diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..13566b8
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
diff --git a/.idea/14_days_challenge.iml b/.idea/14_days_challenge.iml
new file mode 100644
index 0000000..d0876a7
--- /dev/null
+++ b/.idea/14_days_challenge.iml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..a0f56f8
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (pythonProject)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..2215a9c
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/14_days_challenge.iml" filepath="$PROJECT_DIR$/.idea/14_days_challenge.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file
diff --git a/.idea/webResources.xml b/.idea/webResources.xml
new file mode 100644
index 0000000..da03542
--- /dev/null
+++ b/.idea/webResources.xml
@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="WebResourcesPaths">
+    <contentEntries>
+      <entry url="file://$PROJECT_DIR$">
+        <entryData>
+          <resourceRoots>
+            <path value="file://$PROJECT_DIR$/ankit" />
+          </resourceRoots>
+        </entryData>
+      </entry>
+    </contentEntries>
+  </component>
+</project>
\ No newline at end of file
diff --git a/Finance.ipynb b/Finance.ipynb
new file mode 100644
index 0000000..cf5730f
--- /dev/null
+++ b/Finance.ipynb
@@ -0,0 +1,727 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [],
+      "mount_file_id": "1eVjWN0ysDmV2RReXznoIIT_fyNwrYv7n",
+      "authorship_tag": "ABX9TyP51gcOQnjIJkb4JDikMx/g",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Ankit455/14_days_challenge/blob/ankit/Finance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "DK9OMf6Yi7T3"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df  = pd.read_csv('drive/MyDrive/financials.csv')\n",
+        "print(df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GofWEpHll3zk",
+        "outputId": "68761701-37de-400b-e696-12ce7692f552"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Symbol                 Name                  Sector   Price  Price/Earnings  \\\n",
+            "0    MMM           3M Company             Industrials  222.89           24.31   \n",
+            "1    AOS      A.O. Smith Corp             Industrials   60.24           27.76   \n",
+            "2    ABT  Abbott Laboratories             Health Care   56.27           22.51   \n",
+            "3   ABBV          AbbVie Inc.             Health Care  108.48           19.41   \n",
+            "4    ACN        Accenture plc  Information Technology  150.51           25.47   \n",
+            "\n",
+            "   Dividend Yield  Earnings/Share  52 Week Low  52 Week High    Market Cap  \\\n",
+            "0        2.332862            7.92       259.77       175.490  1.387211e+11   \n",
+            "1        1.147959            1.70        68.39        48.925  1.078342e+10   \n",
+            "2        1.908982            0.26        64.60        42.280  1.021210e+11   \n",
+            "3        2.499560            3.29       125.86        60.050  1.813863e+11   \n",
+            "4        1.714470            5.44       162.60       114.820  9.876586e+10   \n",
+            "\n",
+            "         EBITDA  Price/Sales  Price/Book  \\\n",
+            "0  9.048000e+09     4.390271       11.34   \n",
+            "1  6.010000e+08     3.575483        6.35   \n",
+            "2  5.744000e+09     3.740480        3.19   \n",
+            "3  1.031000e+10     6.291571       26.14   \n",
+            "4  5.643228e+09     2.604117       10.62   \n",
+            "\n",
+            "                                         SEC Filings  \n",
+            "0  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "1  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "2  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "3  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "4  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# New Section"
+      ],
+      "metadata": {
+        "id": "md0NhiY2Q8kQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.info())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Yxt0MaG0m9_7",
+        "outputId": "08afdb32-0a95-4a06-c55c-42e9d1c9de26"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 505 entries, 0 to 504\n",
+            "Data columns (total 14 columns):\n",
+            " #   Column          Non-Null Count  Dtype  \n",
+            "---  ------          --------------  -----  \n",
+            " 0   Symbol          505 non-null    object \n",
+            " 1   Name            505 non-null    object \n",
+            " 2   Sector          505 non-null    object \n",
+            " 3   Price           505 non-null    float64\n",
+            " 4   Price/Earnings  503 non-null    float64\n",
+            " 5   Dividend Yield  505 non-null    float64\n",
+            " 6   Earnings/Share  505 non-null    float64\n",
+            " 7   52 Week Low     505 non-null    float64\n",
+            " 8   52 Week High    505 non-null    float64\n",
+            " 9   Market Cap      505 non-null    float64\n",
+            " 10  EBITDA          505 non-null    float64\n",
+            " 11  Price/Sales     505 non-null    float64\n",
+            " 12  Price/Book      497 non-null    float64\n",
+            " 13  SEC Filings     505 non-null    object \n",
+            "dtypes: float64(10), object(4)\n",
+            "memory usage: 55.4+ KB\n",
+            "None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "okGiIBRVQvXO",
+        "outputId": "8f14d8d5-9ab2-4e0c-85d5-4f4c92b71fc4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Symbol            0\n",
+              "Name              0\n",
+              "Sector            0\n",
+              "Price             0\n",
