Kolkata_House_Price_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VXkGR_qgsVFR"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, KFold\n",
        "from sklearn.metrics import r2_score, mean_absolute_error, mean_absolute_percentage_error\n",
        "from joblib import load, dump"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv('House_Price.csv')\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "zOv5N6GTvAj7",
        "outputId": "c8b8a23e-5501-4004-d32f-8cbae61fced0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Flat_Price  EMI_Starts    BHK  css-11nfaq3 Unnamed: 4         HOUSE_TYPE  \\\n",
              "0    ₹8.5 Cr  ₹4.22 Lacs  6 BHK          6.0        BHK  Independent House   \n",
              "1    ₹45.0 L    ₹23.83 K  3 BHK          3.0        BHK  Independent House   \n",
              "2   ₹1.35 Cr    ₹67.02 K  3 BHK          3.0        BHK  Independent House   \n",
              "3    ₹60.0 L    ₹31.77 K  5 BHK          5.0        BHK  Independent House   \n",
              "4    ₹52.0 L    ₹27.54 K  4 BHK          4.0        BHK  Independent House   \n",
              "\n",
              "    Unnamed: 6 Unnamed: 7      Purpose              Location      Area_Type  \\\n",
              "0  Independent      House  for sale in   Ballygunge, Kolkata  Build Up Area   \n",
              "1  Independent      House  for sale in  Barrackpore, Kolkata  Build Up Area   \n",
              "2  Independent      House  for sale in   Santoshpur, Kolkata  Build Up Area   \n",
              "3  Independent      House  for sale in      Sarsuna, Kolkata  Build Up Area   \n",
              "4  Independent      House  for sale in  Madhyamgram, Kolkata  Build Up Area   \n",
              "\n",
              "  Total_Sq.ft Price_per_sq.ft            Owner_name      Owner_type  \n",
              "0  4200 sq.ft  ₹20.24 K/sq.ft   Abhijit Chakraborty  Housing Expert  \n",
              "1  1400 sq.ft   ₹3.21 K/sq.ft       Virtual Reality  Housing Expert  \n",
              "2  2500 sq.ft   ₹5.40 K/sq.ft     AveNew Properties  Housing Expert  \n",
              "3  1100 sq.ft   ₹5.45 K/sq.ft  Ganapati Real Estate  Housing Expert  \n",
              "4   900 sq.ft   ₹5.78 K/sq.ft  Third Eye Consulting  Housing Expert  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dd6b09c4-6b15-46ac-9303-f85abe12b01b\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>css-11nfaq3</th>\n",
              "      <th>Unnamed: 4</th>\n",
              "      <th>HOUSE_TYPE</th>\n",
              "      <th>Unnamed: 6</th>\n",
              "      <th>Unnamed: 7</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>Location</th>\n",
              "      <th>Area_Type</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Owner_name</th>\n",
              "      <th>Owner_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>₹8.5 Cr</td>\n",
              "      <td>₹4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>6.0</td>\n",
              "      <td>BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>Independent</td>\n",
              "      <td>House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>₹20.24 K/sq.ft</td>\n",
              "      <td>Abhijit Chakraborty</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>₹45.0 L</td>\n",
              "      <td>₹23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>Independent</td>\n",
              "      <td>House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>₹3.21 K/sq.ft</td>\n",
              "      <td>Virtual Reality</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>₹1.35 Cr</td>\n",
              "      <td>₹67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>Independent</td>\n",
              "      <td>House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>₹5.40 K/sq.ft</td>\n",
              "      <td>AveNew Properties</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>₹60.0 L</td>\n",
              "      <td>₹31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>5.0</td>\n",
              "      <td>BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>Independent</td>\n",
              "      <td>House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>₹5.45 K/sq.ft</td>\n",
              "      <td>Ganapati Real Estate</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>₹52.0 L</td>\n",
              "      <td>₹27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>4.0</td>\n",
              "      <td>BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>Independent</td>\n",
              "      <td>House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>₹5.78 K/sq.ft</td>\n",
              "      <td>Third Eye Consulting</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd6b09c4-6b15-46ac-9303-f85abe12b01b')\"\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-dd6b09c4-6b15-46ac-9303-f85abe12b01b 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-dd6b09c4-6b15-46ac-9303-f85abe12b01b');\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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.drop(['css-11nfaq3', 'Unnamed: 4', 'Unnamed: 6', 'Unnamed: 7'], axis=1, inplace=True)\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "9AqTck_xvFdu",
        "outputId": "111972dd-991c-48b5-8d60-5bcfae9f82fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Flat_Price  EMI_Starts    BHK         HOUSE_TYPE      Purpose  \\\n",
              "0    ₹8.5 Cr  ₹4.22 Lacs  6 BHK  Independent House  for sale in   \n",
              "1    ₹45.0 L    ₹23.83 K  3 BHK  Independent House  for sale in   \n",
              "2   ₹1.35 Cr    ₹67.02 K  3 BHK  Independent House  for sale in   \n",
              "3    ₹60.0 L    ₹31.77 K  5 BHK  Independent House  for sale in   \n",
              "4    ₹52.0 L    ₹27.54 K  4 BHK  Independent House  for sale in   \n",
              "\n",
              "               Location      Area_Type Total_Sq.ft Price_per_sq.ft  \\\n",
              "0   Ballygunge, Kolkata  Build Up Area  4200 sq.ft  ₹20.24 K/sq.ft   \n",
              "1  Barrackpore, Kolkata  Build Up Area  1400 sq.ft   ₹3.21 K/sq.ft   \n",
              "2   Santoshpur, Kolkata  Build Up Area  2500 sq.ft   ₹5.40 K/sq.ft   \n",
              "3      Sarsuna, Kolkata  Build Up Area  1100 sq.ft   ₹5.45 K/sq.ft   \n",
              "4  Madhyamgram, Kolkata  Build Up Area   900 sq.ft   ₹5.78 K/sq.ft   \n",
              "\n",
              "             Owner_name      Owner_type  \n",
              "0   Abhijit Chakraborty  Housing Expert  \n",
              "1       Virtual Reality  Housing Expert  \n",
              "2     AveNew Properties  Housing Expert  \n",
              "3  Ganapati Real Estate  Housing Expert  \n",
              "4  Third Eye Consulting  Housing Expert  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4973fe5-99e4-47cd-875c-e5d9acf27b13\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>HOUSE_TYPE</th>\n",
              "      <th>Purpose</th>\n",
              "      <th>Location</th>\n",
              "      <th>Area_Type</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Owner_name</th>\n",
              "      <th>Owner_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>₹8.5 Cr</td>\n",
              "      <td>₹4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>₹20.24 K/sq.ft</td>\n",
              "      <td>Abhijit Chakraborty</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>₹45.0 L</td>\n",
              "      <td>₹23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>₹3.21 K/sq.ft</td>\n",
              "      <td>Virtual Reality</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>₹1.35 Cr</td>\n",
              "      <td>₹67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>₹5.40 K/sq.ft</td>\n",
              "      <td>AveNew Properties</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>₹60.0 L</td>\n",
              "      <td>₹31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>₹5.45 K/sq.ft</td>\n",
              "      <td>Ganapati Real Estate</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>₹52.0 L</td>\n",
              "      <td>₹27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>Independent House</td>\n",
              "      <td>for sale in</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>Build Up Area</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>₹5.78 K/sq.ft</td>\n",
              "      <td>Third Eye Consulting</td>\n",
              "      <td>Housing Expert</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4973fe5-99e4-47cd-875c-e5d9acf27b13')\"\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-c4973fe5-99e4-47cd-875c-e5d9acf27b13 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-c4973fe5-99e4-47cd-875c-e5d9acf27b13');\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": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['HOUSE_TYPE'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKzb9z_EvgMe",
        "outputId": "e5ee8e20-00e3-4b61-e2c0-100fe4bd938c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Independent House', ' '], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['Area_Type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ckos09jcAmYX",
        "outputId": "b222551e-cfcc-4706-e125-2cf4b2eb4893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Build Up Area', 'Possession Starts'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['Location'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Jt77RpwAtn2",
        "outputId": "5c16a0cb-58a3-405e-c186-903c73c6a48e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ballygunge, Kolkata', 'Barrackpore, Kolkata',\n",
              "       'Santoshpur, Kolkata', 'Sarsuna, Kolkata', 'Madhyamgram, Kolkata',\n",
              "       'Thakurpukur, Kolkata', 'Rajpur, Kolkata',\n",
              "       'North Dum Dum, Kolkata', 'Dhakuria, Kolkata',\n",
              "       'Shyambazar, Kolkata', 'Nazirabad, Kolkata', 'Joka, Kolkata',\n",
              "       'Kasba, Kolkata', 'Barisha, Kolkata', 'Behala, Kolkata',\n",
              "       'Taratala, Kolkata', 'Jodhpur Park, Kolkata',\n",
              "       'Salt Lake City, Kolkata', 'New Town, Kolkata',\n",
              "       'Mohispota, Kolkata', 'Maheshtala, Kolkata', 'Naihati, Kolkata',\n",
              "       'Birati, Kolkata', 'Amtala, Kolkata', 'Gariahat, Kolkata',\n",
              "       'New Barrakpur, Kolkata', 'Rajpur Sonarpur, Kolkata',\n",
              "       'Khardah, Kolkata', 'Belghoria, Kolkata', 'Sodepur, Kolkata',\n",
              "       'Baghbazar, Kolkata', 'Baghajatin, Kolkata', 'Nimta, Kolkata',\n",
              "       'Lake Gardens, Kolkata', 'Khidirpur, Kolkata',\n",
              "       'Kolutolla, Kolkata', 'Baguiati, Kolkata',\n",
              "       'Diamond Harbour, Kolkata', 'Bantala, Kolkata',\n",
              "       'Tollygunge, Kolkata', 'Sinthi, Kolkata', 'Beliaghata, Kolkata',\n",
              "       'Kamardanga, Kolkata', 'Netaji Nagar, Kolkata', 'Garia, Kolkata',\n",
              "       'Rajarhat, Kolkata', 'Kalikapur, Kolkata',\n",
              "       'East Kolkata Township, Kolkata', 'Tangra, Kolkata',\n",
              "       'Bhowanipore, Kolkata', 'Bansdroni, Kolkata', 'Kalighat, Kolkata',\n",
              "       'Ichapur, Kolkata', 'Mukundapur, Kolkata', 'Bramhapur, Kolkata',\n",
              "       'Hussainpur, Kolkata', nan, 'Berunanpukhuria, Kolkata', 'Kolkata',\n",
              "       'Bhatpara, Kolkata', 'Keshtopur, Kolkata', 'Chinar Park, Kolkata',\n",
              "       'Baguihati, Kolkata', 'New Alipore, Kolkata', 'Kamdahari, Kolkata',\n",
              "       'Paschim Putiary, Kolkata', 'Ganguly Bagan, Kolkata',\n",
              "       'Kaikhali, Kolkata', 'Garden Reach, Kolkata',\n",
              "       'Picnic Garden, Kolkata', 'Haltu, Kolkata',\n",
              "       'Raghunathpur, Kolkata', 'Baishnabghata Patuli Township,',\n",
              "       'Srirampur, Kolkata', 'Agarpara, Kolkata', 'Taltala, Kolkata',\n",
              "       'Entally, Kolkata', 'Purba Barisha, Kolkata', 'Dum Dum, Kolkata',\n",
              "       'Lake Town, Kolkata', 'Ariadaha, Kolkata', 'Elgin, Kolkata',\n",
              "       'Purba Putiary, Kolkata', 'VIP Nagar, Kolkata', 'Barasat, Kolkata',\n",
              "       'Jadavpur, Kolkata', 'Paschim Barisha, Kolkata',\n",
              "       'Alipore, Kolkata', 'Pailan, Kolkata', 'Garfa, Kolkata',\n",
              "       'Shyamnagar, Kolkata', 'Ward No 113, Kolkata',\n",
              "       'Tagore Park, Kolkata', 'Sarada Pally, Kolkata',\n",
              "       'Hanspukuria, Kolkata', 'Golf Green, Kolkata',\n",
              "       'Panchpota, Kolkata', 'Nayabad, Kolkata', 'Bijoygarh, Kolkata',\n",
              "       'Kabardanga, Kolkata', 'Bow Bazaar, Kolkata',\n",
              "       'South Dum Dum, Kolkata', 'Dum Dum Cantonment, Kolkata',\n",
              "       'Kamalgazi, Kolkata', 'Dakshin Gobindopur, Kolkata',\n",
              "       'Pancha Sayar, Kolkata', 'Beniapukur, Kolkata',\n",
              "       'Kalagachhia, Kolkata', 'Chitpur, Kolkata', 'Rajabagan, Kolkata',\n",
              "       'Kalyan Nagar, Kolkata', 'Bangaon, Kolkata', 'Champahati, Kolkata',\n",
              "       'Natagarh, Kolkata', 'Cossipore, Kolkata', 'Machuabazar, Kolkata',\n",
              "       'Habra, Kolkata', 'Sewli Telinipara, Kolkata', 'Topsia, Kolkata',\n",
              "       'Kashipur, Kolkata', 'Shobhabazar, Kolkata', 'Ashokgarh, Kolkata',\n",
              "       'Duttapukur, Kolkata', 'Bagnan, Kolkata',\n",
              "       'Park Street Area, Kolkata', 'Kalyani, Kolkata',\n",
              "       'Budge Budge, Kolkata', 'Panihati, Kolkata',\n",
              "       'Narendrapur, Kolkata', 'Chowbaga, Kolkata', 'Saha Para, Kolkata',\n",
              "       'Bamangachhi, Kolkata', 'Thakuranir Chak, Kolkata',\n",
              "       'Tala, Kolkata', 'Bamunpara, Kolkata', 'Paikpara, Kolkata',\n",
              "       'Jorabagan, Kolkata', 'Krishnanagar, Kolkata',\n",
              "       'Maniktala, Kolkata', 'Regent Park, Kolkata', 'Belgachia, Kolkata',\n",
              "       'Mominpore, Kolkata', 'Rahara, Kolkata', 'Bishnupur, Kolkata',\n",
              "       'Talbanda, Kolkata', 'Bira, Kolkata', 'Barabazar Market, Kolkata',\n",
              "       'Bhatenda, Kolkata', 'Jorasanko, Kolkata', 'Khariberia, Kolkata',\n",
              "       'Bagmari, Kolkata', 'Adarsha Nagar, Kolkata', 'Malickpur, Kolkata',\n",
              "       'Dunlop, Kolkata', 'Hedua, Kolkata', 'Baranagar, Kolkata',\n",
              "       'Pansila, Kolkata', 'Garulia, Kolkata', 'Rania, Kolkata',\n",
              "       'College Square, Kolkata', 'Chandpara, Kolkata',\n",
              "       'Kokapur, Kolkata', 'Bagpota, Kolkata', 'Dakshineswar, Kolkata',\n",
              "       'Beniatola, Kolkata', 'Jugberia, Kolkata', 'Badartala, Kolkata',\n",
              "       'Hridaypur, Kolkata', 'Nabapally, Kolkata', 'Halisahar, Kolkata',\n",
              "       'Airport, Kolkata', 'Tiljala, Kolkata', 'Natunhat, Kolkata',\n",
              "       'Palta, Kolkata', 'Baruipur, Kolkata', 'Fatepur, Kolkata',\n",
              "       'Jagatipota, Kolkata', 'Naoabad, Kolkata', 'Sovabazar, Kolkata',\n",
              "       'Patipukur, Kolkata', 'Aurobindo Park, Kolkata',\n",
              "       'Ramchandrapur, Kolkata', 'Gobra, Kolkata', 'Baruipur P, Kolkata',\n",
              "       'Metiabruz, Kolkata', 'Vedic Village, Kolkata',\n",
              "       'Kanchrapara Loco, Kolkata', 'Basirhat, Kolkata',\n",
