,1459 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "x6l25t1pO3Uz"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "uploaded = files.upload()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "DkHdcroXT5gS",
+        "outputId": "3dbb3bd5-7a72-4ae8-f18b-91d3bb645751"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-65574d80-f01d-4ea2-9de5-43caea919b68\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-65574d80-f01d-4ea2-9de5-43caea919b68\">\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 Linear_Regression_dataset.csv to Linear_Regression_dataset.csv\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_df = pd.read_csv(\"Linear_Regression_dataset.csv\")\n",
+        "print(\"(No of rows, No of Columns) = \",data_df.shape)\n",
+        "data_df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 273
+        },
+        "id": "4d5EwXMfRnW3",
+        "outputId": "3017a784-0eaa-4ad1-e98d-1623a3bb65b5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(No of rows, No of Columns) =  (21613, 21)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
+              "0  7129300520  20141013T000000  221900.0         3       1.00         1180   \n",
+              "1  6414100192  20141209T000000  538000.0         3       2.25         2570   \n",
+              "2  5631500400  20150225T000000  180000.0         2       1.00          770   \n",
+              "3  2487200875  20141209T000000  604000.0         4       3.00         1960   \n",
+              "4  1954400510  20150218T000000  510000.0         3       2.00         1680   \n",
+              "\n",
+              "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
+              "0      5650     1.0           0     0  ...      7        1180              0   \n",
+              "1      7242     2.0           0     0  ...      7        2170            400   \n",
+              "2     10000     1.0           0     0  ...      6         770              0   \n",
+              "3      5000     1.0           0     0  ...      7        1050            910   \n",
+              "4      8080     1.0           0     0  ...      8        1680              0   \n",
+              "\n",
+              "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
+              "0      1955             0    98178  47.5112 -122.257           1340   \n",
+              "1      1951          1991    98125  47.7210 -122.319           1690   \n",
+              "2      1933             0    98028  47.7379 -122.233           2720   \n",
+              "3      1965             0    98136  47.5208 -122.393           1360   \n",
+              "4      1987             0    98074  47.6168 -122.045           1800   \n",
+              "\n",
+              "   sqft_lot15  \n",
+              "0        5650  \n",
+              "1        7639  \n",
+              "2        8062  \n",
+              "3        5000  \n",
+              "4        7503  \n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-68667537-59fa-406e-b315-f786da8bf143\" 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>id</th>\n",
+              "      <th>date</th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>...</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7129300520</td>\n",
+              "      <td>20141013T000000</td>\n",
+              "      <td>221900.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>5650</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1955</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98178</td>\n",
+              "      <td>47.5112</td>\n",
+              "      <td>-122.257</td>\n",
+              "      <td>1340</td>\n",
+              "      <td>5650</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6414100192</td>\n",
+              "      <td>20141209T000000</td>\n",
+              "      <td>538000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.25</td>\n",
+              "      <td>2570</td>\n",
+              "      <td>7242</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>2170</td>\n",
+              "      <td>400</td>\n",
+              "      <td>1951</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>98125</td>\n",
+              "      <td>47.7210</td>\n",
+              "      <td>-122.319</td>\n",
+              "      <td>1690</td>\n",
+              "      <td>7639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>5631500400</td>\n",
+              "      <td>20150225T000000</td>\n",
+              "      <td>180000.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>770</td>\n",
+              "      <td>10000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>6</td>\n",
