HOUSE_PRICE_PEDICTION (3).ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "34273cdc",
      "metadata": {
        "id": "34273cdc"
      },
      "outputs": [],
      "source": [
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "import matplotlib\n",
        "matplotlib.rcParams[\"figure.figsize\"]=(20,20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "448bc99d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "448bc99d",
        "outputId": "5079cbe9-e101-4871-da0c-9ccd090f6c88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              area_type   availability                  location       size  \\\n",
              "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
              "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
              "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
              "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
              "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
              "\n",
              "   society total_sqft  bath  balcony   price  \n",
              "0  Coomee        1056   2.0      1.0   39.07  \n",
              "1  Theanmp       2600   5.0      3.0  120.00  \n",
              "2      NaN       1440   2.0      3.0   62.00  \n",
              "3  Soiewre       1521   3.0      1.0   95.00  \n",
              "4      NaN       1200   2.0      1.0   51.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-da5adacb-feb3-4b08-b9ee-f53741588a8d\" 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>area_type</th>\n",
              "      <th>availability</th>\n",
              "      <th>location</th>\n",
              "      <th>size</th>\n",
              "      <th>society</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>balcony</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>19-Dec</td>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>Coomee</td>\n",
              "      <td>1056</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>39.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Plot  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>Theanmp</td>\n",
              "      <td>2600</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>62.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>Soiewre</td>\n",
              "      <td>1521</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>95.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Super built-up  Area</td>\n",
              "      <td>Ready To Move</td>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da5adacb-feb3-4b08-b9ee-f53741588a8d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-da5adacb-feb3-4b08-b9ee-f53741588a8d 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-da5adacb-feb3-4b08-b9ee-f53741588a8d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-dfc59815-ddd8-4185-aefc-b1f6142dbcd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dfc59815-ddd8-4185-aefc-b1f6142dbcd5')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dfc59815-ddd8-4185-aefc-b1f6142dbcd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df=pd.read_csv(\"/content/Bengaluru_House_Data.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "8f3b26f5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8f3b26f5",
        "outputId": "4fda9961-d81b-44f3-93bd-d9de7da8ae37"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "area_type\n",
              "Built-up  Area          2418\n",
              "Carpet  Area              87\n",
              "Plot  Area              2025\n",
              "Super built-up  Area    8790\n",
              "Name: area_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.groupby(\"area_type\")['area_type'].agg('count')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "14def0bc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "14def0bc",
        "outputId": "b4dcb452-d5ba-4d2a-dccc-b67de2f7f1cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size total_sqft  bath   price\n",
              "0  Electronic City Phase II      2 BHK       1056   2.0   39.07\n",
              "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00\n",
              "2               Uttarahalli      3 BHK       1440   2.0   62.00\n",
              "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00\n",