              "       'Malancha Mahi Nagar, Kolkata', 'Kazipara, Kolkata',\n",
              "       'Kustia, Kolkata', 'Doperia Village, Kolkata',\n",
              "       'Raja Bazar, Kolkata', 'Ruiya, Kolkata', 'Baithakkhana, Kolkata',\n",
              "       'Uttarbhag, Kolkata', 'Bow Barracks, Kolkata', 'Abdalpur, Kolkata',\n",
              "       'Ultadanga, Kolkata', 'Maidan, Kolkata', 'Narayantala, Kolkata',\n",
              "       'Kankurgachi, Kolkata'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.drop(['Area_Type', 'HOUSE_TYPE', 'Purpose', 'Owner_name', 'Owner_type'], axis=1, inplace=True)\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7f5D8Q8fA0B2",
        "outputId": "2d1c8f51-1ea4-4080-dfff-cfaccb7a59ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Flat_Price  EMI_Starts    BHK              Location Total_Sq.ft  \\\n",
              "0    ₹8.5 Cr  ₹4.22 Lacs  6 BHK   Ballygunge, Kolkata  4200 sq.ft   \n",
              "1    ₹45.0 L    ₹23.83 K  3 BHK  Barrackpore, Kolkata  1400 sq.ft   \n",
              "2   ₹1.35 Cr    ₹67.02 K  3 BHK   Santoshpur, Kolkata  2500 sq.ft   \n",
              "3    ₹60.0 L    ₹31.77 K  5 BHK      Sarsuna, Kolkata  1100 sq.ft   \n",
              "4    ₹52.0 L    ₹27.54 K  4 BHK  Madhyamgram, Kolkata   900 sq.ft   \n",
              "\n",
              "  Price_per_sq.ft  \n",
              "0  ₹20.24 K/sq.ft  \n",
              "1   ₹3.21 K/sq.ft  \n",
              "2   ₹5.40 K/sq.ft  \n",
              "3   ₹5.45 K/sq.ft  \n",
              "4   ₹5.78 K/sq.ft  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0b9a6de9-0ac2-4434-a200-24ebab8ddaaa\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Location</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>₹8.5 Cr</td>\n",
              "      <td>₹4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>₹20.24 K/sq.ft</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>₹45.0 L</td>\n",
              "      <td>₹23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>₹3.21 K/sq.ft</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>₹1.35 Cr</td>\n",
              "      <td>₹67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>₹5.40 K/sq.ft</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>₹60.0 L</td>\n",
              "      <td>₹31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>₹5.45 K/sq.ft</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>₹52.0 L</td>\n",
              "      <td>₹27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>₹5.78 K/sq.ft</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0b9a6de9-0ac2-4434-a200-24ebab8ddaaa')\"\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-0b9a6de9-0ac2-4434-a200-24ebab8ddaaa 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-0b9a6de9-0ac2-4434-a200-24ebab8ddaaa');\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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1.copy()"
      ],
      "metadata": {
        "id": "Jqm4GephBPvI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Triming the columns\n",
        "df2['Price_per_sq.ft'] = df2['Price_per_sq.ft'].apply(lambda x: x.rstrip(\"/sq.ft\"))\n",
        "df2['Price_per_sq.ft'] = df2['Price_per_sq.ft'].apply(lambda x: x.lstrip('₹'))\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a18MI9PEBWSI",
        "outputId": "c987062a-8677-4dbf-f9b7-a314f60fb3b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Flat_Price  EMI_Starts    BHK              Location Total_Sq.ft  \\\n",
              "0    ₹8.5 Cr  ₹4.22 Lacs  6 BHK   Ballygunge, Kolkata  4200 sq.ft   \n",
              "1    ₹45.0 L    ₹23.83 K  3 BHK  Barrackpore, Kolkata  1400 sq.ft   \n",
              "2   ₹1.35 Cr    ₹67.02 K  3 BHK   Santoshpur, Kolkata  2500 sq.ft   \n",
              "3    ₹60.0 L    ₹31.77 K  5 BHK      Sarsuna, Kolkata  1100 sq.ft   \n",
              "4    ₹52.0 L    ₹27.54 K  4 BHK  Madhyamgram, Kolkata   900 sq.ft   \n",
              "\n",
              "  Price_per_sq.ft  \n",
              "0         20.24 K  \n",
              "1          3.21 K  \n",
              "2          5.40 K  \n",
              "3          5.45 K  \n",
              "4          5.78 K  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-996d5678-3e96-4e50-ae37-a9b96e855d79\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Location</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>₹8.5 Cr</td>\n",
              "      <td>₹4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>20.24 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>₹45.0 L</td>\n",
              "      <td>₹23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>3.21 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>₹1.35 Cr</td>\n",
              "      <td>₹67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>5.40 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>₹60.0 L</td>\n",
              "      <td>₹31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>5.45 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>₹52.0 L</td>\n",
              "      <td>₹27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>5.78 K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-996d5678-3e96-4e50-ae37-a9b96e855d79')\"\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-996d5678-3e96-4e50-ae37-a9b96e855d79 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-996d5678-3e96-4e50-ae37-a9b96e855d79');\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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['EMI_Starts'] = df2['EMI_Starts'].apply(lambda x: x.lstrip('₹'))\n",
        "df2['Flat_Price'] = df2['Flat_Price'].apply(lambda x: x.lstrip('₹'))\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ar0xL2dZBwk4",
        "outputId": "ebc4f614-cf54-44af-a751-03fefe3e0114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Flat_Price EMI_Starts    BHK              Location Total_Sq.ft  \\\n",
              "0     8.5 Cr  4.22 Lacs  6 BHK   Ballygunge, Kolkata  4200 sq.ft   \n",
              "1     45.0 L    23.83 K  3 BHK  Barrackpore, Kolkata  1400 sq.ft   \n",
              "2    1.35 Cr    67.02 K  3 BHK   Santoshpur, Kolkata  2500 sq.ft   \n",
              "3     60.0 L    31.77 K  5 BHK      Sarsuna, Kolkata  1100 sq.ft   \n",
              "4     52.0 L    27.54 K  4 BHK  Madhyamgram, Kolkata   900 sq.ft   \n",
              "\n",
              "  Price_per_sq.ft  \n",
              "0         20.24 K  \n",
              "1          3.21 K  \n",
              "2          5.40 K  \n",
              "3          5.45 K  \n",
              "4          5.78 K  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-82b8f0c3-9b04-47a3-b380-c961037298ef\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Location</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.5 Cr</td>\n",
              "      <td>4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>20.24 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.0 L</td>\n",
              "      <td>23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>3.21 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.35 Cr</td>\n",
              "      <td>67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>5.40 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60.0 L</td>\n",
              "      <td>31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>5.45 K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0 L</td>\n",
              "      <td>27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>5.78 K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82b8f0c3-9b04-47a3-b380-c961037298ef')\"\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-82b8f0c3-9b04-47a3-b380-c961037298ef 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-82b8f0c3-9b04-47a3-b380-c961037298ef');\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": [
        "df2['Location'].str.split(',', 1, expand=True)[1].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eKm7oAunDsC-",
        "outputId": "78f1ad70-83f6-4d5b-cf2e-312b765703ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' Kolkata', nan, None, ''], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Location'] = df2['Location'].fillna('None')"
      ],
      "metadata": {
        "id": "X5G1mXs6DxIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6zIJhN_GDuC",
        "outputId": "18bee7cf-a9a6-4806-883d-876e7974475c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Flat_Price         0\n",
              "EMI_Starts         0\n",
              "BHK                0\n",
              "Location           0\n",
              "Total_Sq.ft        0\n",
              "Price_per_sq.ft    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['Region'] = df2['Location'].apply(lambda x: x.split(', ')[0])\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "e7Dhs7R7GGM-",
        "outputId": "b64a9cf8-071c-4271-c81d-e1a6b229b080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Flat_Price EMI_Starts    BHK                  Location Total_Sq.ft  \\\n",
              "0        8.5 Cr  4.22 Lacs  6 BHK       Ballygunge, Kolkata  4200 sq.ft   \n",
              "1        45.0 L    23.83 K  3 BHK      Barrackpore, Kolkata  1400 sq.ft   \n",
              "2       1.35 Cr    67.02 K  3 BHK       Santoshpur, Kolkata  2500 sq.ft   \n",
              "3        60.0 L    31.77 K  5 BHK          Sarsuna, Kolkata  1100 sq.ft   \n",
              "4        52.0 L    27.54 K  4 BHK      Madhyamgram, Kolkata   900 sq.ft   \n",
              "...         ...        ...    ...                       ...         ...   \n",
              "3963     13.0 L     6.88 K  2 BHK    Picnic Garden, Kolkata  1500 sq.ft   \n",
              "3964     1.5 Cr    74.47 K  8 BHK  Rajpur Sonarpur, Kolkata  2560 sq.ft   \n",
              "3965     50.0 L    26.48 K  5 BHK           Amtala, Kolkata  1900 sq.ft   \n",
              "3966     1.1 Cr    54.61 K  5 BHK       Tollygunge, Kolkata  2600 sq.ft   \n",
              "3967     30.0 L    15.89 K  2 BHK          Naihati, Kolkata   950 sq.ft   \n",
              "\n",
              "     Price_per_sq.ft           Region  \n",
              "0            20.24 K       Ballygunge  \n",
              "1             3.21 K      Barrackpore  \n",
              "2             5.40 K       Santoshpur  \n",
              "3             5.45 K          Sarsuna  \n",
              "4             5.78 K      Madhyamgram  \n",
              "...              ...              ...  \n",
              "3963             866    Picnic Garden  \n",
              "3964          5.86 K  Rajpur Sonarpur  \n",
              "3965          2.63 K           Amtala  \n",
              "3966          4.23 K       Tollygunge  \n",
              "3967          3.16 K          Naihati  \n",
              "\n",
              "[3968 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-726860dc-90ff-4967-8689-9a520094fe88\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Location</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.5 Cr</td>\n",
              "      <td>4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>Ballygunge, Kolkata</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>20.24 K</td>\n",
              "      <td>Ballygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.0 L</td>\n",
              "      <td>23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Barrackpore, Kolkata</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>3.21 K</td>\n",
              "      <td>Barrackpore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.35 Cr</td>\n",
              "      <td>67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Santoshpur, Kolkata</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>5.40 K</td>\n",
              "      <td>Santoshpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60.0 L</td>\n",
              "      <td>31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Sarsuna, Kolkata</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>5.45 K</td>\n",
              "      <td>Sarsuna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0 L</td>\n",
              "      <td>27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>Madhyamgram, Kolkata</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>5.78 K</td>\n",
              "      <td>Madhyamgram</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>13.0 L</td>\n",
              "      <td>6.88 K</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>Picnic Garden, Kolkata</td>\n",
              "      <td>1500 sq.ft</td>\n",
              "      <td>866</td>\n",
              "      <td>Picnic Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3964</th>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>74.47 K</td>\n",
              "      <td>8 BHK</td>\n",
              "      <td>Rajpur Sonarpur, Kolkata</td>\n",
              "      <td>2560 sq.ft</td>\n",
              "      <td>5.86 K</td>\n",
              "      <td>Rajpur Sonarpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>50.0 L</td>\n",
              "      <td>26.48 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Amtala, Kolkata</td>\n",
              "      <td>1900 sq.ft</td>\n",
              "      <td>2.63 K</td>\n",
              "      <td>Amtala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3966</th>\n",
              "      <td>1.1 Cr</td>\n",
              "      <td>54.61 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>Tollygunge, Kolkata</td>\n",
              "      <td>2600 sq.ft</td>\n",
              "      <td>4.23 K</td>\n",
              "      <td>Tollygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3967</th>\n",
              "      <td>30.0 L</td>\n",
              "      <td>15.89 K</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>Naihati, Kolkata</td>\n",
              "      <td>950 sq.ft</td>\n",
              "      <td>3.16 K</td>\n",
              "      <td>Naihati</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3968 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-726860dc-90ff-4967-8689-9a520094fe88')\"\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-726860dc-90ff-4967-8689-9a520094fe88 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-726860dc-90ff-4967-8689-9a520094fe88');\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": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.drop(['Location'], axis=1, inplace=True)\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "l_ACkj75Gdl7",
        "outputId": "6948353d-a4af-4031-eeb9-f9330963303a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Flat_Price EMI_Starts    BHK Total_Sq.ft Price_per_sq.ft           Region\n",
              "0        8.5 Cr  4.22 Lacs  6 BHK  4200 sq.ft         20.24 K       Ballygunge\n",
              "1        45.0 L    23.83 K  3 BHK  1400 sq.ft          3.21 K      Barrackpore\n",
              "2       1.35 Cr    67.02 K  3 BHK  2500 sq.ft          5.40 K       Santoshpur\n",
              "3        60.0 L    31.77 K  5 BHK  1100 sq.ft          5.45 K          Sarsuna\n",
              "4        52.0 L    27.54 K  4 BHK   900 sq.ft          5.78 K      Madhyamgram\n",
              "...         ...        ...    ...         ...             ...              ...\n",
              "3963     13.0 L     6.88 K  2 BHK  1500 sq.ft             866    Picnic Garden\n",
              "3964     1.5 Cr    74.47 K  8 BHK  2560 sq.ft          5.86 K  Rajpur Sonarpur\n",
              "3965     50.0 L    26.48 K  5 BHK  1900 sq.ft          2.63 K           Amtala\n",
              "3966     1.1 Cr    54.61 K  5 BHK  2600 sq.ft          4.23 K       Tollygunge\n",