+              "      <td>770</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1933</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98028</td>\n",
+              "      <td>47.7379</td>\n",
+              "      <td>-122.233</td>\n",
+              "      <td>2720</td>\n",
+              "      <td>8062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2487200875</td>\n",
+              "      <td>20141209T000000</td>\n",
+              "      <td>604000.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>5000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1050</td>\n",
+              "      <td>910</td>\n",
+              "      <td>1965</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98136</td>\n",
+              "      <td>47.5208</td>\n",
+              "      <td>-122.393</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1954400510</td>\n",
+              "      <td>20150218T000000</td>\n",
+              "      <td>510000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>8080</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1987</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98074</td>\n",
+              "      <td>47.6168</td>\n",
+              "      <td>-122.045</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>7503</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 21 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-68667537-59fa-406e-b315-f786da8bf143')\"\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-68667537-59fa-406e-b315-f786da8bf143 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-68667537-59fa-406e-b315-f786da8bf143');\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-e0202056-63a4-4d70-8b88-d6896245fb71\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0202056-63a4-4d70-8b88-d6896245fb71')\"\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-e0202056-63a4-4d70-8b88-d6896245fb71 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": "data_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_df['date'] = data_df['date'].str.replace('T000000', '')\n"
+      ],
+      "metadata": {
+        "id": "A-hfNH2QUwdS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_array = data_df.drop(columns=['id','date']).to_numpy()\n",
+        "assert (data_array.shape == (21613,19))\n",
+        "data_df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 256
+        },
+        "id": "LJQ21tkvW6S7",
+        "outputId": "4435993b-9068-47b4-8260-86df2bb74bae"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           id      date     price  bedrooms  bathrooms  sqft_living  sqft_lot  \\\n",
+              "0  7129300520  20141013  221900.0         3       1.00         1180      5650   \n",
+              "1  6414100192  20141209  538000.0         3       2.25         2570      7242   \n",
+              "2  5631500400  20150225  180000.0         2       1.00          770     10000   \n",
+              "3  2487200875  20141209  604000.0         4       3.00         1960      5000   \n",
+              "4  1954400510  20150218  510000.0         3       2.00         1680      8080   \n",
+              "\n",
+              "   floors  waterfront  view  ...  grade  sqft_above  sqft_basement  yr_built  \\\n",
+              "0     1.0           0     0  ...      7        1180              0      1955   \n",
+              "1     2.0           0     0  ...      7        2170            400      1951   \n",
+              "2     1.0           0     0  ...      6         770              0      1933   \n",
+              "3     1.0           0     0  ...      7        1050            910      1965   \n",
+              "4     1.0           0     0  ...      8        1680              0      1987   \n",
+              "\n",
+              "   yr_renovated  zipcode      lat     long  sqft_living15  sqft_lot15  \n",
+              "0             0    98178  47.5112 -122.257           1340        5650  \n",
+              "1          1991    98125  47.7210 -122.319           1690        7639  \n",
+              "2             0    98028  47.7379 -122.233           2720        8062  \n",
+              "3             0    98136  47.5208 -122.393           1360        5000  \n",
+              "4             0    98074  47.6168 -122.045           1800        7503  \n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1fb10b80-8cca-42a6-b137-072fb2fcb0b3\" 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>id</th>\n",
+              "      <th>date</th>\n",
+              "      <th>price</th>\n",
+              "      <th>bedrooms</th>\n",
+              "      <th>bathrooms</th>\n",