              "4                  Kothanur      2 BHK       1200   2.0   51.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e33644e-28f9-48d9-a186-c7349e8e1acb\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e33644e-28f9-48d9-a186-c7349e8e1acb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e33644e-28f9-48d9-a186-c7349e8e1acb 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-1e33644e-28f9-48d9-a186-c7349e8e1acb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ba36d7db-7c0f-42dd-bf3c-5a0786aa35ed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba36d7db-7c0f-42dd-bf3c-5a0786aa35ed')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ba36d7db-7c0f-42dd-bf3c-5a0786aa35ed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df2=df.drop(['area_type','society','balcony','availability'],axis='columns')\n",
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "e9be388e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9be388e",
        "outputId": "08f7749b-df38-443c-89c4-9883f7ec10e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location       1\n",
              "size          16\n",
              "total_sqft     0\n",
              "bath          73\n",
              "price          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df2.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "a6db4019",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a6db4019",
        "outputId": "5e717810-b14d-4b23-b950-f819adbbc69f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location      0\n",
              "size          0\n",
              "total_sqft    0\n",
              "bath          0\n",
              "price         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df3=df2.dropna()\n",
        "df3.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "c14ad0c6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c14ad0c6",
        "outputId": "a6c47894-b37d-4cf0-88ab-b8e421295c44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['2 BHK', '4 Bedroom', '3 BHK', '4 BHK', '6 Bedroom', '3 Bedroom',\n",
              "       '1 BHK', '1 RK', '1 Bedroom', '8 Bedroom', '2 Bedroom',\n",
              "       '7 Bedroom', '5 BHK', '7 BHK', '6 BHK', '5 Bedroom', '11 BHK',\n",
              "       '9 BHK', '9 Bedroom', '27 BHK', '10 Bedroom', '11 Bedroom',\n",
              "       '10 BHK', '19 BHK', '16 BHK', '43 Bedroom', '14 BHK', '8 BHK',\n",
              "       '12 Bedroom', '13 BHK', '18 Bedroom'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df3['size'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "75e17eb8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "75e17eb8",
        "outputId": "e219ab9e-1744-472b-977f-6f6cc3f54c6b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-84b303985ccb>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df3['BHK']=df3['size'].apply(lambda x: int(x.split(' ')[0]))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size total_sqft  bath   price  BHK\n",
              "0  Electronic City Phase II      2 BHK       1056   2.0   39.07    2\n",
              "1          Chikka Tirupathi  4 Bedroom       2600   5.0  120.00    4\n",
              "2               Uttarahalli      3 BHK       1440   2.0   62.00    3\n",
              "3        Lingadheeranahalli      3 BHK       1521   3.0   95.00    3\n",
              "4                  Kothanur      2 BHK       1200   2.0   51.00    2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f6b1f39-f4bd-43a2-b3be-d17f609f92bb\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f6b1f39-f4bd-43a2-b3be-d17f609f92bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4f6b1f39-f4bd-43a2-b3be-d17f609f92bb 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-4f6b1f39-f4bd-43a2-b3be-d17f609f92bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-32355156-5e9c-4db3-8248-6755f0c3952f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32355156-5e9c-4db3-8248-6755f0c3952f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-32355156-5e9c-4db3-8248-6755f0c3952f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df3['BHK']=df3['size'].apply(lambda x: int(x.split(' ')[0]))\n",
        "df3.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "1df3e67c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1df3e67c",
        "outputId": "955246a6-5516-41c4-f4d0-ed5c5e6a0162"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1056', '2600', '1440', ..., '1133 - 1384', '774', '4689'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df3.total_sqft.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "4836bbb5",
      "metadata": {
        "id": "4836bbb5"
      },
      "outputs": [],
      "source": [
        "def is_float(x):\n",
        "    try:\n",