              "3967     30.0 L    15.89 K  2 BHK   950 sq.ft          3.16 K          Naihati\n",
              "\n",
              "[3968 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5819afd2-8c4f-47df-838c-653295a0d8bd\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.5 Cr</td>\n",
              "      <td>4.22 Lacs</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>4200 sq.ft</td>\n",
              "      <td>20.24 K</td>\n",
              "      <td>Ballygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>45.0 L</td>\n",
              "      <td>23.83 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1400 sq.ft</td>\n",
              "      <td>3.21 K</td>\n",
              "      <td>Barrackpore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.35 Cr</td>\n",
              "      <td>67.02 K</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>2500 sq.ft</td>\n",
              "      <td>5.40 K</td>\n",
              "      <td>Santoshpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60.0 L</td>\n",
              "      <td>31.77 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>1100 sq.ft</td>\n",
              "      <td>5.45 K</td>\n",
              "      <td>Sarsuna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.0 L</td>\n",
              "      <td>27.54 K</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>900 sq.ft</td>\n",
              "      <td>5.78 K</td>\n",
              "      <td>Madhyamgram</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>13.0 L</td>\n",
              "      <td>6.88 K</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1500 sq.ft</td>\n",
              "      <td>866</td>\n",
              "      <td>Picnic Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3964</th>\n",
              "      <td>1.5 Cr</td>\n",
              "      <td>74.47 K</td>\n",
              "      <td>8 BHK</td>\n",
              "      <td>2560 sq.ft</td>\n",
              "      <td>5.86 K</td>\n",
              "      <td>Rajpur Sonarpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>50.0 L</td>\n",
              "      <td>26.48 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>1900 sq.ft</td>\n",
              "      <td>2.63 K</td>\n",
              "      <td>Amtala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3966</th>\n",
              "      <td>1.1 Cr</td>\n",
              "      <td>54.61 K</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>2600 sq.ft</td>\n",
              "      <td>4.23 K</td>\n",
              "      <td>Tollygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3967</th>\n",
              "      <td>30.0 L</td>\n",
              "      <td>15.89 K</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>950 sq.ft</td>\n",
              "      <td>3.16 K</td>\n",
              "      <td>Naihati</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3968 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5819afd2-8c4f-47df-838c-653295a0d8bd')\"\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-5819afd2-8c4f-47df-838c-653295a0d8bd 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-5819afd2-8c4f-47df-838c-653295a0d8bd');\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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2[(df2['Flat_Price'].str.split(' ', 1, expand=True)[1] == 'L - 1.03 Cr')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "VSMnysRPGkjD",
        "outputId": "636aeb25-ea48-4464-feb7-327e8b450712"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Flat_Price EMI_Starts BHK Total_Sq.ft Price_per_sq.ft Region\n",
              "148  99.83 L - 1.03 Cr    49.56 K       Jan, 2024          4.50 K   None\n",
              "168  99.83 L - 1.03 Cr    49.56 K       Jan, 2024          4.50 K   None"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-25c7430d-1439-4fbb-90e1-9a0cb7687cdf\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>99.83 L - 1.03 Cr</td>\n",
              "      <td>49.56 K</td>\n",
              "      <td></td>\n",
              "      <td>Jan, 2024</td>\n",
              "      <td>4.50 K</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>99.83 L - 1.03 Cr</td>\n",
              "      <td>49.56 K</td>\n",
              "      <td></td>\n",
              "      <td>Jan, 2024</td>\n",
              "      <td>4.50 K</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25c7430d-1439-4fbb-90e1-9a0cb7687cdf')\"\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-25c7430d-1439-4fbb-90e1-9a0cb7687cdf 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-25c7430d-1439-4fbb-90e1-9a0cb7687cdf');\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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.drop([148, 168], axis=0, inplace=True)"
      ],
      "metadata": {
        "id": "qf5x5wz0G5S_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_to_integer(row_value):\n",
        "  splitted_val = row_value.split(\" \")\n",
        "  amt = float(splitted_val[0])\n",
        "  if len(splitted_val) == 2:\n",
        "    unit = splitted_val[1]\n",
        "    if unit == 'K':\n",
        "      return 1000 * float(amt)\n",
        "    elif unit == 'L' or unit == 'Lacs':\n",
        "      return 100000 * float(amt)\n",
        "    elif unit == 'Cr':\n",
        "      return 10000000 * float(amt)\n",
        "  else:\n",
        "    return amt"
      ],
      "metadata": {
        "id": "aSeM46fFHEZ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2['EMI_Starts'] = df2.apply(lambda x: convert_to_integer(x['EMI_Starts']), axis=1)\n",
        "df2['Flat_Price'] = df2['Flat_Price'].apply(convert_to_integer)\n",
        "df2['Price_per_sq.ft'] = df2['Price_per_sq.ft'].apply(convert_to_integer)\n",
        "df2['Total_Sq.ft'] = df2['Total_Sq.ft'].apply(lambda x: float(x.split(' ')[0]))\n",
        "df2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "nbJNDMoMIdak",
        "outputId": "67ead629-f9c7-49f4-81f2-9d7334dbd241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Flat_Price  EMI_Starts    BHK  Total_Sq.ft  Price_per_sq.ft  \\\n",
              "0     85000000.0    422000.0  6 BHK       4200.0          20240.0   \n",
              "1      4500000.0     23830.0  3 BHK       1400.0           3210.0   \n",
              "2     13500000.0     67020.0  3 BHK       2500.0           5400.0   \n",
              "3      6000000.0     31770.0  5 BHK       1100.0           5450.0   \n",
              "4      5200000.0     27540.0  4 BHK        900.0           5780.0   \n",
              "...          ...         ...    ...          ...              ...   \n",
              "3963   1300000.0      6880.0  2 BHK       1500.0            866.0   \n",
              "3964  15000000.0     74470.0  8 BHK       2560.0           5860.0   \n",
              "3965   5000000.0     26480.0  5 BHK       1900.0           2630.0   \n",
              "3966  11000000.0     54610.0  5 BHK       2600.0           4230.0   \n",
              "3967   3000000.0     15890.0  2 BHK        950.0           3160.0   \n",
              "\n",
              "               Region  \n",
              "0          Ballygunge  \n",
              "1         Barrackpore  \n",
              "2          Santoshpur  \n",
              "3             Sarsuna  \n",
              "4         Madhyamgram  \n",
              "...               ...  \n",
              "3963    Picnic Garden  \n",
              "3964  Rajpur Sonarpur  \n",
              "3965           Amtala  \n",
              "3966       Tollygunge  \n",
              "3967          Naihati  \n",
              "\n",
              "[3966 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a60fa088-15cd-468a-b4d3-1022a8951040\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85000000.0</td>\n",
              "      <td>422000.0</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>4200.0</td>\n",
              "      <td>20240.0</td>\n",
              "      <td>Ballygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>23830.0</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>Barrackpore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>67020.0</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Santoshpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>31770.0</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>Sarsuna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>27540.0</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>Madhyamgram</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>1300000.0</td>\n",
              "      <td>6880.0</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>866.0</td>\n",
              "      <td>Picnic Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3964</th>\n",
              "      <td>15000000.0</td>\n",
              "      <td>74470.0</td>\n",
              "      <td>8 BHK</td>\n",
              "      <td>2560.0</td>\n",
              "      <td>5860.0</td>\n",
              "      <td>Rajpur Sonarpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>5000000.0</td>\n",
              "      <td>26480.0</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>2630.0</td>\n",
              "      <td>Amtala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3966</th>\n",
              "      <td>11000000.0</td>\n",
              "      <td>54610.0</td>\n",
              "      <td>5 BHK</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>4230.0</td>\n",
              "      <td>Tollygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3967</th>\n",
              "      <td>3000000.0</td>\n",
              "      <td>15890.0</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>950.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>Naihati</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3966 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a60fa088-15cd-468a-b4d3-1022a8951040')\"\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-a60fa088-15cd-468a-b4d3-1022a8951040 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-a60fa088-15cd-468a-b4d3-1022a8951040');\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": [
        "df2['BHK'] = df2['BHK'].apply(lambda x: float(x.split(' ')[0]))\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "E_zX_Y3bK2RA",
        "outputId": "64dcd522-ac23-4994-f834-38aecf1f8e81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Flat_Price  EMI_Starts  BHK  Total_Sq.ft  Price_per_sq.ft       Region\n",
              "0  85000000.0    422000.0  6.0       4200.0          20240.0   Ballygunge\n",
              "1   4500000.0     23830.0  3.0       1400.0           3210.0  Barrackpore\n",
              "2  13500000.0     67020.0  3.0       2500.0           5400.0   Santoshpur\n",
              "3   6000000.0     31770.0  5.0       1100.0           5450.0      Sarsuna\n",
              "4   5200000.0     27540.0  4.0        900.0           5780.0  Madhyamgram"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f918f9d9-4c8a-4b43-9f33-a06505bda184\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85000000.0</td>\n",
              "      <td>422000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4200.0</td>\n",
              "      <td>20240.0</td>\n",
              "      <td>Ballygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>23830.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>Barrackpore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>67020.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Santoshpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>31770.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>Sarsuna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>27540.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>Madhyamgram</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f918f9d9-4c8a-4b43-9f33-a06505bda184')\"\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-f918f9d9-4c8a-4b43-9f33-a06505bda184 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-f918f9d9-4c8a-4b43-9f33-a06505bda184');\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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target = df2['Flat_Price']\n",
        "df3 = df2.copy()"
      ],
      "metadata": {
        "id": "QCQVii0ikC1Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Encoding categorical columns"
      ],
      "metadata": {
        "id": "QovewNtapvRD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "label_encoder = preprocessing.LabelEncoder()"
      ],
      "metadata": {
        "id": "ImbwW1wFpyme"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3['Region'] = label_encoder.fit_transform(df3['Region'])\n",
        "region_mappings_le = {index: label for index, label in enumerate(label_encoder.classes_)}\n",
        "df3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-tlLPp0wpyos",
        "outputId": "74e9d174-bbd7-4ab9-8e39-f1ee02e9973d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Flat_Price  EMI_Starts  BHK  Total_Sq.ft  Price_per_sq.ft  Region\n",
              "0     85000000.0    422000.0  6.0       4200.0          20240.0      19\n",
              "1      4500000.0     23830.0  3.0       1400.0           3210.0      29\n",
              "2     13500000.0     67020.0  3.0       2500.0           5400.0     173\n",
              "3      6000000.0     31770.0  5.0       1100.0           5450.0     175\n",
              "4      5200000.0     27540.0  4.0        900.0           5780.0     121\n",
              "...          ...         ...  ...          ...              ...     ...\n",
              "3963   1300000.0      6880.0  2.0       1500.0            866.0     157\n",
              "3964  15000000.0     74470.0  8.0       2560.0           5860.0     166\n",
              "3965   5000000.0     26480.0  5.0       1900.0           2630.0       5\n",
              "3966  11000000.0     54610.0  5.0       2600.0           4230.0     194\n",
              "3967   3000000.0     15890.0  2.0        950.0           3160.0     132\n",
              "\n",
              "[3966 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edf4c483-2f2f-4a85-8c27-90f7b05293ef\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85000000.0</td>\n",
              "      <td>422000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4200.0</td>\n",
              "      <td>20240.0</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>23830.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>67020.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>31770.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>27540.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>1300000.0</td>\n",
              "      <td>6880.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>866.0</td>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3964</th>\n",
              "      <td>15000000.0</td>\n",
              "      <td>74470.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2560.0</td>\n",
              "      <td>5860.0</td>\n",
              "      <td>166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>5000000.0</td>\n",
              "      <td>26480.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>2630.0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3966</th>\n",
              "      <td>11000000.0</td>\n",
              "      <td>54610.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>4230.0</td>\n",
              "      <td>194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3967</th>\n",
              "      <td>3000000.0</td>\n",