+              "      <th>sqft_living</th>\n",
+              "      <th>sqft_lot</th>\n",
+              "      <th>floors</th>\n",
+              "      <th>waterfront</th>\n",
+              "      <th>view</th>\n",
+              "      <th>...</th>\n",
+              "      <th>grade</th>\n",
+              "      <th>sqft_above</th>\n",
+              "      <th>sqft_basement</th>\n",
+              "      <th>yr_built</th>\n",
+              "      <th>yr_renovated</th>\n",
+              "      <th>zipcode</th>\n",
+              "      <th>lat</th>\n",
+              "      <th>long</th>\n",
+              "      <th>sqft_living15</th>\n",
+              "      <th>sqft_lot15</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7129300520</td>\n",
+              "      <td>20141013</td>\n",
+              "      <td>221900.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>5650</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1180</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1955</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98178</td>\n",
+              "      <td>47.5112</td>\n",
+              "      <td>-122.257</td>\n",
+              "      <td>1340</td>\n",
+              "      <td>5650</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6414100192</td>\n",
+              "      <td>20141209</td>\n",
+              "      <td>538000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.25</td>\n",
+              "      <td>2570</td>\n",
+              "      <td>7242</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>2170</td>\n",
+              "      <td>400</td>\n",
+              "      <td>1951</td>\n",
+              "      <td>1991</td>\n",
+              "      <td>98125</td>\n",
+              "      <td>47.7210</td>\n",
+              "      <td>-122.319</td>\n",
+              "      <td>1690</td>\n",
+              "      <td>7639</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>5631500400</td>\n",
+              "      <td>20150225</td>\n",
+              "      <td>180000.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1.00</td>\n",
+              "      <td>770</td>\n",
+              "      <td>10000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>6</td>\n",
+              "      <td>770</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1933</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98028</td>\n",
+              "      <td>47.7379</td>\n",
+              "      <td>-122.233</td>\n",
+              "      <td>2720</td>\n",
+              "      <td>8062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2487200875</td>\n",
+              "      <td>20141209</td>\n",
+              "      <td>604000.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>5000</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1050</td>\n",
+              "      <td>910</td>\n",
+              "      <td>1965</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98136</td>\n",
+              "      <td>47.5208</td>\n",
+              "      <td>-122.393</td>\n",
+              "      <td>1360</td>\n",
+              "      <td>5000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1954400510</td>\n",
+              "      <td>20150218</td>\n",
+              "      <td>510000.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>8080</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>...</td>\n",
+              "      <td>8</td>\n",
+              "      <td>1680</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1987</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98074</td>\n",
+              "      <td>47.6168</td>\n",
+              "      <td>-122.045</td>\n",
+              "      <td>1800</td>\n",
+              "      <td>7503</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 21 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-1fb10b80-8cca-42a6-b137-072fb2fcb0b3')\"\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-1fb10b80-8cca-42a6-b137-072fb2fcb0b3 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-1fb10b80-8cca-42a6-b137-072fb2fcb0b3');\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-01d17b70-88b2-4dc7-bc68-7abbd6bdfa9d\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-01d17b70-88b2-4dc7-bc68-7abbd6bdfa9d')\"\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-01d17b70-88b2-4dc7-bc68-7abbd6bdfa9d 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": "data_df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mean = np.mean(data_array,axis=0)\n",
+        "sd = np.std(data_array,axis=0)\n",
+        "\n",
+        "data_array_norm = (data_array - mean)/sd\n",