        "        float(x)\n",
        "    except:\n",
        "        return False\n",
        "    return True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "7a4c8747",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "7a4c8747",
        "outputId": "f60bdb22-51c9-48f2-e2f6-05c4a2de86f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               location       size      total_sqft  bath    price  BHK\n",
              "30            Yelahanka      4 BHK     2100 - 2850   4.0  186.000    4\n",
              "122              Hebbal      4 BHK     3067 - 8156   4.0  477.000    4\n",
              "137  8th Phase JP Nagar      2 BHK     1042 - 1105   2.0   54.005    2\n",
              "165            Sarjapur      2 BHK     1145 - 1340   2.0   43.490    2\n",
              "188            KR Puram      2 BHK     1015 - 1540   2.0   56.800    2\n",
              "410             Kengeri      1 BHK  34.46Sq. Meter   1.0   18.500    1\n",
              "549         Hennur Road      2 BHK     1195 - 1440   2.0   63.770    2\n",
              "648             Arekere  9 Bedroom       4125Perch   9.0  265.000    9\n",
              "661           Yelahanka      2 BHK     1120 - 1145   2.0   48.130    2\n",
              "672        Bettahalsoor  4 Bedroom     3090 - 5002   4.0  445.000    4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f28691b1-c4e0-4556-bf01-1edf433929bd\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2100 - 2850</td>\n",
              "      <td>4.0</td>\n",
              "      <td>186.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>Hebbal</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>3067 - 8156</td>\n",
              "      <td>4.0</td>\n",
              "      <td>477.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>137</th>\n",
              "      <td>8th Phase JP Nagar</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1042 - 1105</td>\n",
              "      <td>2.0</td>\n",
              "      <td>54.005</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>Sarjapur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1145 - 1340</td>\n",
              "      <td>2.0</td>\n",
              "      <td>43.490</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>KR Puram</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1015 - 1540</td>\n",
              "      <td>2.0</td>\n",
              "      <td>56.800</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Kengeri</td>\n",
              "      <td>1 BHK</td>\n",
              "      <td>34.46Sq. Meter</td>\n",
              "      <td>1.0</td>\n",
              "      <td>18.500</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549</th>\n",
              "      <td>Hennur Road</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1195 - 1440</td>\n",
              "      <td>2.0</td>\n",
              "      <td>63.770</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>648</th>\n",
              "      <td>Arekere</td>\n",
              "      <td>9 Bedroom</td>\n",
              "      <td>4125Perch</td>\n",
              "      <td>9.0</td>\n",
              "      <td>265.000</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>661</th>\n",
              "      <td>Yelahanka</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1120 - 1145</td>\n",
              "      <td>2.0</td>\n",
              "      <td>48.130</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>Bettahalsoor</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>3090 - 5002</td>\n",
              "      <td>4.0</td>\n",
              "      <td>445.000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f28691b1-c4e0-4556-bf01-1edf433929bd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f28691b1-c4e0-4556-bf01-1edf433929bd 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-f28691b1-c4e0-4556-bf01-1edf433929bd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-967feea8-862b-49ae-8d60-284d256666c9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-967feea8-862b-49ae-8d60-284d256666c9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-967feea8-862b-49ae-8d60-284d256666c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df3[~df3['total_sqft'].apply(is_float)].head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "71efc36d",
      "metadata": {
        "id": "71efc36d"
      },
      "outputs": [],
      "source": [
        "def convert_sqft_to_num(x):\n",
        "    tokens=x.split('-')\n",
        "    if len(tokens)==2:\n",