              "      <td>15890.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>132</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3966 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-edf4c483-2f2f-4a85-8c27-90f7b05293ef')\"\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-edf4c483-2f2f-4a85-8c27-90f7b05293ef 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-edf4c483-2f2f-4a85-8c27-90f7b05293ef');\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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region_mappings_le"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_HO7UMYpysk",
        "outputId": "34bd2a26-e875-4996-edf8-9e8c7f7555c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Abdalpur',\n",
              " 1: 'Adarsha Nagar',\n",
              " 2: 'Agarpara',\n",
              " 3: 'Airport',\n",
              " 4: 'Alipore',\n",
              " 5: 'Amtala',\n",
              " 6: 'Ariadaha',\n",
              " 7: 'Ashokgarh',\n",
              " 8: 'Aurobindo Park',\n",
              " 9: 'Badartala',\n",
              " 10: 'Baghajatin',\n",
              " 11: 'Baghbazar',\n",
              " 12: 'Bagmari',\n",
              " 13: 'Bagnan',\n",
              " 14: 'Bagpota',\n",
              " 15: 'Baguiati',\n",
              " 16: 'Baguihati',\n",
              " 17: 'Baishnabghata Patuli Township,',\n",
              " 18: 'Baithakkhana',\n",
              " 19: 'Ballygunge',\n",
              " 20: 'Bamangachhi',\n",
              " 21: 'Bamunpara',\n",
              " 22: 'Bangaon',\n",
              " 23: 'Bansdroni',\n",
              " 24: 'Bantala',\n",
              " 25: 'Barabazar Market',\n",
              " 26: 'Baranagar',\n",
              " 27: 'Barasat',\n",
              " 28: 'Barisha',\n",
              " 29: 'Barrackpore',\n",
              " 30: 'Baruipur',\n",
              " 31: 'Baruipur P',\n",
              " 32: 'Basirhat',\n",
              " 33: 'Behala',\n",
              " 34: 'Belgachia',\n",
              " 35: 'Belghoria',\n",
              " 36: 'Beliaghata',\n",
              " 37: 'Beniapukur',\n",
              " 38: 'Beniatola',\n",
              " 39: 'Berunanpukhuria',\n",
              " 40: 'Bhatenda',\n",
              " 41: 'Bhatpara',\n",
              " 42: 'Bhowanipore',\n",
              " 43: 'Bijoygarh',\n",
              " 44: 'Bira',\n",
              " 45: 'Birati',\n",
              " 46: 'Bishnupur',\n",
              " 47: 'Bow Barracks',\n",
              " 48: 'Bow Bazaar',\n",
              " 49: 'Bramhapur',\n",
              " 50: 'Budge Budge',\n",
              " 51: 'Champahati',\n",
              " 52: 'Chandpara',\n",
              " 53: 'Chinar Park',\n",
              " 54: 'Chitpur',\n",
              " 55: 'Chowbaga',\n",
              " 56: 'College Square',\n",
              " 57: 'Cossipore',\n",
              " 58: 'Dakshin Gobindopur',\n",
              " 59: 'Dakshineswar',\n",
              " 60: 'Dhakuria',\n",
              " 61: 'Diamond Harbour',\n",
              " 62: 'Doperia Village',\n",
              " 63: 'Dum Dum',\n",
              " 64: 'Dum Dum Cantonment',\n",
              " 65: 'Dunlop',\n",
              " 66: 'Duttapukur',\n",
              " 67: 'East Kolkata Township',\n",
              " 68: 'Elgin',\n",
              " 69: 'Entally',\n",
              " 70: 'Fatepur',\n",
              " 71: 'Ganguly Bagan',\n",
              " 72: 'Garden Reach',\n",
              " 73: 'Garfa',\n",
              " 74: 'Garia',\n",
              " 75: 'Gariahat',\n",
              " 76: 'Garulia',\n",
              " 77: 'Gobra',\n",
              " 78: 'Golf Green',\n",
              " 79: 'Habra',\n",
              " 80: 'Halisahar',\n",
              " 81: 'Haltu',\n",
              " 82: 'Hanspukuria',\n",
              " 83: 'Hedua',\n",
              " 84: 'Hridaypur',\n",
              " 85: 'Hussainpur',\n",
              " 86: 'Ichapur',\n",
              " 87: 'Jadavpur',\n",
              " 88: 'Jagatipota',\n",
              " 89: 'Jodhpur Park',\n",
              " 90: 'Joka',\n",
              " 91: 'Jorabagan',\n",
              " 92: 'Jorasanko',\n",
              " 93: 'Jugberia',\n",
              " 94: 'Kabardanga',\n",
              " 95: 'Kaikhali',\n",
              " 96: 'Kalagachhia',\n",
              " 97: 'Kalighat',\n",
              " 98: 'Kalikapur',\n",
              " 99: 'Kalyan Nagar',\n",
              " 100: 'Kalyani',\n",
              " 101: 'Kamalgazi',\n",
              " 102: 'Kamardanga',\n",
              " 103: 'Kamdahari',\n",
              " 104: 'Kanchrapara Loco',\n",
              " 105: 'Kankurgachi',\n",
              " 106: 'Kasba',\n",
              " 107: 'Kashipur',\n",
              " 108: 'Kazipara',\n",
              " 109: 'Keshtopur',\n",
              " 110: 'Khardah',\n",
              " 111: 'Khariberia',\n",
              " 112: 'Khidirpur',\n",
              " 113: 'Kokapur',\n",
              " 114: 'Kolkata',\n",
              " 115: 'Kolutolla',\n",
              " 116: 'Krishnanagar',\n",
              " 117: 'Kustia',\n",
              " 118: 'Lake Gardens',\n",
              " 119: 'Lake Town',\n",
              " 120: 'Machuabazar',\n",
              " 121: 'Madhyamgram',\n",
              " 122: 'Maheshtala',\n",
              " 123: 'Maidan',\n",
              " 124: 'Malancha Mahi Nagar',\n",
              " 125: 'Malickpur',\n",
              " 126: 'Maniktala',\n",
              " 127: 'Metiabruz',\n",
              " 128: 'Mohispota',\n",
              " 129: 'Mominpore',\n",
              " 130: 'Mukundapur',\n",
              " 131: 'Nabapally',\n",
              " 132: 'Naihati',\n",
              " 133: 'Naoabad',\n",
              " 134: 'Narayantala',\n",
              " 135: 'Narendrapur',\n",
              " 136: 'Natagarh',\n",
              " 137: 'Natunhat',\n",
              " 138: 'Nayabad',\n",
              " 139: 'Nazirabad',\n",
              " 140: 'Netaji Nagar',\n",
              " 141: 'New Alipore',\n",
              " 142: 'New Barrakpur',\n",
              " 143: 'New Town',\n",
              " 144: 'Nimta',\n",
              " 145: 'North Dum Dum',\n",
              " 146: 'Paikpara',\n",
              " 147: 'Pailan',\n",
              " 148: 'Palta',\n",
              " 149: 'Pancha Sayar',\n",
              " 150: 'Panchpota',\n",
              " 151: 'Panihati',\n",
              " 152: 'Pansila',\n",
              " 153: 'Park Street Area',\n",
              " 154: 'Paschim Barisha',\n",
              " 155: 'Paschim Putiary',\n",
              " 156: 'Patipukur',\n",
              " 157: 'Picnic Garden',\n",
              " 158: 'Purba Barisha',\n",
              " 159: 'Purba Putiary',\n",
              " 160: 'Raghunathpur',\n",
              " 161: 'Rahara',\n",
              " 162: 'Raja Bazar',\n",
              " 163: 'Rajabagan',\n",
              " 164: 'Rajarhat',\n",
              " 165: 'Rajpur',\n",
              " 166: 'Rajpur Sonarpur',\n",
              " 167: 'Ramchandrapur',\n",
              " 168: 'Rania',\n",
              " 169: 'Regent Park',\n",
              " 170: 'Ruiya',\n",
              " 171: 'Saha Para',\n",
              " 172: 'Salt Lake City',\n",
              " 173: 'Santoshpur',\n",
              " 174: 'Sarada Pally',\n",
              " 175: 'Sarsuna',\n",
              " 176: 'Sewli Telinipara',\n",
              " 177: 'Shobhabazar',\n",
              " 178: 'Shyambazar',\n",
              " 179: 'Shyamnagar',\n",
              " 180: 'Sinthi',\n",
              " 181: 'Sodepur',\n",
              " 182: 'South Dum Dum',\n",
              " 183: 'Sovabazar',\n",
              " 184: 'Srirampur',\n",
              " 185: 'Tagore Park',\n",
              " 186: 'Tala',\n",
              " 187: 'Talbanda',\n",
              " 188: 'Taltala',\n",
              " 189: 'Tangra',\n",
              " 190: 'Taratala',\n",
              " 191: 'Thakuranir Chak',\n",
              " 192: 'Thakurpukur',\n",
              " 193: 'Tiljala',\n",
              " 194: 'Tollygunge',\n",
              " 195: 'Topsia',\n",
              " 196: 'Ultadanga',\n",
              " 197: 'Uttarbhag',\n",
              " 198: 'VIP Nagar',\n",
              " 199: 'Vedic Village',\n",
              " 200: 'Ward No 113'}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4 = df2.copy()"
      ],
      "metadata": {
        "id": "oHCCYFAtpywH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "AoNVRwzar3M1",
        "outputId": "eb43b8eb-fa49-452c-dfa8-38b1ac076460"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Flat_Price  EMI_Starts  BHK  Total_Sq.ft  Price_per_sq.ft  \\\n",
              "0     85000000.0    422000.0  6.0       4200.0          20240.0   \n",
              "1      4500000.0     23830.0  3.0       1400.0           3210.0   \n",
              "2     13500000.0     67020.0  3.0       2500.0           5400.0   \n",
              "3      6000000.0     31770.0  5.0       1100.0           5450.0   \n",
              "4      5200000.0     27540.0  4.0        900.0           5780.0   \n",
              "...          ...         ...  ...          ...              ...   \n",
              "3963   1300000.0      6880.0  2.0       1500.0            866.0   \n",
              "3964  15000000.0     74470.0  8.0       2560.0           5860.0   \n",
              "3965   5000000.0     26480.0  5.0       1900.0           2630.0   \n",
              "3966  11000000.0     54610.0  5.0       2600.0           4230.0   \n",
              "3967   3000000.0     15890.0  2.0        950.0           3160.0   \n",
              "\n",
              "               Region  \n",
              "0          Ballygunge  \n",
              "1         Barrackpore  \n",
              "2          Santoshpur  \n",
              "3             Sarsuna  \n",
              "4         Madhyamgram  \n",
              "...               ...  \n",
              "3963    Picnic Garden  \n",
              "3964  Rajpur Sonarpur  \n",
              "3965           Amtala  \n",
              "3966       Tollygunge  \n",
              "3967          Naihati  \n",
              "\n",
              "[3966 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8a47d111-4466-4d89-91c5-cae0560edfa1\">\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>Flat_Price</th>\n",
              "      <th>EMI_Starts</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85000000.0</td>\n",
              "      <td>422000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4200.0</td>\n",
              "      <td>20240.0</td>\n",
              "      <td>Ballygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>23830.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>Barrackpore</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>67020.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>Santoshpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>31770.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>Sarsuna</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>27540.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>Madhyamgram</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3963</th>\n",
              "      <td>1300000.0</td>\n",
              "      <td>6880.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>866.0</td>\n",
              "      <td>Picnic Garden</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3964</th>\n",
              "      <td>15000000.0</td>\n",
              "      <td>74470.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>2560.0</td>\n",
              "      <td>5860.0</td>\n",
              "      <td>Rajpur Sonarpur</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3965</th>\n",
              "      <td>5000000.0</td>\n",
              "      <td>26480.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>2630.0</td>\n",
              "      <td>Amtala</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3966</th>\n",
              "      <td>11000000.0</td>\n",
              "      <td>54610.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>4230.0</td>\n",
              "      <td>Tollygunge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3967</th>\n",
              "      <td>3000000.0</td>\n",
              "      <td>15890.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>950.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>Naihati</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3966 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a47d111-4466-4d89-91c5-cae0560edfa1')\"\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-8a47d111-4466-4d89-91c5-cae0560edfa1 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-8a47d111-4466-4d89-91c5-cae0560edfa1');\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": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature selection"
      ],
      "metadata": {
        "id": "-7rRBmBTpZbc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df3.corr(), cmap=\"YlGnBu\", annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "LHzq_hQIpc5I",
        "outputId": "1468e992-6cc6-4feb-ccb4-429049cb5ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5 = df3.drop('EMI_Starts', axis=1)\n",
        "df5.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2ux-2eutpdEX",
        "outputId": "a66f9966-5cb1-402d-a212-5d2b07304315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Flat_Price  BHK  Total_Sq.ft  Price_per_sq.ft  Region\n",
              "0  85000000.0  6.0       4200.0          20240.0      19\n",
              "1   4500000.0  3.0       1400.0           3210.0      29\n",
              "2  13500000.0  3.0       2500.0           5400.0     173\n",
              "3   6000000.0  5.0       1100.0           5450.0     175\n",
              "4   5200000.0  4.0        900.0           5780.0     121"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5a5af2f-c28c-44bb-9d94-00bf2984d525\">\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>Flat_Price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>85000000.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>4200.0</td>\n",
              "      <td>20240.0</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5a5af2f-c28c-44bb-9d94-00bf2984d525')\"\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-c5a5af2f-c28c-44bb-9d94-00bf2984d525 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-c5a5af2f-c28c-44bb-9d94-00bf2984d525');\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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0_dTSr3gpdLl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing outliers"
      ],
      "metadata": {
        "id": "nBcJG68f3NUt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 5, figsize=(20, 5))\n",
        "\n",
        "for col, i in zip(df5.select_dtypes(np.number).columns, ax.flatten()):\n",
        "  i.set_xlabel(col)\n",
        "  sns.boxplot(data=df5[col], ax=i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "qvAjFgs93QB2",
        "outputId": "347fc808-5a15-48df-ed6f-174537feb705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 5 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5.shape"
      ],
      "metadata": {
        "id": "zoAvjZN03QMH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c3e2692-019c-4eb1-860a-17e10ca9f0fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3966, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5.drop(df5[df5['Flat_Price'] > 1.50*10000000].index, inplace=True)\n",
        "df5.drop(df5[df5['BHK'] > 8].index, inplace=True)\n",
        "df5.drop(df5[df5['Total_Sq.ft'] > 3500].index, inplace=True)\n",
        "df5.drop(df5[df5['Price_per_sq.ft'] >= 8500].index, inplace=True)\n",
        "df5.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EeYgx3ii8in0",