+        "\n",
+        "print(data_array_norm.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0_athC9wcaFX",
+        "outputId": "64a21e85-e94f-4c36-9ae4-1e5a3b167801"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(21613, 19)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "labels= data_df['price'].to_numpy()\n",
+        "x_array_norm = np.delete(data_array_norm, 0, axis=1)\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x_array_norm,labels,test_size=0.15,random_state=42,shuffle=True)    # splitting data into test and train set.\n",
+        "\n",
+        "print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IaV3z-EhhcTO",
+        "outputId": "7496b403-4c7d-44a3-8be4-17c8493d606d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(18371, 18) (3242, 18) (18371,) (3242,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def loss(y_pred,y_true):\n",
+        "  mse =np.mean(y_pred-y_true)**2\n",
+        "\n",
+        "  return mse"
+      ],
+      "metadata": {
+        "id": "KywUvhg8iDDu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def y(x,a,b):\n",
+        "\n",
+        "  m,n = x.shape\n",
+        "  y_pred = np.dot(x, a) + b\n",
+        "\n",
+        "  assert(y_pred.shape == (m,))\n",
+        "  return y_pred"
+      ],
+      "metadata": {
+        "id": "30dgoO0pkusI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def gradient(x,a,b,y_true):\n",
+        "  m,n = x.shape\n",
+        "  yp = y(x,a,b)\n",
+        "  error = y_true - yp\n",
+        "\n",
+        "  da = -2/m * np.dot(x.T, error)\n",
+        "  db = -2/m * np.sum(error)\n",
+        "\n",
+        "  assert(da.shape ==(n,))\n",
+        "  return (da,db)"
+      ],
+      "metadata": {
+        "id": "rTbIOnCqLd9J"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def gradient_descent(x, y_true, learning_rate=0.01, epochs=10):\n",
+        "    m, n = x.shape\n",
+        "    loss_mse = []\n",
+        "\n",
+        "    a = np.zeros(n)\n",
+        "    b = 0.0\n",
+        "\n",
+        "    for i in range(epochs):\n",
+        "\n",
+        "        da, db = gradient(x, a, b, y_true)\n",
+        "        a = a - learning_rate * da\n",
+        "        b = b - learning_rate * db\n",
+        "\n",
+        "        y_pred = y(x, a, b)\n",
+        "        l_mse = loss(y_pred, y_true)\n",
+        "\n",
+        "        loss_mse.append(l_mse)\n",
+        "\n",
+        "        print(\"Epoch\", i + 1, \"Completed! Loss =\", l_mse)\n",
+        "\n",
+        "    print(\"Training completed!!\")\n",
+        "\n",
+        "    assert a.shape == (n,)\n",
+        "    return (loss_mse, a, b)\n"
+      ],
+      "metadata": {
+        "id": "noyzrV-XdZea"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "labels= data_df['price'].to_numpy()\n",
+        "x_array_norm = np.delete(data_array_norm, 0, axis=1)\n",
+        "x_train, x_test, y_train, y_test = train_test_split(x_array_norm,labels,test_size=0.15,random_state=42,shuffle=True)    # splitting data into test and train set.\n",
+        "\n",
+        "print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)\n",
+        "\n",
+        "epochs = 100\n",
+        "learn_rate = 0.01\n",
+        "\n",
+        "train_loss, a, b = gradient_descent(x_train, y_train, learning_rate=learn_rate, epochs=epochs)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-gluSyh6Osey",
+        "outputId": "9ffc19f2-aa52-40b1-8797-400407c28e01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(18371, 18) (3242, 18) (18371,) (3242,)\n",
+            "Epoch 1 Completed! Loss = 278301740779.34436\n",
+            "Epoch 2 Completed! Loss = 267323275269.054\n",
+            "Epoch 3 Completed! Loss = 256775523937.92712\n",
+            "Epoch 4 Completed! Loss = 246641854544.26868\n",
+            "Epoch 5 Completed! Loss = 236906246020.03635\n",
+            "Epoch 6 Completed! Loss = 227553269783.47537\n",
+            "Epoch 7 Completed! Loss = 218568071137.42526\n",
+            "Epoch 8 Completed! Loss = 209936350828.36533\n",
+            "Epoch 9 Completed! Loss = 201644346828.8511\n",
+            "Epoch 10 Completed! Loss = 193678816395.1971\n",
+            "Epoch 11 Completed! Loss = 186027018442.90234\n",
+            "Epoch 12 Completed! Loss = 178676696274.2013\n",
+            "Epoch 13 Completed! Loss = 171616060685.10782\n",
+            "Epoch 14 Completed! Loss = 164833773473.26575\n",
+            "Epoch 15 Completed! Loss = 158318931362.70084\n",
+            "Epoch 16 Completed! Loss = 152061050357.09088\n",
+            "Epoch 17 Completed! Loss = 146050050529.32593\n",