+              "Price/Earnings    2\n",
+              "Dividend Yield    0\n",
+              "Earnings/Share    0\n",
+              "52 Week Low       0\n",
+              "52 Week High      0\n",
+              "Market Cap        0\n",
+              "EBITDA            0\n",
+              "Price/Sales       0\n",
+              "Price/Book        8\n",
+              "SEC Filings       0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.dropna().head(50)\n",
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M1Oi0fVuR_Qr",
+        "outputId": "26347261-540a-4c2e-9aa6-59242c67c9be"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Symbol            0\n",
+              "Name              0\n",
+              "Sector            0\n",
+              "Price             0\n",
+              "Price/Earnings    0\n",
+              "Dividend Yield    0\n",
+              "Earnings/Share    0\n",
+              "52 Week Low       0\n",
+              "52 Week High      0\n",
+              "Market Cap        0\n",
+              "EBITDA            0\n",
+              "Price/Sales       0\n",
+              "Price/Book        0\n",
+              "SEC Filings       0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1= df.loc[:,['52 Week Low','52 Week High']]\n",
+        "df1.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 215
+        },
+        "id": "X5vYdbYNSVEN",
+        "outputId": "c3acb53c-a151-4bfd-bdf1-27c508e69426"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   52 Week Low  52 Week High\n",
+              "0       259.77       175.490\n",
+              "1        68.39        48.925\n",
+              "2        64.60        42.280\n",
+              "3       125.86        60.050\n",
+              "4       162.60       114.820"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-13ed4f96-1457-4edd-9ff3-38c3aaaf904d\">\n",
+              "    <div 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>52 Week Low</th>\n",
+              "      <th>52 Week High</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>259.77</td>\n",
+              "      <td>175.490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>68.39</td>\n",
+              "      <td>48.925</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>64.60</td>\n",
+              "      <td>42.280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>125.86</td>\n",
+              "      <td>60.050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>162.60</td>\n",
+              "      <td>114.820</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13ed4f96-1457-4edd-9ff3-38c3aaaf904d')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-13ed4f96-1457-4edd-9ff3-38c3aaaf904d 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-13ed4f96-1457-4edd-9ff3-38c3aaaf904d');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r,c = df.shape\n",
+        "print(r,c)"
+      ],
+      "metadata": {
+        "id": "YgSBvX_zq2XK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "abf121d8-290d-4411-b206-5b6269ce12a7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "505 14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.describe())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OPCtjBpj67Rz",
+        "outputId": "a36dafc0-56c3-4241-f036-a25f4629d42c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "            Price  Price/Earnings  Dividend Yield  Earnings/Share  \\\n",
+            "count   20.000000       20.000000       20.000000       20.000000   \n",
+            "mean   106.510000       30.991000        1.480700        4.855000   \n",
+            "std     59.155562       37.957537        1.283868        4.239713   \n",
+            "min     10.060000        9.660000        0.000000       -1.720000   \n",
+            "25%     63.652500       18.192500        0.411724        1.667500   \n",
+            "50%    106.830000       23.365000        1.174186        4.415000   \n",
+            "75%    151.082500       27.527500        2.357123        7.552500   \n",
+            "max    222.890000      187.000000        4.961832       13.660000   \n",
+            "\n",
+            "       52 Week Low  52 Week High    Market Cap        EBITDA  Price/Sales  \\\n",
+            "count    20.000000     20.000000  2.000000e+01  2.000000e+01    20.000000   \n",
+            "mean    129.343500     82.828805  4.568229e+10  2.677373e+09     4.520701   \n",
+            "std      70.771182     47.168770  5.107358e+10  2.939704e+09     3.512549   \n",
+            "min      12.050000      9.700000  6.242378e+09  0.000000e+00     0.659514   \n",
+            "25%      73.598750     47.856250  1.069811e+10  6.647725e+08     1.732244   \n",
+            "50%     130.115000     73.805000  1.701399e+10  1.463200e+09     3.928424   \n",
+            "75%     179.977500    115.992500  6.803532e+10  3.285500e+09     5.963882   \n",
+            "max     259.770000    175.490000  1.813863e+11  1.031000e+10    13.092818   \n",
+            "\n",
+            "       Price/Book  \n",
+            "count   20.000000  \n",
+            "mean     6.511000  \n",
+            "std      6.675381  \n",
+            "min      1.530000  \n",
+            "25%      2.795000  \n",
+            "50%      3.450000  \n",
+            "75%      7.417500  \n",
+            "max     26.140000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "name = df['Name'].head(10)\n",
+        "price = df['Price'].head(10)\n",
+        "high52 = df['52 Week High'].head(10)\n",
+        "low52 = df['52 Week Low'].head(10)\n"
+      ],
+      "metadata": {
+        "id": "pW0COy_N7q1h"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "fig = plt.figure(figsize=(10,5))\n",