        "outputId": "1c46a107-ab20-4545-977a-f16cbfcde2a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2411, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yJxed8YhMuY_",
        "outputId": "f09e6970-355f-47b7-ed07-ca2a8c3c04bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Flat_Price          BHK  Total_Sq.ft  Price_per_sq.ft       Region\n",
              "count  2.411000e+03  2411.000000  2411.000000      2411.000000  2411.000000\n",
              "mean   6.339897e+06     3.661551  1665.318540      3966.788055   101.161344\n",
              "std    3.377077e+06     1.581182   741.651304      1621.455922    57.773812\n",
              "min    1.350000e+05     1.000000   200.000000       156.000000     0.000000\n",
              "25%    3.800000e+06     2.000000  1100.000000      2780.000000    45.000000\n",
              "50%    5.900000e+06     3.000000  1500.000000      3750.000000   101.000000\n",
              "75%    8.500000e+06     5.000000  2160.000000      5000.000000   157.000000\n",
              "max    1.500000e+07     8.000000  3500.000000      8460.000000   200.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7335ba11-d42d-4212-b731-edefa139a101\">\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>Flat_Price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2.411000e+03</td>\n",
              "      <td>2411.000000</td>\n",
              "      <td>2411.000000</td>\n",
              "      <td>2411.000000</td>\n",
              "      <td>2411.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.339897e+06</td>\n",
              "      <td>3.661551</td>\n",
              "      <td>1665.318540</td>\n",
              "      <td>3966.788055</td>\n",
              "      <td>101.161344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.377077e+06</td>\n",
              "      <td>1.581182</td>\n",
              "      <td>741.651304</td>\n",
              "      <td>1621.455922</td>\n",
              "      <td>57.773812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.350000e+05</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.800000e+06</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1100.000000</td>\n",
              "      <td>2780.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.900000e+06</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1500.000000</td>\n",
              "      <td>3750.000000</td>\n",
              "      <td>101.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.500000e+06</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2160.000000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>157.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.500000e+07</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>3500.000000</td>\n",
              "      <td>8460.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7335ba11-d42d-4212-b731-edefa139a101')\"\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-7335ba11-d42d-4212-b731-edefa139a101 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-7335ba11-d42d-4212-b731-edefa139a101');\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 5, figsize=(20, 5))\n",
        "# plt.title('After removinf outliers')\n",
        "for col, i in zip(df5.columns, ax.flatten()):\n",
        "  i.set_xlabel(col)\n",
        "  sns.boxplot(data=df5[col].values, ax=i)\n",
        "# df5['Flat_Price'].unique() "
      ],
      "metadata": {
        "id": "epoNLwOS3Qe2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "outputId": "2ba796dc-538d-43b1-b063-493f44bca5f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 5 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABIEAAAFJCAYAAAAMtT2/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAA8R0lEQVR4nO3de5xddX3v/9ebBBGviEz50UAMmqgHb1GnSKvtD8VLxP6M/o6XcFoFS5vqD2g82h4v7UMsQo/WC0WreKJQoLUg3mqOjRcOYtFWLkEQuSkjF8kUSUoA0SAa+Pz+WGvoZpzJTJI9s2fPfj0fj/2Ytb7ru9b6bB58s/b+7O8lVYUkSZIkSZLmt916HYAkSZIkSZJmnkkgSZIkSZKkAWASSJIkSZIkaQCYBJIkSZIkSRoAJoEkSZIkSZIGgEkgSZIkSZKkAdDTJFCS05NsSnLVNOqenOSK9vWDJHfOQoiSJEmSJEnzQqqqdzdPfgf4KXBWVT11B847DnhmVf3BjAUnSZIkSZI0j/S0J1BVXQhs6SxL8oQkX0lyWZJvJnnyBKceAZw9K0FKkiRJkiTNAwt7HcAE1gJvrKrrkzwH+BjwgrGDSR4HHAh8vUfxSZIkSZIk9Z05lQRK8gjgt4DPJBkr3mNctVXAZ6vqvtmMTZIkSZIkqZ/NqSQQzfC0O6tq+XbqrAKOmZ1wJEmSJEmS5oc5tUR8Vf0EuDHJqwHSeMbY8XZ+oMcA3+5RiJIkSZIkSX2p10vEn02T0HlSko1JjgZ+Dzg6yXeBq4GVHaesAs6pXi5pJkmSJEmS1Id6ukS8JEmSJEmSZsecGg4mSZIkSZKkmWESSJIkSZIkaQD0bHWwffbZp5YsWdKr20s9d9lll/1HVQ31Oo7xbJsadLZNae6Zq+0SbJsabLZNaW7aXtvsWRJoyZIlbNiwoVe3l3ouyc29jmEitk0NOtumNPfM1XYJtk0NNtumNDdtr206HEySJEmSJGkAmASSJEmSJEkaACaBJEmSJEmSBoBJIEmSJEmSpAFgEkiSJEmSJGkAmASSJEmSJEkaACaBJEmSJPWdkZERXvaylzEyMtLrUCSpb5gEkua5JP89ydVJrkpydpKH9jomqV8leWiSS5J8t21Xf9mWn5HkxiRXtK/lbXmSfDjJSJIrkzyr41pHJrm+fR3Zo7ckSX3rxBNP5Gc/+xknnnhir0PRDkpyQJILklzTPk/XtOV7JzmvfTael+Qxbfmkz1NJO8YkkDSPJVkE/AkwXFVPBRYAq3obldTX7gVeUFXPAJYDK5Ic0h77s6pa3r6uaMteCixrX6uBU6H5kAscDzwHOBg4fuyDriRpaiMjI9x0000A3HTTTfYG6j/bgLdW1UHAIcAxSQ4C3g6cX1XLgPPbfZjkeSppxy3sdQDz2Uc+8pE5+UAaHR0FYNGiRT2OZGJLly7luOOO63UY88lCYM8kvwQeBvx7j+PpOdvmzrFtQlUV8NN2d/f2Vds5ZSVwVnveRUn2SrIfcChwXlVtAUhyHrACOHumYu8Hts2dY9vUIBrf++fEE0/kjDPO6E0w2mFVdStwa7t9d5JrgUU0z81D22pnAt8A3sYkz9P2OgPL5+aO85lpT6CBdM8993DPPff0OgzNgqoaBT4A/IjmQXtXVX1tfL0kq5NsSLJh8+bNsx2mWrbN/pBkQZIrgE00iZyL20MntV3UT06yR1u2CLil4/SNbdlk5ePvZducA2yb0twz1gtosn31jyRLgGcCFwP7diR2fgzs22773OwjPjfnNnsCzaC5mmFcs2YNAKecckqPI9FMa4eXrAQOBO4EPpPk96vqHzrrVdVaYC3A8PDw9no1zAu2Te2KqroPWJ5kL+ALSZ4KvIPmw+pDaNrS24ATunAv2+YcYNuU5p6FCxeybdu2B+2r/yR5BPA54M1V9ZMkDxyrqkqyQ88+n5tzg8/Nuc2eQNL89kLgxqraXFW/BD4P/FaPY5Lmhaq6E7gAWFFVt1bjXuDvaOb5ARgFDug4bf+2bLJySdI0dCaAJtrX3Jdkd5oE0Keq6vNt8W3tsGnav5vacp+bUpeYMpfmtx8BhyR5GHAPcBiwobchSf0ryRDwy6q6M8mewIuA943NS5DmJ8xXAFe1p6wDjk1yDs0k0He19b4K/FXHZNAvpulNJElzzlycd2SPPfbg3nvvfdD+WO+DucK5RybXPi9PA66tqg91HFoHHAm8t/37xY7yX3mezmLI0rxhEkiax6rq4iSfBb5DswrD5bRdZCXtlP2AM5MsoOlNe25VfSnJ19sEUYArgDe29dcDhwMjwFbgDQBVtSXJe4BL23onjE0SLUma2uLFi7n++usf2H/c4x7Xw2i0E54LvA74XjvPHsA7aZI/5yY5GrgZeE17bMLnqaQdZxJImueq6niapagl7aKqupJm8srx5S+YpH4Bx0xy7HTg9K4GKEkzYK72ZnnJS17Cvffey5IlS1i71t+4+klVfYvmh5OJHDZB/Umfp5J2jHMCSZIkSeo7ixcvZrfdduMv/uIveh2KJPUNk0CSJEmS+s7DHvYwnva0p7F06dJehyJJfcMkkCRJkiRJ0gAwCSRJkiRJkjQATAJJkiSp7yX570muTnJVkrOTPDTJgUkuTjKS5NNJHtLW3aPdH2mPL+m4zjva8u8neUnP3pAkSTPAJJAkSZL6WpJFwJ8Aw1X1VGABsAp4H3ByVS0F7gCObk85GrijLT+5rUeSg9rzngKsAD6WZMFsvhdJkmaSSSBJkiTNBwuBPZMsBB4G3Aq8APhse/xM4BXt9sp2n/b4YUnSlp9TVfdW1Y3ACHDw7IQvSdLMmzIJlOT0JJuSXDVFvd9Isi3Jq7oXniRJkrR9VTUKfAD4EU3y5y7gMuDOqtrWVtsILGq3FwG3tOdua+s/trN8gnMkSep70+kJdAZNd9hJtd1k3wd8rQsxSZIkSdOW5DE0vXgOBH4deDhTfH7dxfutTrIhyYbNmzfP1G0kSeq6KZNAVXUhsGWKascBnwM2dSMoSZIkaQe8ELixqjZX1S+BzwPPBfZqh4cB7A+MttujwAEA7fFHA7d3lk9wzgOqam1VDVfV8NDQ0Ey8H0mSZsQuzwnUTsT3SuDUXQ9HkiRJ2mE/Ag5J8rB2bp/DgGuAC4CxqQqOBL7Ybq9r92mPf72qqi1f1a4ediCwDLhklt6DJEkzbuHUVab0N8Dbqur+5pk7uSSrgdUAixcv7sKtJUmSNOiq6uIknwW+A2wDLgfWAv8MnJPkxLbstPaU04C/TzJC0+N9VXudq5OcS5NA2gYcU1X3zeqbkSRpBnUjCTRM83AF2Ac4PMm2qvqn8RWrai3NA5nh4eHqwr0lSZIkqup44PhxxTcwwepeVfVz4NWTXOck4KSuByhJ0hywy0mgqjpwbDvJGcCXJkoASZIkSZIkqXemTAIlORs4FNgnyUaaX1h2B6iqj89odJIkSZIkSeqKKZNAVXXEdC9WVUftUjSSJEmSJEmaEbu8OpgkSZIkSZLmPpNAkiRJkiRJA8AkkCRJkiRJ0gAwCSRJkiRJkjQATAJJkiRJkiQNAJNAkiRJkqRZk+T0JJuSXNVR9ukkV7Svm5Jc0ZYvSXJPx7GP9yxwaR6Ycol4SZIkSZK66Azgb4Gzxgqq6rVj20k+CNzVUf+HVbV8toKT5jOTQJIkSZKkWVNVFyZZMtGxJAFeA7xgVoOSBoTDwSRJkiRJc8VvA7dV1fUdZQcmuTzJvyT57V4FJs0H9gSSJEmSJM0VRwBnd+zfCiyuqtuTPBv4pyRPqaqfjD8xyWpgNcDixYtnJVip39gTSJrnkjypYyK9K5L8JMmbex2XJEmS1CnJQuD/BT49VlZV91bV7e32ZcAPgSdOdH5Vra2q4aoaHhoamo2Qpb5jTyBpnquq7wPLAZIsAEaBL/QyJkmSJGkCLwSuq6qNYwVJhoAtVXVfkscDy4AbehWg1O/sCSQNlsNoVle4udeBSP0oyUOTXJLku0muTvKXbfmBSS5OMtIucfuQtnyPdn+kPb6k41rvaMu/n+QlPXpLkiTNuiRnA98GnpRkY5Kj20OrePBQMIDfAa5sl4z/LPDGqtoya8FK84w9gaTBMtGDVdL03Qu8oKp+mmR34FtJvgy8BTi5qs5J8nHgaODU9u8dVbU0ySrgfcBrkxxE0x6fAvw68H+SPLGq7uvFm5IkaTZV1RGTlB81QdnngM/NdEzSoLAnkDQg2p4JLwc+M8Gx1Uk2JNmwefPm2Q9O6hPV+Gm7u3v7KpplbD/blp8JvKLdXtnu0x4/rF36diVwTjvPwY3ACHDwzL8DSZIkDTKTQNLgeCnwnaq6bfwBJ9GTpi/JgrZL+ibgPJoJKu+sqm1tlY3AonZ7EXALQHv8LuCxneUTnCNJkiTNCJNA0uAYv9ympJ1QVfdV1XJgf5reO0+eqXvZS0+SJEndZBJIGgBJHg68CPh8r2OR5ouquhO4APhNYK92WVtokkOj7fYocAA8sOzto4HbO8snOKfzHvbSkyRJUteYBJIGQFX9rKoeW1V39ToWqZ8lGUqyV7u9J01y9VqaZNCr2mpHAl9st9e1+7THv15V1ZavalcPO5BmudtLZuVNSJIkaWC5OpgkSdO3H3BmkgU0P6ScW1VfSnINcE6SE4HLgdPa+qcBf59kBNhCsyIYVXV1knOBa4BtwDGuDCZJkqSZZhJIkqRpqqorgWdOUH4DE6zuVVU/B149ybVOAk7qdoySJEnSZBwOJkmSJEmSNABMAkmSJEmSJA0Ak0CSJEmSJEkDYMokUJLTk2xKctUkx38vyZVJvpfk35I8o/thSpIkSZIkaVdMpyfQGcCK7Ry/Efi/q+ppwHuAtV2IS5IkSZIkSV005epgVXVhkiXbOf5vHbsXAft3IS5JkiRJkiR1UbfnBDoa+HKXrylJkiRJkqRdNGVPoOlK8nyaJNDztlNnNbAaYPHixd26tSRJkiRJkqbQlZ5ASZ4OfBJYWVW3T1avqtZW1XBVDQ8NDXXj1pIkSZIkSZqGXU4CJVkMfB54XVX9YNdDkiRJkiRJUrdNORwsydnAocA+STYCxwO7A1TVx4F3AY8FPpYEYFtVDc9UwJIkSZIkzYaPfOQjjIyM9DqMvjL232vNmjU9jqS/LF26lOOOO27G7zOd1cGOmOL4HwJ/2LWIJEmSJEmaA0ZGRrjiqmu572F79zqUvrHbLwqAy264rceR9I8FW7fM2r26NjG0JEmSJEnzzX0P25t7nnx4r8PQPLbndetn7V7dXiJekiRJkiRJc5BJIEmSJEmSpAFgEkiSJEmSNGuSnJ5kU5KrOsrenWQ0yRXt6/COY+9IMpLk+0le0puopfnBJJAkSZIkaTadAayYoPzkqlrevtYDJDkIWAU8pT3nY0kWzFqk0jxjEkiSJEmSNGuq6kJgusshrQTOqap7q+pGYAQ4eMaCk+Y5k0CSJEmSpLng2CRXtsPFHtOWLQJu6aizsS2TtBNMAkmSJEmSeu1U4AnAcuBW4IM7eoEkq5NsSLJh8+bNXQ5Pmh9MAkmSJEmSeqqqbquq+6rqfuAT/OeQr1HggI6q+7dlE11jbVUNV9Xw0NDQzAYs9SmTQJIkSZKknkqyX8fuK4GxlcPWAauS7JHkQGAZcMlsxyfNFwt7HYAkSZIkaXAkORs4FNgnyUbgeODQJMuBAm4C/higqq5Oci5wDbANOKaq7utB2NK8YBJIkiRJkjRrquqICYpP2079k4CTZi4iaXA4HEya55LsleSzSa5Lcm2S3+x1TJIkSZKk2WdPIGn+OwX4SlW9KslDgIf1OiBJkiRJ0uwzCSTNY0keDfwOcBRAVf0C+EUvY5IkSZIk9YbDwaT57UBgM/B3SS5P8skkD+91UJIkddtEw5+T7J3kvCTXt38f09ZNkg8nGUlyZZJndVznyLb+9UmO7N07kiSp+0wCSfPbQuBZwKlV9UzgZ8Dbx1dKsjrJhiQbNm/ePNsxSn0hyQFJLkhyTZKrk6xpy9+dZDTJFe3r8I5z3tF+yfx+kpd0lK9oy0aS/EqblLRTxoY/Pxl4BnAtzTPv/KpaBpzPfz4DX0qzzPQyYDVwKkCSvWlWKXoOcDBw/FjiSJKk+cAkkDS/bQQ2VtXF7f5naZJCD1JVa6tquKqGh4aGZjVAqY9sA95aVQcBhwDHJDmoPXZyVS1vX+sB2mOrgKcAK4CPJVmQZAHwUZovoQcBR3RcR9JO6Bj+fBo0w5+r6k5gJXBmW+1M4BXt9krgrGpcBOyVZD/gJcB5VbWlqu4AzqNpv5IkzQsmgaR5rKp+DNyS5Elt0WHANT0MSepbVXVrVX2n3b6bppfBou2cshI4p6ruraobgRGangUHAyNVdUM7T9c5bV1JO2+y4c/7VtWtbZ0fA/u224uAWzrO39iWTVYuSdK8YBJImv+OAz6V5EpgOfBXvQ1H6n9JlgDPBMZ62R3bzityesfQEb9kSrNnyuHPVVVAdeNmDqOWJPUrk0DSPFdVV7RDvZ5eVa9ou7dL2klJHgF8DnhzVf2EZi6RJ9AkWW8FPtjFe/lFU5qeyYY/39YO86L9u6k9Pgoc0HH+/m3ZZOUP4jBqSVK/MgkkSdI0JdmdJgH0qar6PEBV3VZV91XV/cAnaIZ7wS5+yWyv7RdNaRq2M/x5HTC2wteRwBfb7XXA69tVwg4B7mqHjX0VeHGSx7S9+l7clkmSNC8s7HUAkiT1gyShmXT22qr6UEf5fh1zjrwSuKrdXgf8Y5IPAb9OswrRJUCAZUkOpEn+rAL+2+y8C2leGxv+/BDgBuANND94npvkaOBm4DVt3fXA4TRzdW1t61JVW5K8B7i0rXdCVW2ZvbcgSdLMMgkkSdL0PBd4HfC9JFe0Ze+kWd1rOc1cIzcBfwxQVVcnOZemN8I24Jiqug8gybE0vQsWAKdX1dWz9zak+amqrgCGJzh02AR1CzhmkuucDpze1eAkSZojTAJJkjQNVfUtml48463fzjknASdNUL5+e+dJkiRJM2HKOYHalU42JblqkuNJ8uEkI+3KKM/qfpiSJEmSJEnaFdOZGPoMYMV2jr+UZp6DZcBqmlVSJEmSJEmSNIdMmQSqqguB7U2ItxI4qxoXAXuNLcUpSZIkSZKkuaEbS8QvAm7p2N/YlkmSJEmSJGmO6EYSaNqSrE6yIcmGzZs3z+atJUmSJEmSBlo3kkCjwAEd+/u3Zb+iqtZW1XBVDQ8NDXXh1pIkSZIkSZqObiSB1gGvb1cJOwS4q6pu7cJ1JUmSJEmS1CULp6qQ5GzgUGCfJBuB44HdAarq48B64HBgBNgKvGGmgpUkSZIkSdLOmTIJVFVHTHG8gGO6FpEkSZIkSZK6blYnhpYkSZIkDbYkpyfZlOSqjrL3J7kuyZVJvpBkr7Z8SZJ7klzRvj7es8ClecAkkCRJkiRpNp0BrBhXdh7w1Kp6OvAD4B0dx35YVcvb1xtnKUZpXjIJJEmSJEmaNVV1IbBlXNnXqmpbu3sRzarTkrrMJJAkSZIkaS75A+DLHfsHJrk8yb8k+e1eBSXNB1NODC1JkiRJ0mxI8ufANuBTbdGtwOKquj3Js4F/SvKUqvrJBOeuBlYDLF68eLZClvqKPYEkSZIkST2X5Cjgd4Hfa1ehpqrurarb2+3LgB8CT5zo/KpaW1XDVTU8NDQ0S1FL/cUkkCRJkiSpp5KsAP4H8PKq2tpRPpRkQbv9eGAZcENvopT6n8PBJEmSJEmzJsnZwKHAPkk2AsfTrAa2B3BeEoCL2pXAfgc4IckvgfuBN1bVlgkvLGlKJoEkSZIkSbOmqo6YoPi0Sep+DvjczEYkDQ6Hg0mSJEmSJA0Ak0CSJEmSJEkDwOFgkiQNkI985COMjIz0Ooy+Mvbfa82aNT2OpL8sXbqU4447rtdhSJKkDiaBJEkaICMjI1xx1bXc97C9ex1K39jtFwXAZTfc1uNI+seCrc7ZKknSXGQSSBoASW4C7gbuA7ZV1XBvI5LUS/c9bG/uefLhvQ5D89ie163vdQh9x156O85eejvHXnrSYDMJJA2O51fVf/Q6CEmS9Kvspbfj7KW34+ylJ8kkkCRJkjQH2EtPM81eepJMAkmDoYCvJSngf1XV2pm+od3ad5zd2neO3dolSZKk6TEJJA2G51XVaJJfA85Lcl1VXTh2MMlqYDXA4sWLu3JDu7XvOLu17zi7tUuSJEnT1/dJIHsb7Dh7G+ycfu5tUFWj7d9NSb4AHAxc2HF8LbAWYHh4uLp1X7u1a6bZrV2SJEmavr5PAtnbYMfZ22DH9XNvgyQPB3arqrvb7RcDJ/Q4LEmSJEnSLOv7JBDY20Azr897G+wLfCEJNG3+H6vqK70NSepPSQ4AzqJpVwWsrapTkuwNfBpYAtwEvKaq7kjT8E4BDge2AkdV1Xfaax0J/EV76ROr6szZfC+SJEkaPPMiCSRpclV1A/CMXschzRPbgLdW1XeSPBK4LMl5wFHA+VX13iRvB94OvA14KbCsfT0HOBV4Tps0Oh4YpkkmXZZkXVXdMevvSJIkSQNjt14HIElSv6iqW8d68lTV3cC1wCJgJTDWk+dM4BXt9krgrGpcBOyVZD/gJcB5VbWlTfycB6yYvXciSZKkQWQSSJKknZBkCfBM4GJg36q6tT30Y5rhYtAkiG7pOG1jWzZZuSRJkjRjppUESrIiyfeTjLTd3McfX5zkgiSXJ7kyiRP0SJLmrSSPAD4HvLmqftJ5rKqKZohXN+6zOsmGJBs2b97cjUtKkiRpgE2ZBEqyAPgozbwGBwFHJDloXLW/AM6tqmcCq4CPdTtQSZLmgiS70ySAPlVVn2+Lb2uHedH+3dSWjwIHdJy+f1s2WfmDVNXaqhququGhoaHuvhFJkiQNnOn0BDoYGKmqG6rqF8A5NHMcdCrgUe32o4F/716IkiTNDe1qX6cB11bVhzoOrQOObLePBL7YUf76NA4B7mqHjX0VeHGSxyR5DPDitkySJEmaMdNZHWyieQueM67Ou4GvJTkOeDjwwq5EJ0nS3PJc4HXA95Jc0Za9E3gvcG6So4Gbgde0x9bTLA8/QrNE/BsAqmpLkvcAl7b1TqiqLbPyDiRJkjSwurVE/BHAGVX1wSS/Cfx9kqdW1f2dlZKsBlYDLF68uEu3liRpdlTVt4BMcviwCeoXcMwk1zodOL170UmSJEnbN53hYNOZt+Bo4FyAqvo28FBgn/EXcm4DSZIkSZKk3phOEuhSYFmSA5M8hGbi53Xj6vyI9hfQJP+FJgnkMiaSJEmSJElzxJRJoKraBhxLM2HltTSrgF2d5IQkL2+rvRX4oyTfBc4Gjmq7wEuSJEmS9IAkpyfZlOSqjrK9k5yX5Pr272Pa8iT5cJKRJFcmeVbvIpf633R6AlFV66vqiVX1hKo6qS17V1Wta7evqarnVtUzqmp5VX1tJoOWJEmSJPWtM4AV48reDpxfVcuA89t9gJcCy9rXauDUWYpRmpe6NTG0JEmSJElTqqoLkywZV7wSOLTdPhP4BvC2tvysdqTJRUn2SrJfVd06G7GOjo6yYOtd7Hnd+tm4nQbUgq23Mzq6bVbuNa2eQJIkSZIkzaB9OxI7Pwb2bbcXAbd01NvYlknaCfYEkiRJkiTNGVVVSXZ4jtkkq2mGjLF48eKuxLJo0SJ+fO9C7nny4V25njSRPa9bz6JF+05dsQvsCSRJkiRJ6rXbkuwH0P7d1JaPAgd01Nu/LfsVVbW2qoaranhoaGhGg5X6lUkgSZIkSVKvrQOObLePBL7YUf76dpWwQ4C7Zms+IGk+cjiYJEmSJGnWJDmbZhLofZJsBI4H3gucm+Ro4GbgNW319cDhwAiwFXjDrAcszSMmgSRJkiRJs6aqjpjk0GET1C3gmJmNSBocDgeTJElS30uyIMnlSb7U7h+Y5OIkI0k+neQhbfke7f5Ie3xJxzXe0ZZ/P8lLevRWJEmaMSaBJEmSNB+sAa7t2H8fcHJVLQXuAI5uy48G7mjLT27rkeQgYBXwFGAF8LEkC2YpdkmSZoVJIEmSJPW1JPsDLwM+2e4HeAHw2bbKmcAr2u2V7T7t8cPa+iuBc6rq3qq6kWb+kYNn5Q1IkjRLTAJJkiSp3/0N8D+A+9v9xwJ3VtW2dn8jsKjdXgTcAtAev6ut/0D5BOdIkjQvmASSJElS30ryu8CmqrpsFu+5OsmGJBs2b948W7eVJGmXmQSSJElSP3su8PIkNwHn0AwDOwXYK8nYSrj7A6Pt9ihwAEB7/NHA7Z3lE5zzIFW1tqqGq2p4aGiou+9GkqQZZBJIkiRJfauq3lFV+1fVEpqJnb9eVb8HXAC8qq12JPDFdntdu097/OvtEtTrgFXt6mEHAsuAS2bpbUiSNCsWTl1FkiRJ6jtvA85JciJwOXBaW34a8PdJRoAtNIkjqurqJOcC1wDbgGOq6r7ZD1uSpJljEkiSJEnzQlV9A/hGu30DE6zuVVU/B149yfknASfNXISSJPWWw8GkAZBkQZLLk3yp17FIkiRJknrDJJA0GNYA1/Y6CEmSJElS7zgcTJrnkuwPvIyme/tbehyOJEmawOjoKAu23sWe163vdSiaxxZsvZ3R0W29DkNSD9kTSJr//gb4H8D9PY5DkiRJktRD9gSS5rEkvwtsqqrLkhy6nXqrgdUAixcvnp3gJEnSAxYtWsSP713IPU8+vNehaB7b87r1LFq0b6/DkNRD9gSS5rfnAi9PchNwDvCCJP8wvlJVra2q4aoaHhoamu0YJUmSJEmzwCSQNI9V1Tuqav+qWgKsAr5eVb/f47AkSZIkST1gEkiSJEmSJGkATCsJlGRFku8nGUny9knqvCbJNUmuTvKP3Q1T0q6qqm9U1e/2Og6pnyU5PcmmJFd1lL07yWiSK9rX4R3H3tE+O7+f5CUd5VM+VyVJkqRum3Ji6CQLgI8CLwI2ApcmWVdV13TUWQa8A3huVd2R5NdmKmBJknroDOBvgbPGlZ9cVR/oLEhyEM0wzKcAvw78nyRPbA9v97kqSZIkzYTp9AQ6GBipqhuq6hc0k8uuHFfnj4CPVtUdAFW1qbthSpLUe1V1IbBlmtVXAudU1b1VdSMwQvNMnc5zVZIkSeq66SwRvwi4pWN/I/CccXWeCJDkX4EFwLur6itdiVCSpLnv2CSvBzYAb21/FFkEXNRRZ2NbBlM/V2fM6OgoC7bexZ7XrZ+tW2oALdh6O6Oj23odhiRJGqdbE0MvBJYBhwJHAJ9Istf4SklWJ9mQZMPmzZu7dGtJknrqVOAJwHLgVuCD3bqwz01JkiR103R6Ao0CB3Ts79+WddoIXFxVvwRuTPIDmqTQpZ2VqmotsBZgeHi4djboBwXnL5qaBf6iKWkyVXXb2HaSTwBfane39/yc6rk6du2uPzcXLVrEj+9dyD1PPnzqytJO2vO69SxatG+vw5DUZ5I8Cfh0R9HjgXcBe9FMQTL2i8g7q8ovgNJOmE5PoEuBZUkOTPIQmkku142r8080vYBIsg/N8LAbuhemJElzU5L9OnZfCYytHLYOWJVkjyQH0vw4cgnTe65KkjRwqur7VbW8qpYDzwa2Al9oD588dswEkLTzpuwJVFXbkhwLfJVmvp/Tq+rqJCcAG6pqXXvsxUmuAe4D/qyqbp/JwMf4i6Zmg79oSgJIcjbNjx77JNkIHA8cmmQ5UMBNwB8DtM/Kc4FrgG3AMVV1X3udX3muzu47kSRpzjsM+GFV3Zyk17FI88Z0hoPRZlrXjyt7V8d2AW9pX5IkzUtVdcQExadtp/5JwEkTlP/Kc1WSJD3IKuDsjv2JFmGQtIO6NTG0JEmSJEm7rB0u/XLgM23RtBZhcEEFaWomgSRJkiRJc8lLge+MLb5QVbdV1X1VdT/wCeDgiU6qqrVVNVxVw0NDQ7MYrtQ/TAJJkiRJkuaSI+gYCradRRgk7aBpzQkkSZIkSdJMS/Jw4EW0Cy20/nqiRRgk7TiTQJIkSZKkOaGqfgY8dlzZ63oUjjTvOBxMkiRJkiRpAJgEkiRJkiRJGgAmgSRJkiRJkgaASSBJkiRJkqQBYBJIkiRJkiRpAJgEkiRJkiRJGgAmgSRJkiRJkgaASSBJkiRJkqQBsLDXAUiSJEmCBVu3sOd163sdRt/Y7ec/AeD+hz6qx5H0jwVbtwD79joMST1kEkiSJEnqsaVLl/Y6hL4zMnI3AEsfb1Jj+vb1/zVpwJkEkiRJknrsuOOO63UIfWfNmjUAnHLKKT2ORJL6h3MCSZIkSZIkDQCTQJIkSZIkSQPAJJA0zyV5aJJLknw3ydVJ/rLXMUmSJEmSZp9zAknz373AC6rqp0l2B76V5MtVdVGvA5MkSZIkzR6TQNI8V1UF/LTd3b19Ve8ikiRJkiT1gkkgaQAkWQBcBiwFPlpVF8/0PUdHR1mw9S72vG79TN9KA2zB1tsZHd3W6zAkSZKkvuCcQNIAqKr7qmo5sD9wcJKndh5PsjrJhiQbNm/e3JMYJUmSJEkzy55A0gCpqjuTXACsAK7qKF8LrAUYHh7uylCxRYsW8eN7F3LPkw/vxuWkCe153XoWLdq312FIkiRJfWFaSaAkK4BTgAXAJ6vqvZPU+6/AZ4HfqKoNXYtS0k5LMgT8sk0A7Qm8CHhfj8OS1EMLtm5xqOYO2O3nPwHg/oc+qseR9I8FW7cAJmglSZprpkwCtXOJfJTmi+NG4NIk66rqmnH1HgmsAWZ8rhFJO2Q/4My2Le8GnFtVX+pxTJJ6ZOnSpb0Ooe+MjNwNwNLHm9SYvn39f02SpDloOj2BDgZGquoGgCTnACuBa8bVew9N74I/62qEknZJVV0JPLPXcUiaG4477rheh9B31qxZA8App5zS40gkSZJ2zXQmhl4E3NKxv7Ete0CSZwEHVNU/dzE2SZIkSdIASXJTku8luSLJhrZs7yTnJbm+/fuYXscp9atdXh0syW7Ah4C3TqOuKxBJkiRJkrbn+VW1vKqG2/23A+dX1TLg/HZf0k6YThJoFDigY3//tmzMI4GnAt9IchNwCLAuyTDjVNXaqhququGhoaGdj1qSpB5IcnqSTUmu6iib8NfJND6cZCTJlW2v2bFzjmzrX5/kyF68F0mS+shK4Mx2+0zgFb0LRepv00kCXQosS3JgkocAq4B1Ywer6q6q2qeqllTVEuAi4OWuDiZJmofOAFaMK5vs18mXAsva12rgVGiSRsDxwHNo5t073m7tkiQ9oICvJbksyeq2bN+qurXd/jEuPyjttCknhq6qbUmOBb5Ks0T86VV1dZITgA1VtW77V5h5LnW7Y1zqdse51K0kgKq6MMmSccUrgUPb7TOBbwBva8vPqqoCLkqyV5L92rrnVdUWgCTn0SSWzp7p+CVJ6gPPq6rRJL8GnJfkus6DVVVJaqIT26TRaoDFixfPfKRSH5rO6mBU1Xpg/biyd01S99BdD2v6XH50x7nU7c5wqVtJk5rs18nJFlaYcsEFSZIGVVWNtn83JfkCTa/Z25LsV1W3tj+obJrk3LXAWoDh4eEJE0XSoJtWEmguc6nbHedSt5I0M7b36+TO8BdNSdIgSfJwYLequrvdfjFwAs10JEcC723/frF3UUr9bZdXB5MkacDd1v4qybhfJydbWGGqBRce4IIK0vQkOSDJBUmuSXJ1kjVtuRO3S/1lX+BbSb4LXAL8c1V9hSb586Ik1wMvbPcl7QSTQJIk7ZqxXyfhwb9OrgNe337ZPAS4qx029lXgxUke034hfXFbJmnnbQPeWlUH0axUe0ySg3DidqmvVNUNVfWM9vWUqjqpLb+9qg6rqmVV9cKxefUk7bi+Hw4mSdJsSXI2zcTO+yTZSPNl8b3AuUmOBm4GXtNWXw8cDowAW4E3AFTVliTvoVl9E+AEP8xKu6ZNsN7abt+d5FqaubacuF3SLnMhoh3jQkQ7bjYXIjIJJEnSNFXVEZMcOmyCugUcM8l1TgdO72JoklrtCn7PBC7Gidsl7SIXh9lxLkS0M2ZvISKTQJIkSZoXkjwC+Bzw5qr6SZIHjnVz4nYnbZcGhwsR7TgXIprbnBNIkiRJfS/J7jQJoE9V1efb4hmZuN1J2yVJ/cokkCRJkvpami4/pwHXVtWHOg45cbskSR0cDiZJkqR+91zgdcD3klzRlr0TJ26XJOlBTAJJkiSpr1XVt4BMctiJ2yVJajkcTJIkSZIkaQCYBJIkSZIkSRoAJoEkSZIkSZIGgEkgSZIkSZKkAWASSJIkSZIkaQCYBJIkSZIkSRoAJoEkSZIkSZIGgEkgSZIkSZKkAWASSJIkSZIkaQCYBJIkSZIkSRoAJoGkeSzJAUkuSHJNkquTrOl1TJIkSZKk3ljY6wAkzahtwFur6jtJHglcluS8qrqm14FJkiRJkmaXPYGkeayqbq2q77TbdwPXAot6G5UkSZIkqRfsCSQNiCRLgGcCF8/WPRds3cKe162frdv1vd1+/hMA7n/oo3ocSf9YsHULsG+vw5AkSZL6gkkgaQAkeQTwOeDNVfWTCY6vBlYDLF68uCv3XLp0aVeuM0hGRu4GYOnjTWpM377+vyZJkiRN07SSQElWAKcAC4BPVtV7xx1/C/CHNPOPbAb+oKpu7nKsknZCkt1pEkCfqqrPT1SnqtYCawGGh4erG/c97rjjunGZgbJmTTNv9ymnnNLjSCRJkmZfkgOAs2i6+RawtqpOSfJu4I9ovmsCvLOq7G4u7YQpk0BJFgAfBV4EbAQuTbJu3MSylwPDVbU1yZuAvwZeOxMBS5q+JAFOA66tqg/1Oh5JkiRpOyZc1KQ9dnJVfaCHsUnzwnQmhj4YGKmqG6rqF8A5wMrOClV1QVVtbXcvAvbvbpiSdtJzgdcBL0hyRfs6vNdBSZIkSeO5qIk086YzHGwRcEvH/kbgOdupfzTw5V0JSlJ3VNW3gPQ6DkmSJGlHjFvU5LnAsUleD2yg6S10Rw/Dk/pWV5eIT/L7wDDw/kmOr06yIcmGzZs3T1RFkiRJkjTAJljU5FTgCcBy4Fbgg5Oc5/dNaQrTSQKNAgd07O/flj1IkhcCfw68vKrunehCVbW2qoaranhoaGhn4pUkSZIkzVMTLWpSVbdV1X1VdT/wCZopS36F3zelqU0nCXQpsCzJgUkeAqwC1nVWSPJM4H/RJIA2dT9MSZIkSdJ8NtmiJkn266j2SuCq2Y5Nmi+mnBOoqrYlORb4Ks0S8adX1dVJTgA2VNU6muFfjwA+07RbflRVL5/BuCVJkiRJ88vYoibfS3JFW/ZO4Igky2mWjb8J+ONeBCfNB9OZGJqqWg+sH1f2ro7tF3Y5LkmS+kqSm4C7gfuAbVU1nGRv4NPAEpoPra+pqjvaXzpPAQ4HtgJHja2GIknSoNrOoibrJyiTtBO6OjG0JEkD7vlVtbyqhtv9twPnV9Uy4Px2H+ClwLL2tZpmwktJkiRpRpkEkiRp5qwEzmy3zwRe0VF+VjUuAvYaN9+BJEmS1HUmgSRJ6o4CvpbksiSr27J9q+rWdvvHwL7t9iLglo5zN7ZlkiRJ0oyZ1pxAkiRpSs+rqtEkvwacl+S6zoNVVUlqRy7YJpNWAyxevLh7kUqSJGkg2RNIkqQuqKrR9u8m4AvAwcBtY8O82r+b2uqjwAEdp+/flo2/5tqqGq6q4aGhoZkMX5IkSQPAJJAkSbsoycOTPHJsG3gxcBWwDjiyrXYk8MV2ex3w+jQOAe7qGDYmSZIkzQiHg0mStOv2Bb7QrPzOQuAfq+orSS4Fzk1yNHAz8Jq2/nqa5eFHaJaIf8PshyxJkqRBYxJIkqRdVFU3AM+YoPx24LAJygs4ZhZCkyRJkh7gcDBJkiRJkqQBYBJIkiRJkiRpAJgEkiRJkiRJGgAmgSRJkiRJkgaASSBJkiRJkqQBYBJIkiRJkiRpAJgEkiRJkiRJGgAmgSRJkiRJkgaASSBJkiRJkqQBYBJIkiRJkiRpAJgEkiRJkiRJGgAmgSRJkiRJkgaASSBJkiRJkqQBYBJIkiRJkiRpAJgEkua5JKcn2ZTkql7HIkmSJO2MJCuSfD/JSJK39zoeqV+ZBJLmvzOAFb0OQpIkSdoZSRYAHwVeChwEHJHkoN5GJfWnaSWBpsq6Jtkjyafb4xcnWdL1SCXtlKq6ENjS6zgkSZKknXQwMFJVN1TVL4BzgJU9jknqSwunqtCRdX0RsBG4NMm6qrqmo9rRwB1VtTTJKuB9wGtnIuB+8pGPfISRkZFeh/ErxmJas2ZNjyOZ2NKlSznuuON6HYbmMdvmzrFtaqbZNneObVMzzba5c2ybXbUIuKVjfyPwnB7FMmfYNnec7XJ6PYGmk3VdCZzZbn8WOCxJuhemumnPPfdkzz337HUYmkOSrE6yIcmGzZs39zqcgWXblOYm2+Zgcd6R/mHb1Hh+pp0bbJtz25Q9gZhe1vWBOlW1LcldwGOB/+hGkP1q0DOM6h9VtRZYCzA8PFw9DmfG2Talucm2qV6bZg/4gWPb1BwwChzQsb9/W/YgfqaVpjarE0ObmZUkSdIc5rwj0tx0KbAsyYFJHgKsAtb1OCapL00nCTSdrOsDdZIsBB4N3D7+QlW1tqqGq2p4aGho5yKWtEOSnA18G3hSko1Jju51TJIkzVET9YBf1KNYJLWqahtwLPBV4Frg3Kq6urdRSf1pOsPBHsi60iR7VgH/bVyddcCRNF80XwV8varmffc7qR9U1RG9jkGSpPkkyWpgNcDixYt7HI00GKpqPbC+13FI/W7KnkCTZV2TnJDk5W2104DHJhkB3gI4iZ4kSZL6zbTnHbF3uySpH02nJ9CEWdeqelfH9s+BV3c3NEmSJGlWTacHvCRJfWtaSSBJkiRpvmtXuR3rAb8AON15RyRJ84lJIEmSJKnlvCOSpPlsVpeIlyRJkiRJUm+YBJIkSZIkSRoAJoEkSZIkSZIGQKqqNzdONgM39+TmAtgH+I9eBzHgHldVc25dWdtmz9k2e8+2qYnYNntrTrZLsG3OAbbN3rJtajK2zd6atG32LAmk3kqyoaqGex2HpAezbUpzk21Tmptsm9LcZNucuxwOJkmSJEmSNABMAkmSJEmSJA0Ak0CDa22vA5A0IdumNDfZNqW5ybYpzU22zTnKOYEkSZIkSZIGgD2BJEmSJEmSBoBJoAGUZEWS7ycZSfL2XscjqWHblOYm26Y0N9k2pbnHdjn3ORxswCRZAPwAeBGwEbgUOKKqrulpYNKAs21Kc5NtU5qbbJvS3GO77A/2BBo8BwMjVXVDVf0COAdY2eOYJNk2pbnKtinNTbZNae6xXfYBk0CDZxFwS8f+xrZMUm/ZNqW5ybYpzU22TWnusV32AZNAkiRJkiRJA8Ak0OAZBQ7o2N+/LZPUW7ZNaW6ybUpzk21Tmntsl33AJNDguRRYluTAJA8BVgHrehyTJNumNFfZNqW5ybYpzT22yz6wsNcBaHZV1bYkxwJfBRYAp1fV1T0OSxp4tk1pbrJtSnOTbVOae2yX/cEl4iVJkiRJkgaAw8EkSZIkSZIGgEkgSZIkSZKkAWASSJIkSZIkaQCYBJIkSZIkSRoAJoEkSZIkSZIGgEkgSZolSe5LckWS7yb5TpLfasuXJLlqXN13J/nTdvuMJK9qt/dOcnmSN8z+O5AkSdIg6PjcelWS/51kr528zq8n+WyXw9MuMAk0h3U0vLHXkiSHJvnSFOctT3L4FHWOSrK5ve41Sf5oknovT/L2XXkfkh5wT1Utr6pnAO8A/ueOnJzk0cBXgbVV9XczEaDUb5I8tuM5+eMkox37DxlX981JHjaNa34jyfB2jv9Bku8lubL9cLxyB2P+kyTXJvlUklckOWhHzpd6adwXw89M1qaS/Ntsxzabkrw/ydXt36OS/HqvY5K6bOxz61OBLcAxO3ORqvr3qnpVd0PTrjAJNLeNNbyx103TPG85sN0kUOvTVbUcOBT4qyT7dh5MsrCq1lXVe3cgZknT8yjgjh2o/wjgy8A/VtWpMxOS1H+q6vax5yTwceDkjufmL8ZVfzMwZRJoe5LsD/w58LyqejpwCHDlDl7m/wNeVFW/B7wCMAmkftL5xfAXwBs7DyZZCFBVv9WL4MYbi2cGrAaeXlV/BhwFmATSfPZtYBFAkick+UqSy5J8M8mTO8ovan8kOTHJT9vyB3q8J3lokr9r61ye5Plt+VFJPt9e9/okf92j9zkQTAL1sSQHJ/l224D+LcmT2l89TwBe2/5K89qprlNVm4AfAo9rh518PMnFwF+3DfJv2/vtm+QL7VCW73YMZfn9JJe09/tfSRbM4NuW+tmebTu5Dvgk8J6OY0/o7PnHuA/VwIeAb1XVybMUq9S3khzWPhu/l+T0JHsk+ROaL2kXJLmgrXdqkg3tr/l/Oc3L/xpwN/BTgKr6aVXd2F7v2R3PyPdn3DDPts7HgccDX07y58DLgfe3bf8Ju/zmpdn1TWBp21P9m0nWAdcAjH0BbLff1rbH7yZ5b1s24RfJiXR8Pt2Q5AdJfrctX9C2tUvbnnl/3Jb/SjwTXPPhSf65jemqsc/MSVYkuS7NsO0PZ4Ie+O11HwFc1p43DHyqbcd77tR/SWmOar/bHQasa4vWAsdV1bOBPwU+1pafApxSVU8DNk5yuWOAauscAZyZ5KHtseXAa4Gn0XyXPaDb70WNmcqMqzv2bL8MAtxYVa8cd/w64LeraluSFwJ/VVX/Ncm7gOGqOnY6N0nyeJoPpCNt0f7Ab1XVfUmO6qj6YeBfquqV7T8Gj0jyX2ga63Or6pdJPgb8HnDWjr9dad67p+2tQJLfBM5K8tT22A/HjrXH3z3u3K8DK5N8oE3cSprYQ4EzgMOq6gdJzgLeVFV/k+QtwPOr6j/aun9eVVvaZ9r5SZ5eVVP16vkucBtwY5Lzgc9X1f9uj/0dcGxVXZjk/ROdXFVvTLJiLI4ky4AvVZXzJaivpOlh81LgK23Rs4CnjiVFO+q9FFgJPKeqtibZuz20FnhjVV2f5Dk0XyRfsJ1bLgEOBp5Ak8xdCrweuKuqfiPJHsC/Jvna9uLpsAL496p6WRvno9svo59o4xgBPj3RiVX18iQ/7Ximvwn406rasJ34pX4z9l10EXAtcF6SRwC/BXwmyVi9Pdq/v0nTuxXgH4EPTHDN5wEfAaiq65LcDDyxPXZ+Vd0FkOQa4HHALV18P2qZBJrb7un8UjiBR9NkT5cBBey+g9d/bZLnAfcCf9x+EAb4TFXdN0H9F9A8bGmP35XkdcCzgUvbc/cE/IIqTaGqvp1kH2BomqecA/wrsD7J86vq7pmLTuprC2h+OPlBu38mzS+PfzNB3dckWU3zeWg/mmFZ200CtT+QrAB+g+aX0ZOTPLu9/l5VdWFb9e9pviBL803nj5TfBE6j+VJ4ySQJlxcCf1dVWwHaz5vb+yI5mXOr6n7g+iQ3AE8GXgw8Pe3iCTSfjZfRDFObLJ4x3wM+mOR9NInYbyZZTvPvx/UASf6BZtiXNIjuqarlaeb9+irNs/QM4M4pvqPurHs7tu/DXMWM8T9sf3sPcEHbM2cJ8I0dPP/Tk/QW+tkOXCPAmVX1jh28tzTQ2m7vC4DbmeYcJVV1cpL/C/h8kpdNMN+JpGlKciBNN/bfqKo7kpxB04toSlVVwCXAJUnOo+kB9DczFKo01/zKj5RtImdHPj/uxo5/kawJ9kMzLOWr4+I5dKp42p6Cz6KZR/PEtmffuu2dIw2itgffnwD/RNNj78Ykr66qz6Rp/E+vqu8CFwH/laYH3apJLvdNmlEjX0/yRGAx8H2annuaJc4J1N8eDYy220d1lN8NPHIG7nc+8CZ4YAz2o9uyVyX5tbZ87ySPm4F7S/PB2JxAV9A8II+cpNfdpKrqbTTjrP8+if+GS7/qPmBJO1QE4HXAv7Tbnc/HR9F8SbwrzcII0+q1k2ap284Pq8uBm6vqTuDOtoctNB9yp2OmntnSXHEe8Ia2NwFJ9q6qn9B+kWzLkuQZU1zn1Ul2SzN31uNpvjh+FXhTkt3b6zwxycOnE1Sa1by2VtU/AO+n+RJ6Hc2/H2Pzcx0xzfdoO9a8VlWX0/SUPYLm+XZ0ku8CV9MM94Rm8YW3JLkSWArcNcGlPgbsluR7NJ+Fj6qqeyeopxlkT6D+9tc0w8H+AvjnjvILgLe3XzT/Z1VNOJ55J6wB1iY5muZD9pvaIS1/AXyt/UL6S5qugjd36Z7SvFFVE06a3q7899RxZe/u2D5q3LE3dD86ad74OfAGmmEmC4FLaVYNg2YOkq8k+feqen6Sy2m+9N1CM9xyOnYHPtB+gfw5sJn/nMj9DcDpSQoYm5dk7MvmJ6tqopU7zwE+0f7K+qqq+uEOvFdpzquqr7TDrDYk+QWwHngnzRfJU9vPkbvTtIXvbudSP6LpgfcomrmEfp7kkzRzBX2n7ZGwmf+ck2QqT6OZlP1+ms+vb2qvuRr45yRbaXotPBIgyXB73z+c4FpnAB9Pcg/wm1V1zzRjkOasqnrEuP3/p2N3xQSnjAKHVFUlWQU8qT3vJtrPuVU19owef68zaNrR2P7v7lr02p40PZolSZLULe0w7S+1y2hL2gXtcM1Zn0C9HVb2p34hlaaW5LeBv6UZpnkn8AdVNbLdk9QT9gSSJEmSJEk7raq+CUw1rFNzgD2B5rkkb6AZxtXpX6vqmF7EI0lSv0hyMb+6YtHrqup7vYhHmu+S/Dnw6nHFn6mqk3bhmo+lmcNyvMOq6vadva4k9SuTQJIkSZIkSQPAlWUkSZIkSZIGgEkgSZIkSZKkAWASSJIkSZIkaQCYBJIkSZIkSRoAJoEkSZIkSZIGwP8PwC/hZA6Dgw4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df5.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sSynmi9PbiFr",
        "outputId": "aaf2dc14-3b0b-4799-fe91-06da52922b4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Flat_Price  BHK  Total_Sq.ft  Price_per_sq.ft  Region\n",
              "1   4500000.0  3.0       1400.0           3210.0      29\n",
              "2  13500000.0  3.0       2500.0           5400.0     173\n",
              "3   6000000.0  5.0       1100.0           5450.0     175\n",
              "4   5200000.0  4.0        900.0           5780.0     121\n",
              "5   3200000.0  3.0       1200.0           2670.0     192"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f82a30c6-2e42-46f2-90a3-ef5b199ace96\">\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>Flat_Price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4500000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13500000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6000000.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5200000.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3200000.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2670.0</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f82a30c6-2e42-46f2-90a3-ef5b199ace96')\"\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-f82a30c6-2e42-46f2-90a3-ef5b199ace96 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-f82a30c6-2e42-46f2-90a3-ef5b199ace96');\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": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling"
      ],
      "metadata": {
        "id": "6yVLMEQ_PKrb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()"
      ],
      "metadata": {
        "id": "gkoYBQjnPMwj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df5.drop('Flat_Price', axis=1)\n",
        "y = df5['Flat_Price']\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nGsG0K1hPM0T",
        "outputId": "ff84d3d9-9455-448c-bf43-d9b25e16e797"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK  Total_Sq.ft  Price_per_sq.ft  Region\n",
              "1  3.0       1400.0           3210.0      29\n",
              "2  3.0       2500.0           5400.0     173\n",
              "3  5.0       1100.0           5450.0     175\n",
              "4  4.0        900.0           5780.0     121\n",
              "5  3.0       1200.0           2670.0     192"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1a101da-cc51-49ca-9b89-fcbe62298d25\">\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>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2670.0</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1a101da-cc51-49ca-9b89-fcbe62298d25')\"\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-e1a101da-cc51-49ca-9b89-fcbe62298d25 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-e1a101da-cc51-49ca-9b89-fcbe62298d25');\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": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.to_csv('features.csv', index=False)"
      ],
      "metadata": {
        "id": "AZ5He33Ci5G4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "newdf = pd.read_csv('features.csv', index_col=False)\n",
        "newdf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "laXVp6GHkcP0",
        "outputId": "cd1bd10b-d6f5-4364-84c3-6991ebb2052b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   BHK  Total_Sq.ft  Price_per_sq.ft  Region\n",
              "0  3.0       1400.0           3210.0      29\n",
              "1  3.0       2500.0           5400.0     173\n",
              "2  5.0       1100.0           5450.0     175\n",
              "3  4.0        900.0           5780.0     121\n",
              "4  3.0       1200.0           2670.0     192"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-01b4a6df-5df3-46e7-a21e-e5a2972105d8\">\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>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1400.0</td>\n",
              "      <td>3210.0</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.0</td>\n",
              "      <td>2500.0</td>\n",
              "      <td>5400.0</td>\n",
              "      <td>173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>5450.0</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>900.0</td>\n",
              "      <td>5780.0</td>\n",
              "      <td>121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.0</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2670.0</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-01b4a6df-5df3-46e7-a21e-e5a2972105d8')\"\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-01b4a6df-5df3-46e7-a21e-e5a2972105d8 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-01b4a6df-5df3-46e7-a21e-e5a2972105d8');\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": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nPiDe0DhkcXt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 4, figsize=(25, 4))\n",
        "for col, i in zip(X.select_dtypes(np.number).columns, ax.flatten()):\n",
        "  i.set_xlabel(col)\n",
        "  sns.distplot(X[col], ax=i, kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 982
        },
        "id": "1mD9_ygLeTDi",
        "outputId": "ce65d4b7-7613-4455-f465-4fd83389e3be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-37-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-37-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-37-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-37-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jNlo4HsyeTMb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = pd.DataFrame(data=scaler.fit_transform(X), columns=['BHK', 'Total_Sq.ft', 'Price_per_sq.ft', 'Region'])\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ONaoWSEIPM25",
        "outputId": "ad37ff24-4da3-4e8d-b09c-78bddf5f3c7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        BHK  Total_Sq.ft  Price_per_sq.ft    Region\n",
              "0 -0.418477    -0.357814        -0.466830 -1.249291\n",
              "1 -0.418477     1.125670         0.884088  1.243705\n",
              "2  0.846662    -0.762401         0.914931  1.278330\n",
              "3  0.214092    -1.032126         1.118494  0.343456\n",
              "4 -0.418477    -0.627539        -0.799934  1.572642"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1217885-2982-4167-b47e-dfe450e3f094\">\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>BHK</th>\n",
              "      <th>Total_Sq.ft</th>\n",
              "      <th>Price_per_sq.ft</th>\n",
              "      <th>Region</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.418477</td>\n",
              "      <td>-0.357814</td>\n",
              "      <td>-0.466830</td>\n",
              "      <td>-1.249291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.418477</td>\n",
              "      <td>1.125670</td>\n",
              "      <td>0.884088</td>\n",
              "      <td>1.243705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.846662</td>\n",
              "      <td>-0.762401</td>\n",
              "      <td>0.914931</td>\n",
              "      <td>1.278330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.214092</td>\n",