+            "Epoch 18 Completed! Loss = 140276241251.8534\n",
+            "Epoch 19 Completed! Loss = 134730306869.50908\n",
+            "Epoch 20 Completed! Loss = 129403292814.16943\n",
+            "Epoch 21 Completed! Loss = 124286592158.56694\n",
+            "Epoch 22 Completed! Loss = 119371932604.93565\n",
+            "Epoch 23 Completed! Loss = 114651363902.76468\n",
+            "Epoch 24 Completed! Loss = 110117245688.78859\n",
+            "Epoch 25 Completed! Loss = 105762235741.40605\n",
+            "Epoch 26 Completed! Loss = 101579278640.96579\n",
+            "Epoch 27 Completed! Loss = 97561594826.75998\n",
+            "Epoch 28 Completed! Loss = 93702670041.10696\n",
+            "Epoch 29 Completed! Loss = 89996245150.56276\n",
+            "Epoch 30 Completed! Loss = 86436306334.05571\n",
+            "Epoch 31 Completed! Loss = 83017075627.58545\n",
+            "Epoch 32 Completed! Loss = 79733001815.04242\n",
+            "Epoch 33 Completed! Loss = 76578751654.68462\n",
+            "Epoch 34 Completed! Loss = 73549201430.84021\n",
+            "Epoch 35 Completed! Loss = 70639428820.48181\n",
+            "Epoch 36 Completed! Loss = 67844705064.43136\n",
+            "Epoch 37 Completed! Loss = 65160487433.10048\n",
+            "Epoch 38 Completed! Loss = 62582411976.84017\n",
+            "Epoch 39 Completed! Loss = 60106286551.16494\n",
+            "Epoch 40 Completed! Loss = 57728084107.324\n",
+            "Epoch 41 Completed! Loss = 55443936238.91123\n",
+            "Epoch 42 Completed! Loss = 53250126975.43771\n",
+            "Epoch 43 Completed! Loss = 51143086814.0254\n",
+            "Epoch 44 Completed! Loss = 49119386980.62512\n",
+            "Epoch 45 Completed! Loss = 47175733912.40659\n",
+            "Epoch 46 Completed! Loss = 45308963953.214676\n",
+            "Epoch 47 Completed! Loss = 43516038254.23415\n",
+            "Epoch 48 Completed! Loss = 41794037872.25023\n",
+            "Epoch 49 Completed! Loss = 40140159058.13632\n",
+            "Epoch 50 Completed! Loss = 38551708728.4409\n",
+            "Epoch 51 Completed! Loss = 37026100113.18321\n",
+            "Epoch 52 Completed! Loss = 35560848573.1999\n",
+            "Epoch 53 Completed! Loss = 34153567580.613575\n",
+            "Epoch 54 Completed! Loss = 32801964856.2172\n",
+            "Epoch 55 Completed! Loss = 31503838657.78705\n",
+            "Epoch 56 Completed! Loss = 30257074213.54888\n",
+            "Epoch 57 Completed! Loss = 29059640295.22947\n",
+            "Epoch 58 Completed! Loss = 27909585925.32693\n",
+            "Epoch 59 Completed! Loss = 26805037213.428257\n",
+            "Epoch 60 Completed! Loss = 25744194316.592846\n",
+            "Epoch 61 Completed! Loss = 24725328519.00393\n",
+            "Epoch 62 Completed! Loss = 23746779426.268562\n",
+            "Epoch 63 Completed! Loss = 22806952269.918293\n",
+            "Epoch 64 Completed! Loss = 21904315317.830227\n",
+            "Epoch 65 Completed! Loss = 21037397386.448677\n",
+            "Epoch 66 Completed! Loss = 20204785450.843227\n",
+            "Epoch 67 Completed! Loss = 19405122348.78983\n",
+            "Epoch 68 Completed! Loss = 18637104575.205948\n",
+            "Epoch 69 Completed! Loss = 17899480163.41129\n",
+            "Epoch 70 Completed! Loss = 17191046649.820152\n",
+            "Epoch 71 Completed! Loss = 16510649118.801943\n",
+            "Epoch 72 Completed! Loss = 15857178324.571426\n",
+            "Epoch 73 Completed! Loss = 15229568887.09171\n",
+            "Epoch 74 Completed! Loss = 14626797559.08861\n",
+            "Epoch 75 Completed! Loss = 14047881561.387838\n",
+            "Epoch 76 Completed! Loss = 13491876983.894085\n",
+            "Epoch 77 Completed! Loss = 12957877249.634987\n",
+            "Epoch 78 Completed! Loss = 12445011639.39322\n",
+            "Epoch 79 Completed! Loss = 11952443874.545923\n",
+            "Epoch 80 Completed! Loss = 11479370755.823639\n",
+            "Epoch 81 Completed! Loss = 11025020855.789782\n",
+            "Epoch 82 Completed! Loss = 10588653262.927616\n",
+            "Epoch 83 Completed! Loss = 10169556375.304\n",
+            "Epoch 84 Completed! Loss = 9767046741.85872\n",
+            "Epoch 85 Completed! Loss = 9380467949.444178\n",
+            "Epoch 86 Completed! Loss = 9009189553.81394\n",
+            "Epoch 87 Completed! Loss = 8652606052.828821\n",
+            "Epoch 88 Completed! Loss = 8310135900.217272\n",
+            "Epoch 89 Completed! Loss = 7981220558.291822\n",
+            "Epoch 90 Completed! Loss = 7665323588.086127\n",
+            "Epoch 91 Completed! Loss = 7361929775.437344\n",