+        "plt.bar(name,price,color='blue',width=0.2)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4Vx5h_Z_9Qxx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.plot(x='52 Week High', y='52 Week Low', style='+')\n",
+        "plt.xlabel('52 Week High')\n",
+        "plt.ylabel('52 Week Low')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "LYHfixdzZqO_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "outputId": "6f093f48-24a3-4b35-99bf-7e3810f80e44"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = pd.DataFrame(df['52 Week High'])\n",
+        "y = pd.DataFrame(df['52 Week Low'])"
+      ],
+      "metadata": {
+        "id": "U1xmlVfHf-_n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=1)\n",
+        "X_train.shape\n",
+        "X_test.shape\n",
+        "y_train.shape\n",
+        "y_test.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JSZwIBN4gIRc",
+        "outputId": "8cdb2485-7070-439b-ce6b-0c4353cc9de6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1m0Yo9MjgbHj",
+        "outputId": "e70527c8-d55b-447a-bd64-edd9c1bb2e21"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(regressor.intercept_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eWcF3q32geV7",
+        "outputId": "0e971114-891d-425d-e176-1e5745182166"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[13.40387996]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(regressor.coef_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SjGg7Cu-ghVv",
+        "outputId": "93d668e4-6a04-4949-ba71-17d4efa685d8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1.39623434]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_predict = regressor.predict(X_test)\n",
+        "print(y_test_predict)"
+      ],
+      "metadata": {
+        "id": "RbQFKqd_nE6V",
+        "outputId": "3eb4a5da-80d9-4eab-ffd3-387b96952014",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 97.24775219]\n",
+            " [ 75.74574332]\n",
+            " [211.66915651]\n",
+            " [ 27.36622338]\n",
+            " [ 72.43666793]\n",
+            " [ 82.14049661]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plotdata = pd.DataFrame({\n",
+        "\n",
+        "    \"2019\":[68,73,80,79],\n",
+        "\n",
+        "    \"2020\":[73,78,80,85]},\n",
+        "\n",
+        "    index=[\"Django\", \"Gafur\", \"Tommy\", \"Ronnie\"])\n",
+        "\n",
+        "plotdata.plot(kind=\"bar\",figsize=(15, 8))\n",
+        "\n",
+        "plt.title(\"FIFA ratings\")\n",
+        "\n",
+        "plt.xlabel(\"Footballer\")\n",
+        "\n",
+        "plt.ylabel(\"Ratings\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 555
+        },
+        "id": "nb0bXhSEA9Sg",
+        "outputId": "075f6143-a96f-4747-e053-3d4479e3fe43"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Ratings')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x576 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Track 2/Day 3/Day_3.ipynb b/Track 2/Day 3/Day_3.ipynb
new file mode 100644
index 0000000..a9c747a
--- /dev/null
+++ b/Track 2/Day 3/Day_3.ipynb	
@@ -0,0 +1,348 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyNxK+I6rATHOhmd15QScltA",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Ankit455/14_days_challenge/blob/ankit/Track%202/Day%203/Day_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "4lUVvajYLx4c"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "age=[23,18,27,16,40,29,24,21,34,32]\n",
+        "employ=['unemployed','employed','employed','unemployed','employed','employed','unemployed','unemployed','employed','employed']\n",
+        "salary=[0,100000,10000,0,4000000,1500000,0,0,500000,2000000]\n",
+        "buy=[1,0,1,0,0,1,1,1,0,1]\n",
+        "\n",
+        "df=pd.DataFrame(\n",
+        "    {\n",
+        "        'age':age,\n",
+        "        'employment_type':employ,\n",
+        "        'salary':salary,\n",
+        "        'buy':buy\n",
+        "    }\n",
+        ")\n",
+        "\n",
+        "employment = pd.get_dummies(df['employment_type'], drop_first = True)\n",
+        "df = pd.concat([df, employment], axis = 1)\n",
+        "df.drop(['employment_type'], axis = 1, inplace = True)\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "pW8z6ZXhL9eH",
+        "outputId": "f189df19-570d-41a8-e642-7cca8ecdcf90"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   age   salary  buy  unemployed\n",
+              "0   23        0    1           1\n",
+              "1   18   100000    0           0\n",
+              "2   27    10000    1           0\n",
+              "3   16        0    0           1\n",
+              "4   40  4000000    0           0"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-710ce631-4ee8-4fac-8363-957c920090c7\">\n",
+              "    <div 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>age</th>\n",
+              "      <th>salary</th>\n",
+              "      <th>buy</th>\n",
+              "      <th>unemployed</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>23</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>18</td>\n",
+              "      <td>100000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>27</td>\n",
+              "      <td>10000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>16</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>40</td>\n",
+              "      <td>4000000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-710ce631-4ee8-4fac-8363-957c920090c7')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-710ce631-4ee8-4fac-8363-957c920090c7 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-710ce631-4ee8-4fac-8363-957c920090c7');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_data = df['buy'] \n",
+        "x_data = df.drop('buy', axis = 1)"
+      ],
+      "metadata": {