        "        return (float(tokens[0])+float(tokens[1]))/2\n",
        "    try:\n",
        "        return float(x)\n",
        "    except:\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "52a2be10",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52a2be10",
        "outputId": "5cee0f3a-2088-4e4e-9b2b-3a59efa48240"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2166.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "convert_sqft_to_num('2166')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "a33d1370",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a33d1370",
        "outputId": "44927356-edf8-49f7-8bba-9cad088fdf9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2255.5"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "convert_sqft_to_num('2166-2345')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "b4ee13e0",
      "metadata": {
        "id": "b4ee13e0"
      },
      "outputs": [],
      "source": [
        "convert_sqft_to_num('36.6sq.metre')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "82d050ec",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "82d050ec",
        "outputId": "02aefc33-be99-4cc6-eecd-36e86dbe2798"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size  total_sqft  bath   price  BHK\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2\n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4\n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3\n",
              "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3\n",
              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc4486f4-3c70-493c-a4f9-65ef783b0dce\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc4486f4-3c70-493c-a4f9-65ef783b0dce')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dc4486f4-3c70-493c-a4f9-65ef783b0dce 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-dc4486f4-3c70-493c-a4f9-65ef783b0dce');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6a10fabc-287a-402f-b4d6-8aa436745c6c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a10fabc-287a-402f-b4d6-8aa436745c6c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6a10fabc-287a-402f-b4d6-8aa436745c6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df4=df3.copy()\n",
        "df4['total_sqft']=df3['total_sqft'].apply(convert_sqft_to_num)\n",
        "df4.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "77c2ceef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "77c2ceef",
        "outputId": "4b320540-cf7b-4c7d-bb66-59575b5226ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   location       size  total_sqft  bath   price  BHK  \\\n",
              "0  Electronic City Phase II      2 BHK      1056.0   2.0   39.07    2   \n",
              "1          Chikka Tirupathi  4 Bedroom      2600.0   5.0  120.00    4   \n",
              "2               Uttarahalli      3 BHK      1440.0   2.0   62.00    3   \n",
              "3        Lingadheeranahalli      3 BHK      1521.0   3.0   95.00    3   \n",
              "4                  Kothanur      2 BHK      1200.0   2.0   51.00    2   \n",
              "\n",
              "   price_per_sqft  \n",
              "0     3699.810606  \n",
              "1     4615.384615  \n",
              "2     4305.555556  \n",
              "3     6245.890861  \n",
              "4     4250.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e884df11-6ae4-4820-b2d5-6a8d7428c2f3\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Electronic City Phase II</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1056.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>39.07</td>\n",
              "      <td>2</td>\n",
              "      <td>3699.810606</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Chikka Tirupathi</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2600.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>120.00</td>\n",
              "      <td>4</td>\n",
              "      <td>4615.384615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Uttarahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1440.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>62.00</td>\n",
              "      <td>3</td>\n",
              "      <td>4305.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lingadheeranahalli</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>95.00</td>\n",
              "      <td>3</td>\n",
              "      <td>6245.890861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kothanur</td>\n",