+            "Epoch 92 Completed! Loss = 7070544291.596533\n",
+            "Epoch 93 Completed! Loss = 6790691887.00536\n",
+            "Epoch 94 Completed! Loss = 6521916116.930879\n",
+            "Epoch 95 Completed! Loss = 6263778597.7016735\n",
+            "Epoch 96 Completed! Loss = 6015858292.337903\n",
+            "Epoch 97 Completed! Loss = 5777750824.415447\n",
+            "Epoch 98 Completed! Loss = 5549067819.049739\n",
+            "Epoch 99 Completed! Loss = 5329436269.929012\n",
+            "Epoch 100 Completed! Loss = 5118497931.368559\n",
+            "Training completed!!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_test = y(x_test, a, b)\n",
+        "test_loss = loss(y_pred_test, y_test)\n",
+        "\n",
+        "print(\"Loss on test data =\", test_loss)\n",
+        "\n",
+        "plt.plot(train_loss)\n",
+        "plt.title(\"Training Loss\")\n",
+        "plt.xlabel(\"Epochs\")\n",
+        "plt.ylabel(\"Loss (MSE)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "Zx-4Rw4UPnA2",
+        "outputId": "247e5aab-3fd9-46f9-ae0c-98bd2107ad79"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loss on test data = 6228121125.238866\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Vanshika__241141__neural_network_part2.ipynb b/Vanshika__241141__neural_network_part2.ipynb
new file mode 100644
index 0000000..8ac5cec
--- /dev/null
+++ b/Vanshika__241141__neural_network_part2.ipynb
@@ -0,0 +1,339 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WHb-IJOUSivY"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mnist = keras.datasets.mnist\n",
+        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+        "print(\"No. of training examples = \",x_train.shape[0])\n",
+        "print(\"Size of each image in dataset = \",x_train.shape[1:])\n",
+        "print(\"No. of test examples = \",x_test.shape[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "jwfKOIqpSv5b",
+        "outputId": "fcbd876f-f172-4941-d382-65a9886ab9b9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
+            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n",
+            "No. of training examples =  60000\n",
+            "Size of each image in dataset =  (28, 28)\n",
+            "No. of test examples =  10000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n = 5\n",
+        "\n",
+        "index = np.random.choice(x_train.shape[0],5)\n",
+        "print(\"label: \",end=\"\")\n",
+        "\n",
+        "for i,ind in enumerate(index):\n",
+        "    plt.subplot(1,n,i+1)\n",
+        "    plt.imshow(x_train[ind],cmap=\"gray\")\n",
+        "    plt.axis(\"off\")\n",
+        "    print(y_train[ind],end=\"       \")\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 139
+        },
+        "id": "Gju72DOKS0cZ",
+        "outputId": "4756f983-120a-4d7d-ff76-ae923ab440d2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "label: 4       7       4       4       1       "
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 5 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train_flatten = x_train.reshape(x_train.shape[0], -1)\n",
+        "x_test_flatten = x_test.reshape(x_test.shape[0], -1)\n",
+        "n_validation = 10000\n",
+        "\n",
+        "x_validation = x_train_flatten[:n_validation]\n",
+        "y_validation = y_train[:n_validation]\n",
+        "\n",
+        "x_train_flatten = x_train_flatten[n_validation:]\n",
+        "y_train = y_train[n_validation:]\n",
+        "\n",
+        "print(\"Size of each image in dataset = \", x_train.shape[1:])\n",
+        "print(\"No. of training examples =\", x_train_flatten.shape[0])\n",
+        "print(\"No. of validation examples =\", x_validation.shape[0])\n",
+        "print(\"No. of test examples =\", x_test.shape[0])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t7x8-d9NTdhO",
+        "outputId": "05e0654e-0a58-4b66-b878-b5c99a614b4a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Size of each image in dataset =  (28, 28)\n",
+            "No. of training examples = 50000\n",
+            "No. of validation examples = 10000\n",
+            "No. of test examples = 10000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow import keras\n",
+        "from tensorflow.keras import layers\n",
+        "\n",
+        "model = keras.models.Sequential([\n",
+        "    layers.Input(shape=(784,)),\n",
+        "    layers.Dense(128, activation='relu'),\n",
+        "    layers.Dense(64, activation='relu'),\n",
+        "    layers.Dense(10, activation='softmax')\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "P_NzNjTxT-hI"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(\n",