+        "id": "oU1kP0kfM39W"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_training_data, x_test_data, y_training_data, y_test_data = train_test_split(x_data, y_data, test_size = 0.3) "
+      ],
+      "metadata": {
+        "id": "9JplWP0jNCn2"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LogisticRegression\n",
+        "model = LogisticRegression()"
+      ],
+      "metadata": {
+        "id": "r1BWi2UzNF6A"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(x_training_data, y_training_data)\n",
+        "predictions = model.predict(x_test_data)"
+      ],
+      "metadata": {
+        "id": "2pMix4CfNJp9"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "print (confusion_matrix(y_test_data,predictions))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0JjEXIkENM2_",
+        "outputId": "62938c29-c5ab-4700-9696-bde143f4ca16"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0 0]\n",
+            " [3 0]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.regplot(x=x_data['age'], y=y_data, data=df, logistic=True, ci=None)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "id": "j1aCmc4DNRc8",
+        "outputId": "4402a79a-fdb8-4b2b-9474-cecca7e73ddd"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0469de1bd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Track 2/Day 4/Day4.ipynb b/Track 2/Day 4/Day4.ipynb
new file mode 100644
index 0000000..aa10847
--- /dev/null
+++ b/Track 2/Day 4/Day4.ipynb	
@@ -0,0 +1,710 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "mount_file_id": "16k6mMI25jgjzEG8sbRz8SEZK3B-qiyX3",
+      "authorship_tag": "ABX9TyPLks1eLypVhdGvX3uUJK3n",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Ankit455/14_days_challenge/blob/ankit/Track%202/Day%204/Day4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ylakPrhrIbP-"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "train_data = pd.read_csv('drive/MyDrive/train-data.csv')\n",
+        "test_data = pd.read_csv('drive/MyDrive/test-data.csv')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "axaDMpyOBEP7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "id": "XBsVbEhVwmvt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data = train_data.iloc[:,1:]\n",
+        "train_data.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Zp58juILxA1P",
+        "outputId": "28236289-b84a-4186-e04c-c76106233d8d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                               Name    Location  Year  Kilometers_Driven  \\\n",
+              "0            Maruti Wagon R LXI CNG      Mumbai  2010              72000   \n",
+              "1  Hyundai Creta 1.6 CRDi SX Option        Pune  2015              41000   \n",
+              "2                      Honda Jazz V     Chennai  2011              46000   \n",
+              "3                 Maruti Ertiga VDI     Chennai  2012              87000   \n",
+              "4   Audi A4 New 2.0 TDI Multitronic  Coimbatore  2013              40670   \n",
+              "\n",
+              "  Fuel_Type Transmission Owner_Type     Mileage   Engine      Power  Seats  \\\n",
+              "0       CNG       Manual      First  26.6 km/kg   998 CC  58.16 bhp    5.0   \n",
+              "1    Diesel       Manual      First  19.67 kmpl  1582 CC  126.2 bhp    5.0   \n",
+              "2    Petrol       Manual      First   18.2 kmpl  1199 CC   88.7 bhp    5.0   \n",
+              "3    Diesel       Manual      First  20.77 kmpl  1248 CC  88.76 bhp    7.0   \n",
+              "4    Diesel    Automatic     Second   15.2 kmpl  1968 CC  140.8 bhp    5.0   \n",
+              "\n",
+              "   Price  \n",
+              "0   1.75  \n",
+              "1  12.50  \n",
+              "2   4.50  \n",
+              "3   6.00  \n",
+              "4  17.74  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-40b114f5-d801-4590-a492-ce2e309ac39a\">\n",
+              "    <div 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>Name</th>\n",
+              "      <th>Location</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Kilometers_Driven</th>\n",
+              "      <th>Fuel_Type</th>\n",
+              "      <th>Transmission</th>\n",
+              "      <th>Owner_Type</th>\n",
+              "      <th>Mileage</th>\n",
+              "      <th>Engine</th>\n",
+              "      <th>Power</th>\n",
+              "      <th>Seats</th>\n",
+              "      <th>Price</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Maruti Wagon R LXI CNG</td>\n",
+              "      <td>Mumbai</td>\n",
+              "      <td>2010</td>\n",
+              "      <td>72000</td>\n",
+              "      <td>CNG</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>First</td>\n",
+              "      <td>26.6 km/kg</td>\n",
+              "      <td>998 CC</td>\n",
+              "      <td>58.16 bhp</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>1.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Hyundai Creta 1.6 CRDi SX Option</td>\n",
+              "      <td>Pune</td>\n",
+              "      <td>2015</td>\n",
+              "      <td>41000</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>First</td>\n",
+              "      <td>19.67 kmpl</td>\n",
+              "      <td>1582 CC</td>\n",
+              "      <td>126.2 bhp</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>12.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Honda Jazz V</td>\n",
+              "      <td>Chennai</td>\n",
+              "      <td>2011</td>\n",
+              "      <td>46000</td>\n",
+              "      <td>Petrol</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>First</td>\n",
+              "      <td>18.2 kmpl</td>\n",
+              "      <td>1199 CC</td>\n",
+              "      <td>88.7 bhp</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>4.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Maruti Ertiga VDI</td>\n",
+              "      <td>Chennai</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>87000</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Manual</td>\n",
+              "      <td>First</td>\n",
+              "      <td>20.77 kmpl</td>\n",
+              "      <td>1248 CC</td>\n",
+              "      <td>88.76 bhp</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>6.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Audi A4 New 2.0 TDI Multitronic</td>\n",
+              "      <td>Coimbatore</td>\n",
+              "      <td>2013</td>\n",
+              "      <td>40670</td>\n",
+              "      <td>Diesel</td>\n",
+              "      <td>Automatic</td>\n",
+              "      <td>Second</td>\n",
+              "      <td>15.2 kmpl</td>\n",
+              "      <td>1968 CC</td>\n",
+              "      <td>140.8 bhp</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>17.74</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40b114f5-d801-4590-a492-ce2e309ac39a')\"\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 0 24 24\"\n",
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+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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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+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-40b114f5-d801-4590-a492-ce2e309ac39a 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-40b114f5-d801-4590-a492-ce2e309ac39a');\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",
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+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.shape"
+      ],
+      "metadata": {
+        "colab": {
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+        },
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+        "outputId": "b07cb1b1-3a10-4c99-a45a-d90378d25895"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(5911, 12)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "a7a2eGYlxPg2",
+        "outputId": "5b044a8e-436b-4e58-ce61-b5e10bce2dc2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Name                  0\n",
+              "Location              0\n",
+              "Year                  0\n",
+              "Kilometers_Driven     0\n",
+              "Fuel_Type             0\n",
+              "Transmission          0\n",
+              "Owner_Type            0\n",
+              "Mileage               2\n",
+              "Engine               35\n",
+              "Power                35\n",
+              "Seats                37\n",
+              "Price                 0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.drop([\"Name\"],axis=1,inplace=True)\n",
+        "train_data.drop([\"Mileage\"],axis=1,inplace=True)\n",
+        "train_data.drop([\"Engine\"],axis=1,inplace=True)\n",
+        "train_data.drop([\"Power\"],axis=1,inplace=True)\n",
+        "train_data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "O0SmKRiPBF08",
+        "outputId": "8a74f982-2052-4960-e459-f1e660c14e7c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 5911 entries, 0 to 5910\n",
+            "Data columns (total 8 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   Location           5911 non-null   object \n",
+            " 1   Year               5911 non-null   int64  \n",
+            " 2   Kilometers_Driven  5911 non-null   int64  \n",
+            " 3   Fuel_Type          5911 non-null   object \n",
+            " 4   Transmission       5911 non-null   object \n",
+            " 5   Owner_Type         5911 non-null   object \n",
+            " 6   Seats              5874 non-null   float64\n",
+            " 7   Price              5911 non-null   float64\n",
+            "dtypes: float64(2), int64(2), object(4)\n",
+            "memory usage: 369.6+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "var = 'Location'\n",
+        "var1= 'Company'\n",
+        "train_data[var].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m_a-gEIUBbMG",
+        "outputId": "ddc74cfe-3160-4e84-a7d3-9de505f5e528"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Mumbai        780\n",
+              "Hyderabad     719\n",
+              "Kochi         648\n",
+              "Coimbatore    630\n",
+              "Pune          603\n",
+              "Delhi         550\n",
+              "Kolkata       525\n",
+              "Chennai       478\n",
+              "Jaipur        405\n",
+              "Bangalore     352\n",
+              "Ahmedabad     221\n",
+              "Name: Location, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Location = train_data[[var]]\n",
+        "Location = pd.get_dummies(Location,drop_first=True)\n",
+        "Location.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 250
+        },
+        "id": "wxT97RTxBgGn",
+        "outputId": "694c6ae7-2dc7-4027-8ad9-51febbe63aeb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Location_Bangalore  Location_Chennai  Location_Coimbatore  Location_Delhi  \\\n",
+              "0                   0                 0                    0               0   \n",
+              "1                   0                 0                    0               0   \n",
+              "2                   0                 1                    0               0   \n",
+              "3                   0                 1                    0               0   \n",
+              "4                   0                 0                    1               0   \n",
+              "\n",
+              "   Location_Hyderabad  Location_Jaipur  Location_Kochi  Location_Kolkata  \\\n",