              "      <td>2 BHK</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>51.00</td>\n",
              "      <td>2</td>\n",
              "      <td>4250.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e884df11-6ae4-4820-b2d5-6a8d7428c2f3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e884df11-6ae4-4820-b2d5-6a8d7428c2f3 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-e884df11-6ae4-4820-b2d5-6a8d7428c2f3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-728c0a4b-2971-44f7-9a78-121842ebb8a0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-728c0a4b-2971-44f7-9a78-121842ebb8a0')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-728c0a4b-2971-44f7-9a78-121842ebb8a0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df5=df4.copy()\n",
        "df5['price_per_sqft']=df5['price']*100000/df5['total_sqft']\n",
        "df5.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "d3ce9b05",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3ce9b05",
        "outputId": "2d52b9a8-9218-4a12-9127-5c90c3516cce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1304"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "len(df5.location.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "6e15f6a2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6e15f6a2",
        "outputId": "33d07e38-7f19-4fab-fd56-76ce9c6a5e54"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location\n",
              "Whitefield               535\n",
              "Sarjapur  Road           392\n",
              "Electronic City          304\n",
              "Kanakpura Road           266\n",
              "Thanisandra              236\n",
              "                        ... \n",
              "1 Giri Nagar               1\n",
              "Kanakapura Road,           1\n",
              "Kanakapura main  Road      1\n",
              "Karnataka Shabarimala      1\n",
              "whitefiled                 1\n",
              "Name: location, Length: 1293, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df5.location=df5.location.apply(lambda x: x.strip())\n",
        "location_stats=df5.groupby('location')['location'].agg('count').sort_values(ascending=False)\n",
        "location_stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "96836960",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96836960",
        "outputId": "8e7bbcf3-8ec3-431c-d6b6-122723633585"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1052"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "len(location_stats[location_stats<=10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "fc49de26",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fc49de26",
        "outputId": "6a65d5c7-37ac-4ae6-f026-c5607d51f548"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "location\n",
              "Basapura                 10\n",
              "1st Block Koramangala    10\n",
              "Gunjur Palya             10\n",
              "Kalkere                  10\n",
              "Sector 1 HSR Layout      10\n",
              "                         ..\n",
              "1 Giri Nagar              1\n",
              "Kanakapura Road,          1\n",
              "Kanakapura main  Road     1\n",
              "Karnataka Shabarimala     1\n",
              "whitefiled                1\n",
              "Name: location, Length: 1052, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "location_stats_lessthan_10=location_stats[location_stats<=10]\n",
        "location_stats_lessthan_10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "40c2ea7a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40c2ea7a",
        "outputId": "0c483089-b678-4523-a9cf-f2c5913e6876"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1293"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "len(df5.location.unique())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "af00250b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "af00250b",
        "outputId": "f5f383fd-d2db-4c1e-b804-7c21960e87cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "242"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df5.location=df5.location.apply(lambda x: 'other' if x in location_stats_lessthan_10 else x)\n",
        "len(df5.location.unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "d11d92c4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "d11d92c4",
        "outputId": "459b5ace-4e0e-498f-9643-08ee3f3f9850"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               location       size  total_sqft  bath  price  BHK  \\\n",