              "      <td>-1.032126</td>\n",
              "      <td>1.118494</td>\n",
              "      <td>0.343456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.418477</td>\n",
              "      <td>-0.627539</td>\n",
              "      <td>-0.799934</td>\n",
              "      <td>1.572642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e1217885-2982-4167-b47e-dfe450e3f094')\"\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-e1217885-2982-4167-b47e-dfe450e3f094 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-e1217885-2982-4167-b47e-dfe450e3f094');\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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ELphxgH2iNfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1, 4, figsize=(25, 4))\n",
        "for col, i in zip(X.select_dtypes(np.number).columns, ax.flatten()):\n",
        "  i.set_xlabel(col)\n",
        "  sns.distplot(X[col], ax=i, kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 983
        },
        "id": "ln5b_GBubJnR",
        "outputId": "928e03fd-c4a3-49ba-af46-963809a1ffe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-39-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-39-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-39-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n",
            "<ipython-input-39-346b42b42da6>:4: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(X[col], ax=i, kde=True)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x288 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jZUnL4hvbJwb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Baseline model"
      ],
      "metadata": {
        "id": "WfbNXi9ITAhf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, random_state=10)"
      ],
      "metadata": {
        "id": "QL9MEcz0TDEB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LinearRegression()"
      ],
      "metadata": {
        "id": "KQXJbCRSTDLg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "7C7wMAkFPM6B",
        "outputId": "f680dfda-759e-4752-e8ec-62a8a38fcde8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prediction = np.exp(lr.predict(X_test))\n",
        "prediction = lr.predict(X_test)"
      ],
      "metadata": {
        "id": "i3olGTHW3QpY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# r2 = r2_score(np.exp(y_test), prediction)\n",
        "# mae = mean_absolute_error(np.exp(y_test), prediction)\n",
        "# mape = mean_absolute_percentage_error(np.exp(y_test), prediction)\n",
        "\n",
        "r2 = r2_score(y_test, prediction)\n",
        "mae = mean_absolute_error(y_test, prediction)\n",
        "mape = mean_absolute_percentage_error(y_test, prediction)"
      ],
      "metadata": {
        "id": "fhqkmkFtZwCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"R2 : \", r2)\n",
        "print(\"MAE : \", mae)\n",
        "print(\"MAPE : \", mape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLDPjHUuZwK6",
        "outputId": "99d08dc9-b1d0-4855-a38d-b41e0bf84554"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 :  0.9156565591336163\n",
            "MAE :  711791.9114491852\n",
            "MAPE :  0.16630849753300275\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_key(val):\n",
        "    for key, value in region_mappings_le.items():\n",
        "        if val == value:\n",
        "            return key\n",
        " \n",
        "    return \"key doesn't exist\""
      ],
      "metadata": {
        "id": "fcU15C-enfyz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_price(BHK, Total_Sq_ft, Price_per_sq_ft, Region):\n",
        "  val = get_key(Region)\n",
        "  x = np.zeros(len(X.columns))\n",
        "  x[0] = BHK\n",
        "  x[1] = Total_Sq_ft\n",
        "  x[2] = Price_per_sq_ft\n",
        "  x[3] = val\n",
        "  # x = np.log1p(x)\n",
        "  x_scaled = scaler.transform([x])\n",
        "  # return x.shape\n",
        "  return (lr.predict(x_scaled)[0])"
      ],
      "metadata": {
        "id": "fSuc-hrotId4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price(4.0, 1581.0, 7000.0, 'Joka')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lc78wep4t0Ji",
        "outputId": "8cbe7d1d-bacc-46e8-e310-41bf1af56d97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10566874.952708032"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price(4.0, 2500.0, 4400.0, 'Kalyani')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jrmq8-6Hqip-",
        "outputId": "29001791-2d69-44a7-e045-6ee89c59186b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9826691.861478608"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model compare"
      ],
      "metadata": {
        "id": "5jyVMIrPmK9M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "models = [LinearRegression(),\n",
        "          Ridge(alpha=1.0), \n",
        "          Lasso(),\n",
        "          DecisionTreeRegressor(), \n",
        "          KNeighborsRegressor(),\n",
        "          SVR(),\n",
        "          XGBRegressor(n_estimators=1000, learning_rate=0.01)\n",
        "         ]\n",
        "model_names = ['Linear Regression', \n",
        "               'Ridge',\n",
        "               'Lasso',\n",
        "    'Decision Tree', \n",
        "    'KNeighbors Regression',\n",
        "    'SVR',\n",
        "    'XGB'\n",
        "  ]"
      ],
      "metadata": {
        "id": "vojkSp92mNsd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Zedt_pPxmN0r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aP_lNv3zmN9p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "performances = {}\n",
        "kf = KFold(n_splits=10)\n",
        "for model, name in zip(models, model_names):\n",
        "  model.fit(X_train, y_train)\n",
        "  results = cross_val_score(model, X_train, y_train, scoring='neg_mean_absolute_percentage_error', cv=kf)\n",
        "  prediction = (model.predict(X_test))\n",
        "  r2 = r2_score((y_test), prediction)\n",
        "  mape = mean_absolute_percentage_error((y_test), prediction)\n",
        "  performances[name] = [(np.mean(-results)), r2, mape]"
      ],
      "metadata": {
        "id": "w2b5phNVZwWd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "plt.bar(performances.keys(), [p[1] for p in performances.values()])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('R2 Score')\n",
        "plt.title('Model performance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "P3z4z4SYnH5E",
        "outputId": "985575d2-ac5b-4593-e4c0-d73044945fe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3gAAAGDCAYAAAB5pLK9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAohElEQVR4nO3deZgtdX0n/vdHQNGAqOHqY2S5anDBDfVGzU/HZVwGNAEzGpXEuIwJyfyC0bhMMDGKZuIycRsNaoghqElcEwwqCa5EY1zAgCigBhEE3FDRuCGLn/mjquXYdDfdcM/t23Vfr+fpp+vUdj516pyq865vVZ3q7gAAALDxXWe9CwAAAGDrEPAAAAAmQsADAACYCAEPAABgIgQ8AACAiRDwAAAAJkLAA2C7VlWbq6qraudVjPuEqvrXbVHX+Hy/UlXnV9X3ququ2+p5AWA5Ah4AW01VnVtVl1bVnov6nzqGtM3rVNq8vCTJ4d29W3efut7FAICAB8DW9sUkhy48qKo7JbnB+pWz9c20Ju6b5IxrOI+dtl5FADAQ8ADY2t6Y5HEzjx+f5A2zI1TVHlX1hqq6qKrOq6pnV9V1xmE7VdVLquobVXVOkoctMe1fVdVXqurCqvrfqwlLM6d6HlZVXx6nf8bM8OtU1RFV9YWq+mZVvbWqbrJo2idV1ZeSfLiqvpdkpySfqqovjOPdvqpOqqpvV9UZVXXwzPyPrarXVNUJVfX9JA8YWzyfWVWnV9X3x+W6WVX9U1V9t6reV1U3npnH26rqq1X1nar6UFXdYdH8j6qqd4/Tfryqbj0z/A5V9d6q+lZVfa2q/vDqlhuAjUfAA2Br+1iSG45hZ6ckj0nyN4vGeVWSPZLcKsn9MgTCJ47DfivJLyW5a5ItSR65aNpjk1ye5OfHcR6S5DfXUN8Dkuw3TvcHVfWgsf+Tkzx8rOfnklyc5KhF094vye2T/Nfu3m3sd5fuvnVV7ZLknUnek+Sm4/z+tqpuOzP9ryX50yS7J1m4VvARSR6c5DZJfjnJPyX5wySbMuynf29m+n8aa79pkn9P8reL6ntMkucluXGSs8fnSlXtnuR9Sf55XLafT/L+NSw3ABuEgAfAPCy04j04yVlJLlwYMBP6ntXd3+3uc5O8NMlvjKM8Kskruvv87v5WkhfOTHuzJA9N8tTu/n53fz3Jy8f5rdbzxmk/neSvc+XppL+T5I+6+4Lu/lGSI5M8ctHNXY4cp/3hEvO9V5Ldkryouy/t7g8kedfM/JPkH7v7I9394+6+ZOz3qu7+WndfmOTDST7e3aeOw4/LEGKTJN19zPiaLdR3l6raY2b+x3X3J7r78gzh74Cx/y8l+Wp3v7S7Lxnn8fE1LDcAG4SNNwDz8MYkH0pyyyw6PTPJnkl2SXLeTL/zktxi7P65JOcvGrZg33Har1TVQr/rLBr/6iye951m5n1cVf14ZvgVSW62zLSL/VyS87t7dvrZ5Vpu+q/NdP9wice7JT8Jxn+a5FcztO4tPM+eSb4zdn91ZtofLEybZO8kX1im7pWW+8KlJwFge6UFD4CtrrvPy3CzlYcm+YdFg7+R5LIMwWLBPrkyTHwlQyCZHbbg/CQ/SrJnd99o/Lthd98hq7d43l+emfdBM/O9UXfvOras/WTRVpjvl5PsvXAt4RLLdXXTX51fS3JIkgdlOL1189i/lptgxvkZToddbtjVLTcAG4SAB8C8PCnDtWrfn+3Z3VckeWuSP62q3atq3yRPy5XX6b01ye9V1V7jDUaOmJn2KxmucXtpVd1wvEHIravqfmuo64+r6gbjDUqemOQtY//XjjXtmyRVtamqDlnDfD+eodXsf1XVLlV1/wzX1L15DfNYye4Zwu03M9yV9AVrmPZdSW5eVU+tquuNr/s9x2HXdrkB2I4IeADMRXd/obtPWWbwk5N8P8k5GW428ndJjhmH/WWSE5N8KsONRBa3AD4uyXWTnJnhhiBvT3LzNZT2LxluQPL+JC/p7veM/f9vkuOTvKeqvpvhZjH3XHoWV9Xdl2YIdAdlaKV8dZLHdfdn11DbSt6Q4ZTPCzMs+8fWUNt3M1wP+csZTuP8jww3m0mu5XIDsH2p7mtztggAbAzjj6x/Mcku401IAGBytOABAABMhIAHAAAwEU7RBAAAmAgteAAAABMh4AEAAEzEzutdwFrtueeevXnz5vUuAwAAYF188pOf/EZ3b1pq2IYLeJs3b84ppyz3s0oAAADTVlXnLTfMKZoAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMxt4BXVcdU1der6jPLDK+qemVVnV1Vp1fV3eZVCwAAwI5gni14xyY5cIXhByXZb/w7LMlr5lgLAADA5M0t4HX3h5J8a4VRDknyhh58LMmNqurm86oHAABg6nZex+e+RZLzZx5fMPb7yuIRq+qwDK182WeffbZJcQAAsD3bfMS717uEyTv3RQ9b7xLWbD0D3qp199FJjk6SLVu29DqXA8BW5AvK/G3ELygAXDPreRfNC5PsPfN4r7EfAAAA18B6BrzjkzxuvJvmvZJ8p7uvcnomAAAAqzO3UzSr6k1J7p9kz6q6IMlzk+ySJN392iQnJHlokrOT/CDJE+dVCwAAwI5gbgGvuw+9muGd5Hfn9fwAAAA7mvU8RRMAAICtSMADAACYCAEPAABgIgQ8AACAidgQP3QOAGyf/FD9/PmhemAttOABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwETuvdwFTsfmId693CTuEc1/0sLnM1/rbNqy/jWte6w4A2Lq04AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEXMNeFV1YFV9rqrOrqojlhi+T1V9sKpOrarTq+qh86wHAABgyuYW8KpqpyRHJTkoyf5JDq2q/ReN9uwkb+3uuyZ5TJJXz6seAACAqZtnC949kpzd3ed096VJ3pzkkEXjdJIbjt17JPnyHOsBAACYtHkGvFskOX/m8QVjv1lHJnlsVV2Q5IQkT15qRlV1WFWdUlWnXHTRRfOoFQAAYMNb75usHJrk2O7eK8lDk7yxqq5SU3cf3d1bunvLpk2btnmRAAAAG8E8A96FSfaeebzX2G/Wk5K8NUm6+6NJdk2y5xxrAgAAmKx5BryTk+xXVbesqutmuInK8YvG+VKSByZJVd0+Q8BzDiYAAMA1MLeA192XJzk8yYlJzspwt8wzqur5VXXwONrTk/xWVX0qyZuSPKG7e141AQAATNnO85x5d5+Q4eYps/2eM9N9ZpJ7z7MGAACAHcV632QFAACArUTAAwAAmAgBDwAAYCIEPAAAgIkQ8AAAACZCwAMAAJgIAQ8AAGAiBDwAAICJEPAAAAAmQsADAACYCAEPAABgIgQ8AACAiRDwAAAAJkLAAwAAmAgBDwAAYCIEPAAAgIkQ8AAAACZCwAMAAJgIAQ8AAGAiBDwAAICJEPAAAAAmQsADAACYCAEPAABgIgQ8AACAiRDwAAAAJkLAAwAAmAgBDwAAYCIEPAAAgIkQ8AAAACZCwAMAAJgIAQ8AAGAiBDwAAICJEPAAAAAmQsADAACYCAEPAABgIgQ8AACAiRDwAAAAJkLAAwAAmAgBDwAAYCIEPAAAgIkQ8AAAACZCwAMAAJgIAQ8AAGAiBDwAAICJEPAAAAAmQsADAACYCAEPAABgIgQ8AACAiRDwAAAAJmKuAa+qDqyqz1XV2VV1xDLjPKqqzqyqM6rq7+ZZDwAAwJTtPK8ZV9VOSY5K8uAkFyQ5uaqO7+4zZ8bZL8mzkty7uy+uqpvOqx4AAICpm2cL3j2SnN3d53T3pUnenOSQReP8VpKjuvviJOnur8+xHgAAgEmbZ8C7RZLzZx5fMPabdZskt6mqj1TVx6rqwDnWAwAAMGlzO0VzDc+/X5L7J9kryYeq6k7d/e3ZkarqsCSHJck+++yzjUsEAADYGObZgndhkr1nHu819pt1QZLju/uy7v5iks9nCHw/pbuP7u4t3b1l06ZNcysYAABgI5tnwDs5yX5Vdcuqum6SxyQ5ftE478jQepeq2jPDKZvnzLEmAACAyZpbwOvuy5McnuTEJGcleWt3n1FVz6+qg8fRTkzyzao6M8kHkzyzu785r5oAAACmbK7X4HX3CUlOWNTvOTPdneRp4x8AAADXwlx/6BwAAIBtR8ADAACYCAEPAABgIgQ8AACAiRDwAAAAJuJqA14NHltVzxkf71NV95h/aQAAAKzFalrwXp3kF5McOj7+bpKj5lYRAAAA18hqfgfvnt19t6o6NUm6++Kquu6c6wIAAGCNVtOCd1lV7ZSkk6SqNiX58VyrAgAAYM1WE/BemeS4JDetqj9N8q9JXjDXqgAAAFizFU/RrKrrJPlikv+V5IFJKsnDu/usbVAbAAAAa7BiwOvuH1fVUd191ySf3UY1AQAAcA2s5hTN91fVI6qq5l4NAAAA19hqAt5vJ3lbkkur6rvj33/OuS4AAADW6Gp/JqG7d98WhQAAAHDtrOZ38FJVBye57/jwpO5+1/xKAgAA4Jq42lM0q+pFSZ6S5Mzx7ylV9cJ5FwYAAMDarKYF76FJDujuHydJVb0+yalJnjXPwgAAAFib1dxkJUluNNO9xxzqAAAA4FpaTQveC5OcWlUfzPBD5/dNcsRcqwIAAGDNVnMXzTdV1UlJfmHs9Qfd/dW5VgUAAMCareYmK7+S5AfdfXx3H5/kkqp6+NwrAwAAYE1Wcw3ec7v7OwsPuvvbSZ47t4oAAAC4RlYT8JYaZ1W/nwcAAMC2s5qAd0pVvayqbj3+vTzJJ+ddGAAAAGuzmoD35CSXJnnL+HdJkt+dZ1EAAACs3Wruovn9jD+LUFU3TvLt7u55FwYAAMDaLNuCV1XPqarbjd3Xq6oPJDk7ydeq6kHbqkAAAABWZ6VTNB+d5HNj9+PHcW+a5H5JXjDnugAAAFijlQLepTOnYv63JG/q7iu6+6y4iyYAAMB2Z6WA96OqumNVbUrygCTvmRl2g/mWBQAAwFqt1BL3lCRvT7Ipycu7+4tJUlUPTXLqNqgNAACANVg24HX3x5Pcbon+JyQ5YZ5FAQAAsHar+R08AAAANgABDwAAYCIEPAAAgIlYMeBV1Q2r6tZL9L/z/EoCAADgmlg24FXVo5J8NsnfV9UZVfULM4OPnXdhAAAArM1KLXh/mOTu3X1AkicmeWNV/co4rOZdGAAAAGuz0u/g7dTdX0mS7v5EVT0gybuqau8kvU2qAwAAYNVWasH77uz1d2PYu3+SQ5LcYc51AQAAsEYrteD9zywKgN393ao6MMmj5loVAAAAa7ZswOvuTy0z6Io51QIAAMC1sNJdNG9YVc+qqj+vqofU4MlJzokWPAAAgO3OSqdovjHJxUk+muQ3M9xVs5I8vLtPm39pAAAArMVKAe9W3X2nJKmq1yX5SpJ9uvuSbVIZAAAAa7LSXTQvW+jo7iuSXCDcAQAAbL9WasG7S1X959hdSa4/Pq4k3d03nHt1AAAArNpKd9HcaVsWAgAAwLWz0imaAAAAbCBzDXhVdWBVfa6qzq6qI1YY7xFV1VW1ZZ71AAAATNncAl5V7ZTkqCQHJdk/yaFVtf8S4+2e5ClJPj6vWgAAAHYE82zBu0eSs7v7nO6+NMmbkxyyxHh/kuTFSdyhEwAA4FqYZ8C7RZLzZx5fMPb7iaq6W5K9u/vdc6wDAABgh7BuN1mpquskeVmSp69i3MOq6pSqOuWiiy6af3EAAAAb0DwD3oVJ9p55vNfYb8HuSe6Y5KSqOjfJvZIcv9SNVrr76O7e0t1bNm3aNMeSAQAANq55BryTk+xXVbesqusmeUyS4xcGdvd3unvP7t7c3ZuTfCzJwd19yhxrAgAAmKy5BbzuvjzJ4UlOTHJWkrd29xlV9fyqOnhezwsAALCj2nmeM+/uE5KcsKjfc5YZ9/7zrAUAAGDq1u0mKwAAAGxdAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBEzDXgVdWBVfW5qjq7qo5YYvjTqurMqjq9qt5fVfvOsx4AAIApm1vAq6qdkhyV5KAk+yc5tKr2XzTaqUm2dPedk7w9yf+ZVz0AAABTN88WvHskObu7z+nuS5O8OckhsyN09we7+wfjw48l2WuO9QAAAEzaPAPeLZKcP/P4grHfcp6U5J/mWA8AAMCk7bzeBSRJVT02yZYk91tm+GFJDkuSffbZZxtWBgAAsHHMswXvwiR7zzzea+z3U6rqQUn+KMnB3f2jpWbU3Ud395bu3rJp06a5FAsAALDRzTPgnZxkv6q6ZVVdN8ljkhw/O0JV3TXJX2QId1+fYy0AAACTN7eA192XJzk8yYlJzkry1u4+o6qeX1UHj6P9WZLdkrytqk6rquOXmR0AAABXY67X4HX3CUlOWNTvOTPdD5rn8wMAAOxI5vpD5wAAAGw7Ah4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBECHgAAwEQIeAAAABMh4AEAAEyEgAcAADARAh4AAMBEzDXgVdWBVfW5qjq7qo5YYvj1quot4/CPV9XmedYDAAAwZXMLeFW1U5KjkhyUZP8kh1bV/otGe1KSi7v755O8PMmL51UPAADA1M2zBe8eSc7u7nO6+9Ikb05yyKJxDkny+rH77UkeWFU1x5oAAAAmq7p7PjOuemSSA7v7N8fHv5Hknt19+Mw4nxnHuWB8/IVxnG8smtdhSQ5Lkn322efu55133lxqBgDYkWw+4t3rXcLknfuih613CUxQVX2yu7csNWxD3GSlu4/u7i3dvWXTpk3rXQ4AAMB2aZ4B78Ike8883mvst+Q4VbVzkj2SfHOONQEAAEzWznOc98lJ9quqW2YIco9J8muLxjk+yeOTfDTJI5N8oOd1zigAAD/F6YMwPXMLeN19eVUdnuTEJDslOaa7z6iq5yc5pbuPT/JXSd5YVWcn+VaGEAgAAMA1MM8WvHT3CUlOWNTvOTPdlyT51XnWAAAAsKPYEDdZAQAA4OoJeAAAABMh4AEAAEyEgAcAADARAh4AAMBECHgAAAATIeABAABMhIAHAAAwEQIeAADARAh4AAAAEyHgAQAATISABwAAMBHV3etdw5pU1UVJzlvvOiZizyTfWO8iuMasv43N+tvYrL+Ny7rb2Ky/jc3623r27e5NSw3YcAGPraeqTunuLetdB9eM9bexWX8bm/W3cVl3G5v1t7FZf9uGUzQBAAAmQsADAACYCAFvx3b0ehfAtWL9bWzW38Zm/W1c1t3GZv1tbNbfNuAaPAAAgInQggcAADARAt5WVFXfW6Lf71TV47ZxHSdV1eeq6lNVdXJVHbAtn38lVXVwVR2x3nVsT6rqiqo6rao+U1XvrKobjf1/rqrevsw0J1WVu1BtJ5b67DM/M5+ZM8bt3NOr6hrtz6rq+VX1oBWGX+tteFXdaaz3tKr6VlV9cex+37WZ77zMvp+r6qFV9fmq2reqjqyqH1TVTZcad4X5nbCwXVthnCW3aVX1hKr68zUuwpqMy3XhuE7OrKpD5/l8a7HSfmCqquqPxs/26eM6eW5VvXDROAdU1Vlj97lV9elx/H+pqn3Xp/IdW1XtPW7bbjI+vvH4eHNV7VdV76qqL1TVJ6vqg1V133G8J1TVRTPb9LdX1Q3Wd2k2PgFvzrr7td39hnnNvwZLrcdf7+67JHl1kj/bSs+107WdR3cf390v2hr1TMgPu/uA7r5jkm8l+d0k6e4vd/cj17c02C4tfGbukOTBSQ5K8txrMqPufk53Lxu0tsY2vLs/PdZ7QJLjkzxzfPyTYFlVO1+b55iHqnpgklcmOai7F35/9htJnr6W+XT3Q7v721u5vKu1wv5xKS8f188hSf6iqnbZCs9/rdfpjrYfqKpfTPJLSe7W3XdO8qAkH0zy6EWjPibJm2YeP2Ac/6Qkz94GpbJId5+f5DVJFr7jvSjD9XZfTfLuJEd39627++5JnpzkVjOTv2Vmm35prrq+WSMBb87GI4PPGLtPqqoXV9UnxiOi/2Xsv1NV/dnY2nZ6Vf322H+3qnp/Vf37eHTqkLH/5rGF7g1JPpNk7xVK+GiSW4zT/UxVHTM+/6kz87tBVb11PHJ5XFV9fOFIalV9r6peWlWfSvKLVfXYcfrTquovxtp3qqpja2iB+nRV/f447e+N8zy9qt489vvJ0dhxOT4wDn9/Ve0z9j+2ql5ZVf9WVedU1Q6zc8tPr6/NVfWZsfv6VfXmqjqrqo5Lcv2FCarqSeP76RNV9Zczr++mqvr78X11clXdez0WaEdVVb88fpZOrar3VdXNxv73qytbc06tqt2r6uZV9aG6siV3Ydtw6PiZ+kxVvXh9l2j71N1fT3JYksPHL/RLbk+TpKr+YHw9P1VVLxr7HbuwjamqF81ss14y9pvdhh9QVR8bhx9XVTce+y+5bb8643SvqKpTkjylqu5eQwvEJ6vqxKq6+Tjeravqn8f+H66q223Fl3C52u6b5C+T/FJ3f2Fm0DFJHl3jUfpF01xl/zD2P7eq9hy7/7iG/de/VtWbFl7b0a8u8xruPb5W/1FVz515vqeNn43PVNVTx35X2T8utX9aTnf/R5IfJFlYt8+ceS89b+a5l1yONazTpfaPS20bZvcDu1bVX4/LcWpVPWDs/4Sq+ofxPfIfVfV/VlrG7dzNk3yju3+UJN39je7+UJKLq+qeM+M9Kj8d8Bb8ZB/Kunh5knuNn8f7JHlJkl9P8tHuPn5hpO7+THcfu3jiGg6K/EySi7dJtVPW3f620l+S7y3R78gkzxi7T0ry0rH7oUneN3YfluTZY/f1kpyS5JZJdk5yw7H/nknOTlJJNif5cZJ7LVPHSUm2jN1PTfKCsfsFSR47dt8oyeczfJCekeQvxv53THL5zPSd5FFj9+2TvDPJLuPjVyd5XJK7J3nvzPPfaPz/5STXW9TvCUn+fOx+Z5LHj93/I8k7xu5jk7wtwwGI/ZOcvd7rdlu8b5LsNC73gePjzUk+M3Y/LckxY/edF9ZRkp9Lcm6SmyTZJcmHZ17fv0tyn7F7nyRnrfeyTvVvmc/+jXPljax+c+az/84k9x67dxs/509P8kcz74Pdx3X7pSSbxnE+kOTh672s28PfMq/