+              "0                   0                0               0                 0   \n",
+              "1                   0                0               0                 0   \n",
+              "2                   0                0               0                 0   \n",
+              "3                   0                0               0                 0   \n",
+              "4                   0                0               0                 0   \n",
+              "\n",
+              "   Location_Mumbai  Location_Pune  \n",
+              "0                1              0  \n",
+              "1                0              1  \n",
+              "2                0              0  \n",
+              "3                0              0  \n",
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+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-6f395339-cd46-47e0-ab66-a0b9b7505b97 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-6f395339-cd46-47e0-ab66-a0b9b7505b97');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "Company= train_data[[var1]]\n",
+        "Company = pd.get_dummies(Company,drop_first=True)\n",
+        "Company.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 345
+        },
+        "id": "d_WPOKlfBjUl",
+        "outputId": "a2a42e9a-db74-4b5a-9c95-1dbe99ec92a3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-20-be230f1545fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mCompany\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar1\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      2\u001b[0m \u001b[0mCompany\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_dummies\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCompany\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdrop_first\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mCompany\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\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;32m/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3463\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3464\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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   3465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3466\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\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/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\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   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\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[0;31mKeyError\u001b[0m: \"None of [Index(['Company'], dtype='object')] are in the [columns]\""
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/ankit/Finance.ipynb b/ankit/Finance.ipynb
new file mode 100644
index 0000000..c61fa67
--- /dev/null
+++ b/ankit/Finance.ipynb
@@ -0,0 +1,727 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [],
+      "mount_file_id": "1eVjWN0ysDmV2RReXznoIIT_fyNwrYv7n",
+      "authorship_tag": "ABX9TyP51gcOQnjIJkb4JDikMx/g",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/Ankit455/14_days_challenge/blob/ankit/ankit/Finance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "DK9OMf6Yi7T3"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df  = pd.read_csv('drive/MyDrive/financials.csv')\n",
+        "print(df.head())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GofWEpHll3zk",
+        "outputId": "68761701-37de-400b-e696-12ce7692f552"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Symbol                 Name                  Sector   Price  Price/Earnings  \\\n",
+            "0    MMM           3M Company             Industrials  222.89           24.31   \n",
+            "1    AOS      A.O. Smith Corp             Industrials   60.24           27.76   \n",
+            "2    ABT  Abbott Laboratories             Health Care   56.27           22.51   \n",
+            "3   ABBV          AbbVie Inc.             Health Care  108.48           19.41   \n",
+            "4    ACN        Accenture plc  Information Technology  150.51           25.47   \n",
+            "\n",
+            "   Dividend Yield  Earnings/Share  52 Week Low  52 Week High    Market Cap  \\\n",
+            "0        2.332862            7.92       259.77       175.490  1.387211e+11   \n",
+            "1        1.147959            1.70        68.39        48.925  1.078342e+10   \n",
+            "2        1.908982            0.26        64.60        42.280  1.021210e+11   \n",
+            "3        2.499560            3.29       125.86        60.050  1.813863e+11   \n",
+            "4        1.714470            5.44       162.60       114.820  9.876586e+10   \n",
+            "\n",
+            "         EBITDA  Price/Sales  Price/Book  \\\n",
+            "0  9.048000e+09     4.390271       11.34   \n",
+            "1  6.010000e+08     3.575483        6.35   \n",
+            "2  5.744000e+09     3.740480        3.19   \n",
+            "3  1.031000e+10     6.291571       26.14   \n",
+            "4  5.643228e+09     2.604117       10.62   \n",
+            "\n",
+            "                                         SEC Filings  \n",
+            "0  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "1  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "2  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "3  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n",
+            "4  http://www.sec.gov/cgi-bin/browse-edgar?action...  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# New Section"
+      ],
+      "metadata": {
+        "id": "md0NhiY2Q8kQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.info())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Yxt0MaG0m9_7",