              "9                 other  6 Bedroom      1020.0   6.0  370.0    6   \n",
              "45           HSR Layout  8 Bedroom       600.0   9.0  200.0    8   \n",
              "58        Murugeshpalya  6 Bedroom      1407.0   4.0  150.0    6   \n",
              "68  Devarachikkanahalli  8 Bedroom      1350.0   7.0   85.0    8   \n",
              "70                other  3 Bedroom       500.0   3.0  100.0    3   \n",
              "\n",
              "    price_per_sqft  \n",
              "9     36274.509804  \n",
              "45    33333.333333  \n",
              "58    10660.980810  \n",
              "68     6296.296296  \n",
              "70    20000.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-724fe136-bf2c-49c4-874e-fa2792dedd53\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>other</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1020.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>370.0</td>\n",
              "      <td>6</td>\n",
              "      <td>36274.509804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>HSR Layout</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>600.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>8</td>\n",
              "      <td>33333.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Murugeshpalya</td>\n",
              "      <td>6 Bedroom</td>\n",
              "      <td>1407.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>6</td>\n",
              "      <td>10660.980810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Devarachikkanahalli</td>\n",
              "      <td>8 Bedroom</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>8</td>\n",
              "      <td>6296.296296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>other</td>\n",
              "      <td>3 Bedroom</td>\n",
              "      <td>500.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>3</td>\n",
              "      <td>20000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-724fe136-bf2c-49c4-874e-fa2792dedd53')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-724fe136-bf2c-49c4-874e-fa2792dedd53 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-724fe136-bf2c-49c4-874e-fa2792dedd53');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fb6bde47-ddb4-4b68-a55e-9bf22f64b3f1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb6bde47-ddb4-4b68-a55e-9bf22f64b3f1')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fb6bde47-ddb4-4b68-a55e-9bf22f64b3f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df5[df5.total_sqft/df5.BHK<300].head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "0b08aca1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0b08aca1",
        "outputId": "c7da6c46-f0d3-4e59-c650-9696db13170a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13246, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df5.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "ee76131b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee76131b",
        "outputId": "4c06a334-96ba-4584-9350-6a7f3f033589"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12502, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df6=df5[~(df5.total_sqft/df5.BHK<300)]\n",
        "df6.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "72125ff6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72125ff6",
        "outputId": "2c45b6be-8b30-4226-a08a-a1bde24fd02b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     12456.000000\n",
              "mean       6308.502826\n",
              "std        4168.127339\n",
              "min         267.829813\n",
              "25%        4210.526316\n",
              "50%        5294.117647\n",
              "75%        6916.666667\n",
              "max      176470.588235\n",
              "Name: price_per_sqft, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df6.price_per_sqft.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "a7a3bb61",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a7a3bb61",
        "outputId": "d26cc5bd-c09b-404d-da43-94828e79af9c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10241, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "def remove_pps_outliers(df):\n",
        "    df_out=pd.DataFrame()\n",
        "\n",
        "    for key,subdf in df.groupby('location'):\n",
        "        m=np.mean(subdf.price_per_sqft)\n",
        "        st=np.std(subdf.price_per_sqft)\n",