3t5PcLMtvTw9K8m9JbjAOu8n4/9gkj0zys0k+N7PObjT+PzJXbsNPT3K/sfv5SV4xdp+UJbbty9R+bJJHzkz36rF7l7G+TePjR8985t+fZL+x+55JPjDn1/eyDGcT3HlR/yMz7C+ek+R5s+siy+wfxu5zM+zDfiHJaUl2Hd/j/5Gr3z8+IclXxvVz/QyhbUuG/c6nM+zDdktyRpK7ZtH+Mcvsn5ZarrH7bkk+PHY/JEMLRGXYH70ryX1XsRyrWadL7R+X2jZszpX7gafPTH+7DNuHXcfX6Jwke4yPz0uy93p/Tq/he2+38bX9/PgeWvi8PSNDK2uS3CvJKTPTnJtkz7H7FUkOW+/l2JH/kvy3DN8dHzw+flmSp6ww/hOSXDSu969l+B6z03ovx0b/2+5OCdkB/MP4/5MZNtzJsBO5c13ZUrVHkv2SXJDkBeOR1B9nOCp1s3Gc87r7Yys8z99W1XUzbCwPmHmeg2eOmO6a4Yv/fZL832Q4qlJVp8/M54okfz92PzDDzvLkqkqGne3XM+yUblVVr8rQDP+ecfzTxzrekeQdS9T4i0n++9j9xiSzRx3f0d0/TnJmjS0fE3b9qjotw/o9K8l7lxjnvhlOlUp3nz6zju6R5F+6+1tJUlVvS3KbcdiDkuw/rqskuWFV7dbdrhfbNvZK8pbxiP11k3xx7P+RJC+rqr9N8g/dfUFVnZzkmBpOC3tHd59WVf81yUndfVGSjOPfN0t/lrjSctvTByX56+7+QZIsfGZmfCfJJUn+qqreleHL/E9U1R4Zvoj/y9jr9RkOyCxYatu+Gm8Z/982wwG2946f2Z2SfKWqdkvy/yV528xn+XprmP81cVmGYPKkJE9ZYvgrk5y20Mo5Wm7/MOveSf6xuy9JcklVvXPR8OVew/d29zeTpKr+IcM+q5Mc193fn+n/XzKcAju7fzwnS++fFvv9qnpihu3nL4/9HjL+nTo+3i3De2n3q1mOFdfpOGyp/eNS24bZ+d4nyauSpLs/W1Xn5crt/fu7+zvja3Fmkn2TnL/Msm63uvt7VXX3DOvyARm2oUdkeE3/raqenquenpkkHxxblb+X5I+3Zc1cxUEZ3ud3zBLfZ2o4C2m/JJ/v7oXvgG/p7sNreMMfleSZufJUT64Bp2huez8a/1+R/CRgV5In93iNRnffsrvfk6FZe1OSu/dwbcDXMoSyJPn+1TzPr2c4v/n1GXcI4/M8YuZ59unus65mPpd09xUz079+ZvrbdveR3X1xkrtkOHL5O0leN47/sAwf1Ltl2Omv5YDCj2a6a9mxpuGH4/rdN8Oy/u5Wmu91MhzFXlhftxDutqlXZWhNvVOS38742e3hGtTfzPAF+CNVdbseTkG6b5ILkxxb2/jGTBtdVd0qwzb161l+e7qi7r48wwGTt2e4Buif11jGUtv21VjYlleSM2bqvlN3PyTD5/jbM/0P6O7br7G2tfpxhlPg7lFVf7h4YA/X0/1dfnpbteT+YY3Pu9xruPj3nK7u951+sn9cYf+02Mt7uP7nERlC/q4ZlumFM8v08939V6tYjqtbp8kS+8eltg2reK4Fs/vMtb4HtyvdfUV3n9Tdz01yeIbvLednOEh2vwzr6C2LJntAhn3oaUmeF9ZFDTf1e3CGVtbfHw9wnpHhfZ4k6e5fydBqd5XTvLu7MzQa3HcblDtpAt724cQk/3M8ep+quk1V/UyGI89f7+7LxnPt13RnqPGD8scZzoe+3fg8Tx6PkKSq7jqO+pEMO/NU1f5J7rTMLN+f5JE13kGtqm5Sw53V9kxyne7++wwXN9+thgvb9+7uDyb5g3FZdls0v3/LcCQuGQLph9eyfFMztiz8XpKnLxGGP5Tk15Kkqu6Y4TTNJDk5yf1quFvVzhl2fAvek+FC5ozTHTCn0lnaHhkCW5I8fqFnVd26h5tuvDjD+rtdDXd9+1p3/2WGL6B3S/KJDOt2zxquZTo0yb+En1JVm5K8NkOY7iy/PX1vkifWeHe2WnQN2dhStkd3n5Dk9zOEgp8YW0curiuvDfuNbN318bkkm2q4yUSqapequkN3/2eSL1bVr479q6rustKMtoZxe/SwJL9eVU9aYpSXZThwsbCtWnL/sGiajyT55fFast0yBOnVePA4v+snefg4nw8neXgN15D/TJJfyRL7kKX2Tys9UQ/XCZ2S4TN7YpL/MdaaqrrFuHyrXY4l1+ly+8eltg2L5vfhDPvKVNVtMpyB87mVlmejqarbVtV+M70OyHDKaTK02r08yTndfcHiaceDNE9N8rjFn2/mb/xu+ZokT+3uL2W4wd9LMhwMundVHTwz+kp3ybxPki+sMJxV2LBHeLZTN6iq2Y3Oy1Y53esynI7y7+MH5KIMO7G/TfLOqvp0hh3OZ9daUHf/sKpemqG5+/AM56efPu5gvphhx/TqJK8fT+v4bIajLd9ZYl5nVtWzk7xnnP6yDEdwf5jkr+vKu5U9K8OpKH8zntZUSV7Z3d9edLrJk8fpnjku8xPXunxT092n1nD65aH56S8rr8nwWp2V4TTOT47jX1hVL8gQBr6VYf0trLvfS3LUOL+dM4TE39kmC7LjWeqzf2SG0+ouznD93C3HYU8dD9j8OMNn7Z8yHOh4ZlVdluEUo8d191fGU5M+mOEz9O7u/sdtsjTbv4XTmnfJcD3qG3Pl9nbJ7Wl3//N4kOOUqro0yQlJZlundk/yjzMtN09b4nkfn+S1Y0g8J1txm9Xdl9ZwWukrx+3mzhm212dk+FL/mnH7u0uSNyf51NZ67hVq+lZVHZjkQ1V10aJh3xhPtfr98fFy+4fzZqY5uaqOz3B64tcyXEN3lX3NEj6R4VKBvZL8TXefkgw3yBmHJcnrxu3n5kXT3iJX3T9dnedn+FJ6+/Hvo+O+63sZrmNf1XKssE4/n6X3j3+yxLbh5jOzfHWG98GnM7zvn9DdP1q0X93odkvyqhp+VuPyDPceOGwc9rYMpwc/eelJk3G7+aYM770/mW+pLPJbSb7U3QunZb46wzbyHhm+a76sql6R4TPz3ST/e2baR1fVfTI0PF2QoYWPa2HhYnJ2YGPrwC7dfUlV3TrJ+5LctrsvXefSWIUar6sbW/COy3AR/nHrXRfAYjPbqxtkOOh0WHf/+3rXtVZTWQ5gmrTgkQxN5R8cT2mqJP+/cLehHFnDDzXvmuG0zHesbzkAyzp6vBRg1wzX7G3UUDSV5QAmSAseAADARLjJCgAAwEQIeAAAABMh4AEAAEyEgAfADququqr+ZubxzlV1UVW9a43zOXf8zbVrNQ4AXFsCHgA7su8nueP4I9pJ8uBc+QP1ALDhCHgA7OhOSPKwsfvQJG9aGFBVN6mqd1TV6VX1saq689j/Z6vqPVV1RlW9LsNPzCxM89iq+kRVnVZVfzH+1igAbBMCHgA7ujcneUxV7Zrkzkk+PjPseUlO7e47J/nDJG8Y+z83yb929x2SHJdknySpqtsneXSSe3f3AUmuSPLr22IhACDxQ+cA7OC6+/Sq2pyh9e6ERYPvk+QR43gfGFvubpjkvkn++9j/3VV18Tj+A5PcPcnJVZUk10/y9bkvBACMBDwASI5P8pIk90/ys9diPpXk9d39rK1RFACslVM0ASA5JsnzuvvTi/p/OOMpllV1/yTf6O7/TPKhJL829j8oyY3H8d+f5JFVddNx2E2qat+5Vw8AIy14AOzwuvuCJK9cYtCRSY6pqtOT/CDJ48f+z0vypqo6I8m/JfnSOJ8zq+rZSd5TVddJclmS301y3nyXAAAG1d3rXQMAAABbgVM0AQAAJkLAAwAAmAgBDwAAYCIEPAAAgIkQ8AAAACZCwAMAAJgIAQ8AAGAiBDwAAICJ+H935felFortxwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 6))\n",
        "plt.bar(performances.keys(), [p[2] for p in performances.values()])\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Mean Absolute Percentage Error')\n",
        "plt.title('Model performance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "r4FvyfrGoKWQ",
        "outputId": "0a243c58-2907-49f5-faaa-6b150385fd73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb = XGBRegressor(n_estimators=1000, learning_rate=0.01)\n",
        "xgb.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "oXUuX0xfnL2A",
        "outputId": "2dcbf84e-6d77-482c-e95e-3d8013f808a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = xgb.predict(X_test)\n",
        "print('MAPE : ', mean_absolute_percentage_error(y_test, prediction))\n",
        "print('R2 : ', r2_score(y_test, prediction))\n",
        "print('MAE : ', mean_absolute_error(y_test, prediction))\n",
        "results = cross_val_score(xgb, X_train, y_train, scoring='neg_mean_absolute_percentage_error', cv=kf)\n",
        "print('cross val score : ', np.mean(-results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nr-99b9XosjH",
        "outputId": "d89c7aa8-80d7-4391-fba1-802977ef5cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MAPE :  0.012091712302099636\n",
            "R2 :  0.9990475619024936\n",
            "MAE :  59824.84970649171\n",
            "cross val score :  0.014832139002595276\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "region_mappings_le"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pIcSi-bWopS5",
        "outputId": "b12760cf-81a9-4ce3-d896-8af7a7478b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Abdalpur',\n",
              " 1: 'Adarsha Nagar',\n",
              " 2: 'Agarpara',\n",
              " 3: 'Airport',\n",
              " 4: 'Alipore',\n",
              " 5: 'Amtala',\n",
              " 6: 'Ariadaha',\n",
              " 7: 'Ashokgarh',\n",
              " 8: 'Aurobindo Park',\n",
              " 9: 'Badartala',\n",
              " 10: 'Baghajatin',\n",
              " 11: 'Baghbazar',\n",
              " 12: 'Bagmari',\n",
              " 13: 'Bagnan',\n",
              " 14: 'Bagpota',\n",
              " 15: 'Baguiati',\n",
              " 16: 'Baguihati',\n",
              " 17: 'Baishnabghata Patuli Township,',\n",
              " 18: 'Baithakkhana',\n",
              " 19: 'Ballygunge',\n",
              " 20: 'Bamangachhi',\n",
              " 21: 'Bamunpara',\n",
              " 22: 'Bangaon',\n",
              " 23: 'Bansdroni',\n",
              " 24: 'Bantala',\n",
              " 25: 'Barabazar Market',\n",
              " 26: 'Baranagar',\n",
              " 27: 'Barasat',\n",
              " 28: 'Barisha',\n",
              " 29: 'Barrackpore',\n",
              " 30: 'Baruipur',\n",
              " 31: 'Baruipur P',\n",
              " 32: 'Basirhat',\n",
              " 33: 'Behala',\n",
              " 34: 'Belgachia',\n",
              " 35: 'Belghoria',\n",
              " 36: 'Beliaghata',\n",
              " 37: 'Beniapukur',\n",
              " 38: 'Beniatola',\n",
              " 39: 'Berunanpukhuria',\n",
              " 40: 'Bhatenda',\n",
              " 41: 'Bhatpara',\n",
              " 42: 'Bhowanipore',\n",
              " 43: 'Bijoygarh',\n",
              " 44: 'Bira',\n",
              " 45: 'Birati',\n",
              " 46: 'Bishnupur',\n",
              " 47: 'Bow Barracks',\n",
              " 48: 'Bow Bazaar',\n",
              " 49: 'Bramhapur',\n",
              " 50: 'Budge Budge',\n",
              " 51: 'Champahati',\n",
              " 52: 'Chandpara',\n",
              " 53: 'Chinar Park',\n",
              " 54: 'Chitpur',\n",
              " 55: 'Chowbaga',\n",
              " 56: 'College Square',\n",
              " 57: 'Cossipore',\n",
              " 58: 'Dakshin Gobindopur',\n",
              " 59: 'Dakshineswar',\n",
              " 60: 'Dhakuria',\n",
              " 61: 'Diamond Harbour',\n",
              " 62: 'Doperia Village',\n",
              " 63: 'Dum Dum',\n",
              " 64: 'Dum Dum Cantonment',\n",
              " 65: 'Dunlop',\n",
              " 66: 'Duttapukur',\n",
              " 67: 'East Kolkata Township',\n",
              " 68: 'Elgin',\n",
              " 69: 'Entally',\n",
              " 70: 'Fatepur',\n",
              " 71: 'Ganguly Bagan',\n",
              " 72: 'Garden Reach',\n",
              " 73: 'Garfa',\n",
              " 74: 'Garia',\n",
              " 75: 'Gariahat',\n",
              " 76: 'Garulia',\n",
              " 77: 'Gobra',\n",
              " 78: 'Golf Green',\n",
              " 79: 'Habra',\n",
              " 80: 'Halisahar',\n",
              " 81: 'Haltu',\n",
              " 82: 'Hanspukuria',\n",
              " 83: 'Hedua',\n",
              " 84: 'Hridaypur',\n",
              " 85: 'Hussainpur',\n",
              " 86: 'Ichapur',\n",
              " 87: 'Jadavpur',\n",
              " 88: 'Jagatipota',\n",
              " 89: 'Jodhpur Park',\n",
              " 90: 'Joka',\n",
              " 91: 'Jorabagan',\n",
              " 92: 'Jorasanko',\n",
              " 93: 'Jugberia',\n",
              " 94: 'Kabardanga',\n",
              " 95: 'Kaikhali',\n",
              " 96: 'Kalagachhia',\n",
              " 97: 'Kalighat',\n",
              " 98: 'Kalikapur',\n",
              " 99: 'Kalyan Nagar',\n",
              " 100: 'Kalyani',\n",
              " 101: 'Kamalgazi',\n",
              " 102: 'Kamardanga',\n",
              " 103: 'Kamdahari',\n",
              " 104: 'Kanchrapara Loco',\n",
              " 105: 'Kankurgachi',\n",
              " 106: 'Kasba',\n",
              " 107: 'Kashipur',\n",
              " 108: 'Kazipara',\n",
              " 109: 'Keshtopur',\n",
              " 110: 'Khardah',\n",
              " 111: 'Khariberia',\n",
              " 112: 'Khidirpur',\n",
              " 113: 'Kokapur',\n",
              " 114: 'Kolkata',\n",
              " 115: 'Kolutolla',\n",
              " 116: 'Krishnanagar',\n",
              " 117: 'Kustia',\n",
              " 118: 'Lake Gardens',\n",
              " 119: 'Lake Town',\n",
              " 120: 'Machuabazar',\n",
              " 121: 'Madhyamgram',\n",
              " 122: 'Maheshtala',\n",
              " 123: 'Maidan',\n",
              " 124: 'Malancha Mahi Nagar',\n",
              " 125: 'Malickpur',\n",
              " 126: 'Maniktala',\n",
              " 127: 'Metiabruz',\n",
              " 128: 'Mohispota',\n",
              " 129: 'Mominpore',\n",
              " 130: 'Mukundapur',\n",
              " 131: 'Nabapally',\n",
              " 132: 'Naihati',\n",
              " 133: 'Naoabad',\n",
              " 134: 'Narayantala',\n",
              " 135: 'Narendrapur',\n",
              " 136: 'Natagarh',\n",
              " 137: 'Natunhat',\n",
              " 138: 'Nayabad',\n",
              " 139: 'Nazirabad',\n",
              " 140: 'Netaji Nagar',\n",
              " 141: 'New Alipore',\n",
              " 142: 'New Barrakpur',\n",
              " 143: 'New Town',\n",
              " 144: 'Nimta',\n",
              " 145: 'North Dum Dum',\n",
              " 146: 'Paikpara',\n",
              " 147: 'Pailan',\n",
              " 148: 'Palta',\n",
              " 149: 'Pancha Sayar',\n",
              " 150: 'Panchpota',\n",
              " 151: 'Panihati',\n",
              " 152: 'Pansila',\n",
              " 153: 'Park Street Area',\n",
              " 154: 'Paschim Barisha',\n",
              " 155: 'Paschim Putiary',\n",
              " 156: 'Patipukur',\n",
              " 157: 'Picnic Garden',\n",
              " 158: 'Purba Barisha',\n",
              " 159: 'Purba Putiary',\n",
              " 160: 'Raghunathpur',\n",
              " 161: 'Rahara',\n",
              " 162: 'Raja Bazar',\n",
              " 163: 'Rajabagan',\n",
              " 164: 'Rajarhat',\n",
              " 165: 'Rajpur',\n",
              " 166: 'Rajpur Sonarpur',\n",
              " 167: 'Ramchandrapur',\n",
              " 168: 'Rania',\n",
              " 169: 'Regent Park',\n",
              " 170: 'Ruiya',\n",
              " 171: 'Saha Para',\n",
              " 172: 'Salt Lake City',\n",
              " 173: 'Santoshpur',\n",
              " 174: 'Sarada Pally',\n",
              " 175: 'Sarsuna',\n",
              " 176: 'Sewli Telinipara',\n",
              " 177: 'Shobhabazar',\n",
              " 178: 'Shyambazar',\n",
              " 179: 'Shyamnagar',\n",
              " 180: 'Sinthi',\n",
              " 181: 'Sodepur',\n",
              " 182: 'South Dum Dum',\n",
              " 183: 'Sovabazar',\n",
              " 184: 'Srirampur',\n",
              " 185: 'Tagore Park',\n",
              " 186: 'Tala',\n",
              " 187: 'Talbanda',\n",
              " 188: 'Taltala',\n",
              " 189: 'Tangra',\n",
              " 190: 'Taratala',\n",
              " 191: 'Thakuranir Chak',\n",
              " 192: 'Thakurpukur',\n",
              " 193: 'Tiljala',\n",
              " 194: 'Tollygunge',\n",
              " 195: 'Topsia',\n",
              " 196: 'Ultadanga',\n",
              " 197: 'Uttarbhag',\n",
              " 198: 'VIP Nagar',\n",
              " 199: 'Vedic Village',\n",
              " 200: 'Ward No 113'}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_key(val):\n",
        "    for key, value in region_mappings_le.items():\n",
        "        if val == value:\n",
        "            return key\n",
        " \n",
        "    return \"key doesn't exist\""
      ],
      "metadata": {
        "id": "b6SP79stpFgW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict_price(BHK, Total_Sq_ft, Price_per_sq_ft, Region):\n",
        "  val = get_key(Region)\n",
        "  x = np.zeros(len(X.columns))\n",
        "  x[0] = BHK\n",
        "  x[1] = Total_Sq_ft\n",
        "  x[2] = Price_per_sq_ft\n",
        "  x[3] = val\n",
        "  x_scaled = scaler.transform([x])\n",
        "  # return x.shape\n",
        "  return xgb.predict(x_scaled)[0]"
      ],
      "metadata": {
        "id": "4zXf3YownhFn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price(6.0, 1408.0, 6889.0, 'Kolkata')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IT1LWDFypw5h",
        "outputId": "57afd563-6e2a-4b43-b21f-a99a5c5eeffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9465784.0"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price(4.0, 1581.0, 7000.0, 'Joka')"
      ],
      "metadata": {
        "id": "Vb0vGVyZqL0N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f73e6cec-9c29-427b-8800-5d3c66dc10cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11163463.0"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict_price(4.0, 2500.0, 4400.0, 'Kalyani')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t-8SS4Lhpn_B",
        "outputId": "3da85663-5b93-4ddb-d28b-21d7d09e3e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11233770.0"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('West_Bengal_House_Price_Prediction_Mode.pickle', 'wb') as f:\n",
        "  pickle.dump(xgb, f)"
      ],
      "metadata": {
        "id": "X0nNitcOqRtK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dump(lr, 'lr_model.joblib')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "st4dI1jbucC2",
        "outputId": "aad658d5-ded4-4e9d-82c5-3c83ed0b8e8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['lr_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_in = load('xgb_model.joblib')"
      ],
      "metadata": {
        "id": "yNlmi1gOJSOP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_in"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "sJT4vkUsJ3Id",
        "outputId": "c8687cdf-0ed6-4a28-de21-5fd1e81d3b3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qdacdEvIbZqN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}