+        "outputId": "08afdb32-0a95-4a06-c55c-42e9d1c9de26"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 505 entries, 0 to 504\n",
+            "Data columns (total 14 columns):\n",
+            " #   Column          Non-Null Count  Dtype  \n",
+            "---  ------          --------------  -----  \n",
+            " 0   Symbol          505 non-null    object \n",
+            " 1   Name            505 non-null    object \n",
+            " 2   Sector          505 non-null    object \n",
+            " 3   Price           505 non-null    float64\n",
+            " 4   Price/Earnings  503 non-null    float64\n",
+            " 5   Dividend Yield  505 non-null    float64\n",
+            " 6   Earnings/Share  505 non-null    float64\n",
+            " 7   52 Week Low     505 non-null    float64\n",
+            " 8   52 Week High    505 non-null    float64\n",
+            " 9   Market Cap      505 non-null    float64\n",
+            " 10  EBITDA          505 non-null    float64\n",
+            " 11  Price/Sales     505 non-null    float64\n",
+            " 12  Price/Book      497 non-null    float64\n",
+            " 13  SEC Filings     505 non-null    object \n",
+            "dtypes: float64(10), object(4)\n",
+            "memory usage: 55.4+ KB\n",
+            "None\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "okGiIBRVQvXO",
+        "outputId": "8f14d8d5-9ab2-4e0c-85d5-4f4c92b71fc4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Symbol            0\n",
+              "Name              0\n",
+              "Sector            0\n",
+              "Price             0\n",
+              "Price/Earnings    2\n",
+              "Dividend Yield    0\n",
+              "Earnings/Share    0\n",
+              "52 Week Low       0\n",
+              "52 Week High      0\n",
+              "Market Cap        0\n",
+              "EBITDA            0\n",
+              "Price/Sales       0\n",
+              "Price/Book        8\n",
+              "SEC Filings       0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.dropna().head(50)\n",
+        "df.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M1Oi0fVuR_Qr",
+        "outputId": "26347261-540a-4c2e-9aa6-59242c67c9be"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Symbol            0\n",
+              "Name              0\n",
+              "Sector            0\n",
+              "Price             0\n",
+              "Price/Earnings    0\n",
+              "Dividend Yield    0\n",
+              "Earnings/Share    0\n",
+              "52 Week Low       0\n",
+              "52 Week High      0\n",
+              "Market Cap        0\n",
+              "EBITDA            0\n",
+              "Price/Sales       0\n",
+              "Price/Book        0\n",
+              "SEC Filings       0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1= df.loc[:,['52 Week Low','52 Week High']]\n",
+        "df1.head(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 215
+        },
+        "id": "X5vYdbYNSVEN",
+        "outputId": "c3acb53c-a151-4bfd-bdf1-27c508e69426"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   52 Week Low  52 Week High\n",
+              "0       259.77       175.490\n",
+              "1        68.39        48.925\n",
+              "2        64.60        42.280\n",
+              "3       125.86        60.050\n",
+              "4       162.60       114.820"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-13ed4f96-1457-4edd-9ff3-38c3aaaf904d\">\n",
+              "    <div 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>52 Week Low</th>\n",
+              "      <th>52 Week High</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>259.77</td>\n",
+              "      <td>175.490</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>68.39</td>\n",
+              "      <td>48.925</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>64.60</td>\n",
+              "      <td>42.280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>125.86</td>\n",
+              "      <td>60.050</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>162.60</td>\n",
+              "      <td>114.820</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13ed4f96-1457-4edd-9ff3-38c3aaaf904d')\"\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 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\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",
+              "    [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-13ed4f96-1457-4edd-9ff3-38c3aaaf904d 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-13ed4f96-1457-4edd-9ff3-38c3aaaf904d');\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",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "r,c = df.shape\n",
+        "print(r,c)"
+      ],
+      "metadata": {
+        "id": "YgSBvX_zq2XK",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "abf121d8-290d-4411-b206-5b6269ce12a7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "505 14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.describe())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OPCtjBpj67Rz",
+        "outputId": "a36dafc0-56c3-4241-f036-a25f4629d42c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "            Price  Price/Earnings  Dividend Yield  Earnings/Share  \\\n",
+            "count   20.000000       20.000000       20.000000       20.000000   \n",
+            "mean   106.510000       30.991000        1.480700        4.855000   \n",
+            "std     59.155562       37.957537        1.283868        4.239713   \n",
+            "min     10.060000        9.660000        0.000000       -1.720000   \n",
+            "25%     63.652500       18.192500        0.411724        1.667500   \n",