        "        reduced_df=subdf[(subdf.price_per_sqft>(m-st)) & (subdf.price_per_sqft<=(m+st))]\n",
        "        df_out=pd.concat([df_out,reduced_df],ignore_index=True)\n",
        "    return df_out\n",
        "df7=remove_pps_outliers(df6)\n",
        "df7.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "ead0462b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "ead0462b",
        "outputId": "595b2564-ec17-4f31-93b2-25a10fc8de21"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABNYAAANXCAYAAAD5PtJMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB6oklEQVR4nOz9eZhdZZ0vbn92pTLuoiqCJAEJioBgmFSwJQZQEImAiAQ9rdIMfTk0EFCSxuakm8HgEPVwiDgkaMtPREA9IKDgq4gDSDCAB5lRbBEIAklQDylqU0lVqvb7R3WKFBnIXtRc931d+6q1nvXsvb8rWQT45BlK1Wq1GgAAAACgJnUDXQAAAAAADEWCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAgEGuVCrlU5/6VM3vu/nmm1MqlXLzzTd3t5100kl5zWte02u1AQCMZII1AIBecumll6ZUKnW/6uvr86pXvSonnXRSnnzyyYEuryZvf/vbUyqVctRRR21w7bHHHkupVMoFF1wwAJUBAAwe9QNdAADAcHP++ednp512yurVq3P77bfn0ksvzZIlS/LAAw9k3LhxNX9ea2tr6utr/8+2gw46KK2trRkzZkx323/+53+ms7Nziz/jhhtuyF133ZV999235u8HABjujFgDAOhlhx9+eP7pn/4pH/nIR/LNb34zZ555Zh555JH86Ec/KvR548aNKxSs1dXVZdy4camre+E/+UaPHp2xY8du0ft33HHHvOIVr8j8+fNr/u6BVqlUBroEAGAEEKwBAPSxAw88MEnyyCOPdLe1tbXl3HPPzb777pumpqaUy+UceOCB+dWvfrXB+1+8xtrjjz+eU089NbvttlvGjx+fbbbZJu9///vz2GOP9Xjfy11jbauttsqcOXNy/fXX53e/+91m+/7973/PmWeemb322isNDQ1pbGzM4YcfnnvvvXeDvo8//nje8573pFwuZ9KkSZkzZ05uvPHGDWq99dZb8/73vz877rhjxo4dm6lTp2bOnDlpbW3t8XknnXRSGhoa8sgjj+SII47IVlttleOOO26L7hEA4OUwFRQAoI+tC7xe8YpXdLc1Nzfnm9/8Zj74wQ/mox/9aJ577rlccsklmTlzZu6888684Q1v2OTn/fa3v81vfvObfOADH8gOO+yQxx57LIsXL87b3/72PPTQQ5kwYUKv1f6JT3wiCxcuzKc+9anNjrj785//nOuuuy7vf//7s9NOO2XFihX5+te/nre97W156KGHsv322yfpGkl2yCGH5Omnn84nPvGJTJkyJVdeeeVGA8Wrrroqzz//fE455ZRss802ufPOO/OVr3wlf/nLX3LVVVf16Lt27drMnDkzBxxwQC644IJe/TUAANgUwRoAQC9btWpV/vrXv2b16tW54447Mn/+/IwdOzbvfve7u/u84hWvyGOPPdZj/bOPfvSj2X333fOVr3wll1xyySY//8gjj8z73ve+Hm1HHXVUpk+fnh/84Ac5/vjje+1eGhsbc8YZZ+S8887L7373u7zpTW/aaL+99torf/zjH3tMOz3++OOz++6755JLLsk555yTJPn617/eHcIdffTRSZJ/+Zd/yRvf+MYNPvMLX/hCxo8f333+sY99LLvsskv+/d//PcuWLcuOO+7YfW3NmjV5//vfnwULFvTKfQMAbAlTQQEAetmhhx6abbfdNlOnTs373ve+lMvl/OhHP8oOO+zQ3WfUqFHdoVpnZ2f+/ve/Z+3atdlvv/1ectrl+mFTe3t7/va3v2WXXXbJxIkTX/K9RXziE594ybXWxo4d2x2qdXR05G9/+1saGhqy22679ajppz/9aV71qlflPe95T3fbuHHj8tGPfnSDz1z/PiuVSv7617/mrW99a6rVau6+++4N+p9yyimF7g8AoCjBGgBAL/va176Wm266KVdffXWOOOKI/PWvf93ohgHf/va3s/fee2fcuHHZZpttsu222+bHP/5xVq1atdnPb21tzbnnnpupU6dm7NixeeUrX5ltt902zz777Eu+t4impqacccYZ+dGPfrTRQCvpCgcXLlyYXXfdtUdN9913X4+aHn/88ey8884plUo93r/LLrts8JnLli3LSSedlK233joNDQ3Zdttt87a3vS1JNrjP+vr6HsElAEB/EKwBAPSyf/iHf8ihhx6aY489Nj/60Y+y55575kMf+lBaWlq6+1x++eU56aSTsvPOO+eSSy7JT3/609x000055JBD0tnZudnPP/300/PZz342/+N//I/8n//zf/Kzn/0sN910U7bZZpuXfG9Rn/jEJzJx4sRNjlr73Oc+l7lz5+aggw7K5ZdfnhtvvDE33XRT9thjj0I1dXR05J3vfGd+/OMf56yzzsp1112Xm266KZdeemmSbPCZ64+YAwDoL9ZYAwDoQ6NGjcqCBQty8MEH56tf/Wr+5//8n0mSq6++Oq997WtzzTXX9Bi9dd55573kZ1599dU58cQT87//9//ublu9enWeffbZXq9/nXWj1j71qU/lxBNP3GhNBx988AZrwz377LN55Stf2X3+6le/Og899FCq1WqP+/7Tn/7U4333339//vjHP+bb3/52TjjhhO72m266qbduCQDgZfPXegAAfeztb397/uEf/iFf+tKXsnr16iRdgVuSVKvV7n533HFHli5d+pKfN2rUqB7vS5KvfOUr6ejo6MWqN3TGGWdk4sSJOf/887eopquuuipPPvlkj7aZM2fmySef7LHD6OrVq/Of//mfG3xe0vPXp1qt5qKLLnrZ9wEA0FuMWAMA6Aef/OQn8/73vz+XXnppTj755Lz73e/ONddck2OOOSZHHnlkHn300Vx88cWZNm1ajymjG/Pud7873/nOd9LU1JRp06Zl6dKl+fnPf55tttmmT++hqakpn/jEJzY6HfTd7353zj///PzzP/9z3vrWt+b+++/PFVdckde+9rU9+v3Lv/xLvvrVr+aDH/xgPvGJT2S77bbLFVdckXHjxiVJ9yi23XffPTvvvHPOPPPMPPnkk2lsbMwPfvCD/L//9//69B4BAGphxBoAQD+YNWtWdt5551xwwQXp6OjISSedlM997nO599578/GPfzw33nhjLr/88uy3334v+VkXXXRRTjjhhFxxxRX513/91zz99NP5+c9/noaGhj6/jzPOOCNNTU0btP/7v/97/vVf/zU33nhjPvGJT+R3v/tdfvzjH2fq1Kk9+jU0NOSXv/xlDjnkkFx00UX5zGc+kwMPPDDnnHNOknQHbKNHj87111+fN7zhDVmwYEHmz5+fXXfdNZdddlmf3yMAwJYqVV88Zh8AgEGjo6Mj9fX1+fSnP52zzz67pvf+4he/yKGHHppbb701BxxwQB9V2Du+9KUvZc6cOfnLX/6SV73qVQNdDgDAFjFiDQBgEHv66aeTpMcGAP3x3r7U2tra43z16tX5+te/nl133VWoBgAMKdZYAwAYpK6++upcdtllKZVKOfjgg7f4fZVKJVdccUUuuuii7LDDDnnd617Xh1XWbtasWdlxxx3zhje8IatWrcrll1+eP/zhD7niiisGujQAgJoI1gAABql/+7d/S6lUyiWXXJLddttti9/3zDPP5PTTT89ee+2Vb33rW6mrG1yTFGbOnJlvfvObueKKK9LR0ZFp06ble9/7Xv7xH/9xoEsDAKiJNdYAAAAAoIDB9deXAAAAADBECNYAAAAAoABrrCXp7OzMU089la222iqlUmmgywEAAABggFSr1Tz33HPZfvvtX3KtWsFakqeeeipTp04d6DIAAAAAGCSeeOKJ7LDDDpvtI1hLstVWWyXp+gVrbGwc4GoAAAAAGCjNzc2ZOnVqd160OYK1pHv6Z2Njo2ANAAAAgC1aLszmBQAAAABQgGANAAAAAAoQrAEAAABAAdZY20IdHR1pb28f6DKGtFGjRqW+vn6L5igDAAAADHaCtS3Q0tKSv/zlL6lWqwNdypA3YcKEbLfddhkzZsxAlwIAAADwsgjWXkJHR0f+8pe/ZMKECdl2222NtiqoWq2mra0tzzzzTB599NHsuuuuqaszExkAAAAYugRrL6G9vT3VajXbbrttxo8fP9DlDGnjx4/P6NGj