+        "    optimizer=\"adam\",\n",
+        "    loss=\"sparse_categorical_crossentropy\",\n",
+        "    metrics=[\"accuracy\"]\n",
+        ")\n",
+        "\n",
+        "n_epochs = 10\n",
+        "batch_size = 512\n",
+        "\n",
+        "history = model.fit(\n",
+        "    x_train_flatten, y_train,\n",
+        "    epochs=n_epochs,\n",
+        "    batch_size=batch_size,\n",
+        "    validation_data=(x_validation, y_validation),\n",
+        "    verbose=1\n",
+        ")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 373
+        },
+        "id": "SW8SOcFGUpzL",
+        "outputId": "e47d3577-a2c4-4d1f-fb2a-9fba79254026"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "Data cardinality is ambiguous. Make sure all arrays contain the same number of samples.'x' sizes: 50000\n'y' sizes: 30000\n",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-9-1119672429.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m512\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m     11\u001b[0m     \u001b[0mx_train_flatten\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0;31m# To get the full stack trace, call:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m             \u001b[0;31m# `keras.config.disable_traceback_filtering()`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 122\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    123\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/keras/src/trainers/data_adapters/data_adapter_utils.py\u001b[0m in \u001b[0;36mcheck_data_cardinality\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    113\u001b[0m             )\n\u001b[1;32m    114\u001b[0m             \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34mf\"'{label}' sizes: {sizes}\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 115\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\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    116\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Data cardinality is ambiguous. Make sure all arrays contain the same number of samples.'x' sizes: 50000\n'y' sizes: 30000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = model.evaluate(x_test_flatten, y_test)\n",
+        "print(\"Loss = \", results[0])\n",
+        "print(\"Accuracy = \", results[1] * 100, \"%\")\n",
+        "\n",
+        "plt.plot(history.history['accuracy'], label=\"Training Accuracy\")\n",
+        "plt.plot(history.history['val_accuracy'], label=\"Validation Accuracy\")\n",
+        "plt.title(\"Model Accuracy\")\n",
+        "plt.xlabel(\"Epochs\")\n",
+        "plt.ylabel(\"Accuracy\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "plt.plot(history.history['loss'], label=\"Training Loss\")\n",
+        "plt.plot(history.history['val_loss'], label=\"Validation Loss\")\n",
+        "plt.title(\"Model Loss\")\n",
+        "plt.xlabel(\"Epochs\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 263
+        },
+        "id": "ZhDXSZt9VVzF",
+        "outputId": "eb134440-d322-4ba6-e171-88a755f8d997"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.0843 - loss: 107.0016\n",
+            "Loss =  112.7437744140625\n",
+            "Accuracy =  7.769999653100967 %\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'history' 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-7-2864823823.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\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      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Training Accuracy\"\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      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Validation Accuracy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model Accuracy\"\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 'history' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "n = 5\n",
+        "\n",
+        "index = np.random.choice(x_test.shape[0], n)\n",
+        "\n",
+        "print(\"Actual Labels: \")\n",
+        "for i, ind in enumerate(index):\n",
+        "    plt.subplot(1, n, i + 1)\n",
+        "    plt.imshow(x_test[ind], cmap=\"gray\")\n",
+        "    plt.axis(\"off\")\n",
+        "    print(y_test[ind], end=\"       \")\n",
+        "\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"\\nPredicted Labels: \")\n",
+        "\n",
+        "for i, ind in enumerate(index):\n",
+        "\n",
+        "    img = x_test[ind].reshape(1, 784)\n",
+        "\n",
+        "    prediction = model.predict(img, verbose=0)\n",
+        "\n",
+        "    digit = np.argmax(prediction)\n",
+        "\n",
+        "    print(digit, end=\"       \")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "rcz7kRhPV2BI",