+            "50%    106.830000       23.365000        1.174186        4.415000   \n",
+            "75%    151.082500       27.527500        2.357123        7.552500   \n",
+            "max    222.890000      187.000000        4.961832       13.660000   \n",
+            "\n",
+            "       52 Week Low  52 Week High    Market Cap        EBITDA  Price/Sales  \\\n",
+            "count    20.000000     20.000000  2.000000e+01  2.000000e+01    20.000000   \n",
+            "mean    129.343500     82.828805  4.568229e+10  2.677373e+09     4.520701   \n",
+            "std      70.771182     47.168770  5.107358e+10  2.939704e+09     3.512549   \n",
+            "min      12.050000      9.700000  6.242378e+09  0.000000e+00     0.659514   \n",
+            "25%      73.598750     47.856250  1.069811e+10  6.647725e+08     1.732244   \n",
+            "50%     130.115000     73.805000  1.701399e+10  1.463200e+09     3.928424   \n",
+            "75%     179.977500    115.992500  6.803532e+10  3.285500e+09     5.963882   \n",
+            "max     259.770000    175.490000  1.813863e+11  1.031000e+10    13.092818   \n",
+            "\n",
+            "       Price/Book  \n",
+            "count   20.000000  \n",
+            "mean     6.511000  \n",
+            "std      6.675381  \n",
+            "min      1.530000  \n",
+            "25%      2.795000  \n",
+            "50%      3.450000  \n",
+            "75%      7.417500  \n",
+            "max     26.140000  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "name = df['Name'].head(10)\n",
+        "price = df['Price'].head(10)\n",
+        "high52 = df['52 Week High'].head(10)\n",
+        "low52 = df['52 Week Low'].head(10)\n"
+      ],
+      "metadata": {
+        "id": "pW0COy_N7q1h"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "fig = plt.figure(figsize=(10,5))\n",
+        "plt.bar(name,price,color='blue',width=0.2)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "4Vx5h_Z_9Qxx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.plot(x='52 Week High', y='52 Week Low', style='+')\n",
+        "plt.xlabel('52 Week High')\n",
+        "plt.ylabel('52 Week Low')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "LYHfixdzZqO_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 279
+        },
+        "outputId": "6f093f48-24a3-4b35-99bf-7e3810f80e44"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X = pd.DataFrame(df['52 Week High'])\n",
+        "y = pd.DataFrame(df['52 Week Low'])"
+      ],
+      "metadata": {
+        "id": "U1xmlVfHf-_n"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3, random_state=1)\n",
+        "X_train.shape\n",
+        "X_test.shape\n",
+        "y_train.shape\n",
+        "y_test.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JSZwIBN4gIRc",
+        "outputId": "8cdb2485-7070-439b-ce6b-0c4353cc9de6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6, 1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.linear_model import LinearRegression\n",
+        "from sklearn.metrics import mean_squared_error\n",
+        "regressor = LinearRegression()\n",
+        "regressor.fit(X_train, y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1m0Yo9MjgbHj",
+        "outputId": "e70527c8-d55b-447a-bd64-edd9c1bb2e21"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LinearRegression()"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(regressor.intercept_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eWcF3q32geV7",
+        "outputId": "0e971114-891d-425d-e176-1e5745182166"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[13.40387996]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(regressor.coef_)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SjGg7Cu-ghVv",
+        "outputId": "93d668e4-6a04-4949-ba71-17d4efa685d8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1.39623434]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_predict = regressor.predict(X_test)\n",
+        "print(y_test_predict)"
+      ],
+      "metadata": {
+        "id": "RbQFKqd_nE6V",
+        "outputId": "3eb4a5da-80d9-4eab-ffd3-387b96952014",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 97.24775219]\n",
+            " [ 75.74574332]\n",
+            " [211.66915651]\n",
+            " [ 27.36622338]\n",
+            " [ 72.43666793]\n",
+            " [ 82.14049661]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plotdata = pd.DataFrame({\n",
+        "\n",
+        "    \"2019\":[68,73,80,79],\n",
+        "\n",
+        "    \"2020\":[73,78,80,85]},\n",
+        "\n",
+        "    index=[\"Django\", \"Gafur\", \"Tommy\", \"Ronnie\"])\n",
+        "\n",
+        "plotdata.plot(kind=\"bar\",figsize=(15, 8))\n",
+        "\n",
+        "plt.title(\"FIFA ratings\")\n",
+        "\n",
+        "plt.xlabel(\"Footballer\")\n",
+        "\n",
+        "plt.ylabel(\"Ratings\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 555
+        },
+        "id": "nb0bXhSEA9Sg",
+        "outputId": "075f6143-a96f-4747-e053-3d4479e3fe43"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0, 0.5, 'Ratings')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x576 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/ankit/ds.py b/ankit/ds.py
new file mode 100644
index 0000000..df39e65
--- /dev/null
+++ b/ankit/ds.py
@@ -0,0 +1,2 @@
+import numpy as np
+import pandas as pd
diff --git a/ankit/requirements.txt b/ankit/requirements.txt
new file mode 100644
index 0000000..8318c86
--- /dev/null
+++ b/ankit/requirements.txt
@@ -0,0 +1 @@
+Test
\ No newline at end of file
diff --git a/details.txt.txt b/details.txt.txt
new file mode 100644
index 0000000..1c38493
--- /dev/null
+++ b/details.txt.txt
@@ -0,0 +1,2 @@
+ankit
+ankitdhandharia45@gmail.com
\ No newline at end of file