+        "outputId": "03c5a807-3859-4ff6-e9c6-db0ee8afe43e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Actual Labels: \n",
+            "4       2       5       9       6       "
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 5 Axes>"
+            ],
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAABpCAYAAABF9zs7AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAC9JJREFUeJzt3WdoFNsbx/GxcTEWJAo27BW7iBWxoWLvRl9YQLHHLlFUUIkIIYotsRAloNiDoqK+ULCgqC8siILYu5FgL7Ek+n/x5z4+yd3RmczszOzu9/Pql3Wze5bNXM59njnnFPv169cvAwAAxLTifg8AAAD4jwkBAABgQgAAAJgQAAAAgwkBAAAwmBAAAACDCQEAADCYEAAAAMMwSlp9YrFixcI5jpjlxr5QfDfh4fS74XsJD66Z4OKaCSar3wsVAgAAwIQAAAAwIQAAAAYTAgAAYDAhAAAABhMCAABgMCEAAAAGEwIAAGDY2JgIQGw4ffq05G7duknu3r275DNnzng4IiB6LV++XHLXrl1Nn7dixQrJ4br+qBAAAAAmBAAAgJYBXBQXFye5V69ekrt06fLX37169apkXbJ+8eKFS6PDn5i1CTTaBEDR6etKX29WnT17VjItAwAAEDZMCAAAAC0DOFOuXDnJmZmZkocMGSJZH2lq5RjOnJwcyc2aNZP8+vXrog4Tf2HWJtB0mVOvODB7nWXLlpm+ltnvA9HE6XHQujXgRcuOCgEAAGBCAAAAIqRlMHLkSMk7d+6U3K5dO8k3btzwdEz4v6ZNm0rWbQLtwIEDkvWqgf3790tu3bq15LS0tJC/q/8OaB84Z6VNYPZ8s7uk7b4mfpszZ47klJQUyaVKlZKs22+bNm2SvGjRIskfP34M0whhxmqrLBTdCtCbDxX+Ny9QIQAAAEwIAABAgFsG9erVkzxhwgTJT548keykbKxLnm/evJE8fPjwIr9mLMrOzpZ87tw5yRkZGZL37Nnz19e5dOmS5J8/f0pOT0+XnJCQIHnz5s32B4sC7JY2taK0BtjY6L90myA5OVmyvjv96dOnkuPj4yVPmzZNct26dSX37dvX7WHiL/S1ZOXa0K0BfZaB36gQAAAAJgQAAIAJAQAAMAyj2C+LWynp5S7hUqZMGckHDx6UrA/K0T3lmTNn2nr9Tp06Sdb97hMnTkgeOHCgrdd0yulOVobhzXfjpRo1akh+9OiR5GPHjkkeNmyY5Ly8vLCMw+l3E8TvxcrSwcJLn/5l956Dwq/jVq800q8Z3ePft2+f5LJly0qeMmWKZH0/Tv/+/SVnZWVJLlny9+1gqampkhcvXuzCiK2LxmvGjJXParak0Ov7aax+L1QIAAAAEwIAABCwlsGaNWskz507V7Iur0yePFnyvXv3bL1+ly5dQr7mzZs3Jbdo0cLWazoV6eXPcNAtg4cPH0q+ePGi5N69e0vOzc0Nyziisfxp9pn09WB28JCT33VTJF4zlSpVkvz48WPJpUuXlpyUlCRZ/7fQ7PO2bNlS8rVr1yTrZbvVq1eX/OrVK7vDti0arxlNt9msLC8MyuehZQAAACxjQgAAAPzdqXDMmDEFfk5MTJT84MEDyU7aBIg8o0ePDvn48+fPJYerTRCNzFYTaFZK/UEpf0ai2bNnS9ZtAn2Q15YtWyRbKfHm5+eHfLx48d//n8d35pxuDVhpE3jRNgsXKgQAAIAJAQAA8LllMHjw4AI/63O/jx49KjncbQK9MRH8t3TpUr+HEPHsljn1cziEyH1t2rQJ+fjWrVslf/r0yavhwAa7G3KZteiCsknRn1AhAAAATAgAAIDPLYPbt2+b/pvey/v169eSt2/fLvnly5e23q9x48YhH8/Ozrb1OnBHXFycZL2ne7ly5STru63Pnj3rzcCigJU2gWZlJYJmdt5BkM5291uVKlUk6/NY3KI3O9Ju3bol+cOHD66/byywuwGRFWZtPL0qwe/2ARUCAADAhAAAAPh8loEuDRuGYYwbN07ykiVLJOvS248fPyTv3r1bst77W+8V/vHjR8n6GNGJEydKbtCggeT79+9b/wAuiJR92atVqya5Zs2akvVxrJpuw1y5ciXkcxYsWCB5yJAhkvXn0SW0hIQEybqNFC6RvC97OEqedsX68cdVq1aVrDfV0po3by5Zl/o1vZHR9OnTJS9cuFCybh/oVVNm12e4RPI1Y+Vo8HAL1+fnLAMAAGAZEwIAAODvKgNdzjcMw0hPT5esy14DBgyQPG/ePMn6LITx48dLzsnJCfk6w4cPdzji6NOqVSvJ+njoUaNGFXieLn/WqlVLspVSlC6D2S0pnjp1SrIXbYJoYbYiw+xxu5uvWNG1a1fXXzOSfP36VbI+erhy5cqSdftNtwz0necbNmyQ3LRpU9fHGct0G8vJNaDbY2atMbfeK5yoEAAAACYEAADA51UGTnXo0EFy27ZtJc+fP1+yLsmZ0WXpz58/Sz5y5IjkzMzMIo/zT/y+Y/r9+/eSy5QpY/v9Xrx4IXnv3r2SJ02aJNlsoyErr//t2zfJFy5ckDxs2DDJhVtPbonkO6bDzUr5M9ZXGWhz5syRvHr1asn6/AJ9Z3vfvn0llyhRQvKxY8ck65U8+oj4Xbt2SR47dqyDUdsXCdeMk9K9XvVk95hjK+/LKgMAAOA7JgQAAMDfVQZOXbp0KWTeuHGjZF1K00eNvn37VrJuE+gS3rVr19wbbECVL19e8s+fPyV/+fKlwPN0W2XlypWSdUl/1apVknWbQJfBvn//Lvn8+fOSjx8/HnJ8emMVvSLi3bt3knX7wDAM4+TJk6afIxLpUmNQzgqIhDumg2TdunWS9d9rx44dJffr10+ybh8cOHBAsj7LJTU1NeR76aPj8d+NuZz8vdptE2hmq27MzgXxAxUCAADAhAAAAET4KgMr9IZFeqVAWlqa5FmzZnk6Js3vO6bz8/NDjiUrK6vA83R5Uu+h3r59e8nVq1cP+R56wxV9t7Xd/cJ1uW7btm2Sa9euXeB5Bw8elDxhwgTJdlcj+HnHtNm+6kG5Dq2clVC4vOrW0a5+XzNu6tmzp2T993n58uW//q4+I6RJkyaS9dksz549czpEW4KyysDNcwmsbDrk5L29+FtklQEAALCMCQEAAIjsVQZWmN3Z+fDhQ49HEkx3796VXL9+fck9evQo8LxBgwZJ/ueffySblaL0BipTp06V/PLlyyKPVZffOnfuLLlwWXTo0KEhf1+3j4K++sDsTujC5Xm3yvBm7JY/dZsg3GOLBnr1jhV6VVCdOnUk5+XlSfa6TRBEVo/8Nvsb1b9v5UwOu9dJUK8NKgQAAIAJAQAAiIGWgZnr16/7PYRA0Hc5p6SkSC58/LEZ3RpITk6WrDd10isZ3KJbD4WPhD106JBk3T7QrQ7dAokkhcuRZpuaWDmC1YyTjVuCWgqNFrp8XaFCBcl6czVY/xs2u350C8CsHWC1LfEvJ+cgeIUKAQAAYEIAAACidGMifSfu/fv3JeuNP1q2bBnyca8FdZOVhg0bFvhZnymwdu1a19/PTYmJiZLXr18f8jn6SFkzfm6y4sbfRTj5Wf4M6jXjhYEDB0o+fPiwZL1aqFGjRp6OSQvKxkRBuX7sbmoULmxMBAAALGNCAAAAonOVgd5Up2LFipL1nvp+tgkiwZ07d/74c5Dp8lhQSod26TK8073Y3aLbBEE6sjWWJCQkhHy88Nkj8E9Q2gRFQYUAAAAwIQAAAEwIAACAEaX3EJQuXTrk47m5uR6PBH7YsWOHZL2bmNmhR0Fktqyv8A5sdndLs/J+ugfKzoP+08uo+/TpI/nNmzeSMzIyPB1T0BXlHhwrvX99vUXjtUGFAAAAMCEAAABRulPhnj17JOtDembMmCF58+bNno7JTCzvuuaFuLg4yfHx8ZKtnBkflF3XUFCsXTOzZ8+WrHcJvXnzpuQWLVp4OiYzXDPBxE6FAADAMiYEAAAgOlcZPHjwIOTjVg60QXT58uVLyAwAKIgKAQAAYEIAAACitGVw6NAhyUlJSZJHjBghOS0tzdMxAUBR3Lt3L+Tj/DcMbqNCAAAAmBAAAIAo3ZgoksTaJiuRhE1WgolrJri4ZoKJjYkAAIBlTAgAAAATAgAAwIQAAAAYTAgAAIBhY5UBAACIXlQIAAAAEwIAAMCEAAAAGEwIAACAwYQAAAAYTAgAAIDBhAAAABhMCAAAgMGEAAAAGIbxP9aGSXrO0K6xAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Predicted Labels: \n",
+            "8       8       6       1       8       "
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file