8/jjj6etrS3jxo0b6JIAAAAACjNkaAsZqdY7jFIDAAAAhgspBwAAAAAUIFgDAAAAgAIEayNYqVTKddddt8nrjz32WEqlUu65555N9rn00kszceLEXq8NAAAAYLATrPWj1tZkxYqun31twYIFefOb35ytttoqkyZNynvf+948/PDDff/FAAAAACOEYK0fLFmSzJqVNDQkU6Z0/Zw1K7nttr77zltuuSWzZ8/O7bffnptuuint7e057LDDUqlU+u5LAQAAAEYQwVofW7w4Oeig5Prrk87OrrbOzq7zAw9MLr64b773pz/9aU466aTsscce2WeffXLppZdm2bJlueuuu3r0e/rpp3P44Ydn/Pjxee1rX5urr756g8/685//nIMPPjgTJkzIPvvsk6VLl27ye5955pnst99+OeaYY7JmzZpevy8AAACAwUKw1oeWLElmz06q1WTt2p7X1q7taj/11L4dubbOqlWrkiRbb711j/Zzzjknxx57bO69994cd9xx+cAHPpDf//73Pfr8x3/8R84888zcc889ed3rXpcPfvCDWfviG0ryxBNP5MADD8yee+6Zq6++OmPHju27GwIAAAAYYIK1PnThhcmoUZvvM2pUsnBh39bR2dmZM844IzNmzMiee+7Z49r73//+fOQjH8nrXve6fPrTn85+++2Xr3zlKz36nHnmmTnyyCPzute9LvPnz8/jjz+eP/3pTz36PPzww5kxY0ZmzpyZb33rWxn1UjcOAAAAMMQJ1vpIa2vywx9uOFLtxdauTa69tm83NJg9e3YeeOCBfO9739vg2vTp0zc4f/GItb333rv7eLvttkuSrFy5sruttbU1Bx54YGbNmpWLLroopVKpN8sHAAAAGJQEa32kufmFNdVeSmdnV/++cNppp+WGG27Ir371q+ywww6FPmP06NHdx+tCs871bm7s2LE59NBDc8MNN+TJJ598eQUDAAAADBGCtT7S2JjUbeGvbl1dV//eVK1Wc9ppp+Xaa6/NL3/5y+y0004b7Xf77bdvcP7617++pu+qq6vLd77zney77745+OCD89RTTxWuGwAAAGCoEKz1kfHjk6OPTurrN9+vvj455piu/r1p9uzZufzyy3PllVdmq622yvLly7N8+fK0vmjO6VVXXZX/7//7//LHP/4x5513Xu68886cdtppNX/fqFGjcsUVV2SfffbJIYcckuXLl/fWrQAAAAAMSoK1PjR3btLRsfk+HR3JnDm9/92LFy/OqlWr8va3vz3bbbdd9+v73/9+j37z58/P9773vey999657LLL8t3vfjfTpk0r9J319fX57ne/mz322COHHHJIj3XYAAAAAIabUrVarQ50EQOtubk5TU1NWbVqVRpfNCdz9erVefTRR7PTTjtl3LhxNX/2xRcnp57atfvn+hsZ1Nd3hWqLFiUnn/xy72DoeLm/ngAAAAB9aXM50YsZsdbHTj45ufXWrmmh69Zcq6vrOr/11pEVqgEAAAAMJy+xAhi9YcaMrldra9fun42Nvb+mGgAAAAD9S7DWj8aPF6gBAAAADBemggIAAABAAYI1AAAAAChAsAYAAAAwQlTaKinNL6U0v5RKW2WgyxnyBGsAAAAAUIBgDQAAAAAKsCsoAAAAwDC2/pTPSvvGj5OkPKbcbzUNF0asjVClUinXXXfdJq8/9thjKZVKueeeezbZ59JLL83EiRN7vTYAAACg9zQsaOh+Tb5gcnf75Asm97hG7QRrw9TixYuz9957p7GxMY2NjZk+fXp+8pOfDHRZAAAAAMOGqaD9pNJW6U5/W+a19Pnwyh122CGf//zns+uuu6Zarebb3/52jj766Nx9993ZY489+vS7AQAAgMGjZV5L93GlvdI9am3FmStSHm3658thxNowddRRR+WII47Irrvumte97nX57Gc/m4aGhtx+++3dfZ5++ukcfvjhGT9+fF772tfm6quv3uBz/vznP+fggw/OhAkTss8++2Tp0qWb/M5nnnkm++23X4455pisWbOmT+4LAAAAqE15TPmF13pBWnl0ucc1aidYGwE6Ojryve99L5VKJdOnT+9uP+ecc3Lsscfm3nvvzXHHHZcPfOAD+f3vf9/jvf/xH/+RM888M/fcc09e97rX5YMf/GDWrl27wXc88cQTOfDAA7Pnnnvm6quvztixY/v8vgAAAAAGkmCtD1XaKi+8XrTrxvrX+sr999+fhoaGjB07NieffHKuvfbaTJs2rfv6+9///nzkIx/J6173unz605/Ofvvtl6985Ss9PuPMM8/MkUcemde97nWZP39+Hn/88fzpT3/q0efhhx/OjBkzMnPmzHzrW9/KqFGj+uyeAAAAAAYLa6z1oU3tqLH+DhxJUj2v2iffv9tuu+Wee+7JqlWrcvXVV+fEE0/MLbfc0h2urT96bd35i3cB3XvvvbuPt9tuuyTJypUrs/vuuydJWltbc+CBB+ZDH/pQvvSlL/XJfQAAAAC9ozym3Gc5xEhkxNowNmbMmOyyyy7Zd999s2DBguyzzz656KKLavqM0aNHdx+XSqUkSWdnZ3fb2LFjc+ihh+aGG27Ik08+2TuFAwAAAAwBRqz1ocG260ZnZ2ePTQVuv/32nHDCCT3O3/jGN9b0mXV1dfnOd76TD33oQzn44INz8803Z/vtt++1mgEAAAAGK8FaH9rUjhrrdt3oS/Pmzcvhhx+eHXfcMc8991yuvPLK3Hzzzbnxxhu7+1x11VXZb7/9csABB+SKK67InXfemUsuuaTm7xo1alSuuOKKfPCDH8whhxySm2++OVOmTOnN2wEAAAAYdEwFHaZWrlyZE044Ibvttlve8Y535Le//W1uvPHGvPOd7+zuM3/+/Hzve9/L3nvvncsuuyzf/e53e2xuUIv6+vp897vfzR577JFDDjkkK1eu7K1bAQAAABiUStVqdcSvWNfc3JympqasWrUqjY2NPa6tXr06jz76aHbaaaeMGzeu8HdU2irdmxm0zGvp8xFrg1Vv/XoCAAAA9IXN5UQvZipoP7HrBgAAAMDwYiooAAAAABQgWAMAAACAAgRrAAAAAFCAYG0L2eOhd/h1BAAAAIYLwdpLGDVqVJKkra1tgCsZHp5//vkkyejRowe4EgAAAICXx66gL6G+vj4TJkzIM888k9GjR6euThZZRLVazfPPP5+VK1dm4sSJ3YElAAAAwFAlWHsJpVIp2223XR599NE8/vjjA13OkDdx4sRMmTJloMsAAACgl1TaKmlY0JAkaZnXkvKY8gBXBP1HsLYFxowZk1133dV00Jdp9OjRRqoBAAAAw4ZgbQvV1dVl3LhxA10GAAAAAIOEYA0AAACoSaWt8sJx+8aPk5gWyrAnWAMAAABqsm5NtRebfMHkHufV86r9UQ4MGFtcAgAAAEABRqwBAAAANWmZ19J9XGmvdI9UW3HmipRHm/7JyCFYAwAAAGqyqbXTyqPL1lVjRDEVFAAAAAAKEKwBAAAAQAGmggIAAACFlceU7f7JiGXEGgAAAAAUIFgDAAAACqu0VVKaX0ppfimVtspAlwP9SrAGAAAAAAUI1gAAAACgAJsXAAAAADVZf8pnpX3jx0nXxgYwnAnWAAAAgJo0LGjYaPvkCyb3OLdbKMOdqaAAAAAAUIARawAAAEBNWua1dB9X2ivdI9VWnLki5dGmfzJyCNYAAACAmmxq7bTy6LJ11RhRTAUFAAAAgAIEawAAAABQgKmgAAAAQGHlMWW7fzJiGbEGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAACAmlXaKinNL6U0v5RKW2WgyxkQgjUAAAAAKECwBgAAAAAF1A90AQAAAAAMDetP+ay0b/w4Scpjyv1W00ASrAEAAACwRRoWNGy0ffIFk3ucV8+r9kc5A85UUAAAAAAowIg1AAAAALZIy7yW7uNKe6V7pNqKM1ekPHpkTP9cn2ANAAAAGBEqbZXuqYwt81pGzDpgvWlTv2bl0eUR+etpKigAAAAAFDBogrXPf/7zKZVKOeOMM7rbVq9endmzZ2ebbbZJQ0NDjj322KxYsaLH+5YtW5YjjzwyEyZMyKRJk/LJT34ya9eu7efqAQAAABhpBsVU0N/+9rf5+te/nr333rtH+5w5c/LjH/84V111VZqamnLaaadl1qxZue2225IkHR0dOfLIIzNlypT85je/ydNPP50TTjgho0ePzuc+97mBuBUAAACgoL6Yqllpq7xw3L7x42TTUxzZtPKY8ojZ/XNTBjxYa2lpyXHHHZf//M//zGc+85nu9lWrVuWSSy7JlVdemUMOOSRJ8q1vfSuvf/3rc/vtt2f//ffPz372szz00EP5+c9/nsmTJ+cNb3hDPv3pT+ess87Kpz71qYwZM2aj37lmzZqsWbOm+7y5ublvbxIAAAAYEOuCuhdbt+j+OiM9IKKYAZ8KOnv27Bx55JE59NBDe7TfddddaW9v79G+++67Z8cdd8zSpUuTJEuXLs1ee+2VyZNf+Idh5syZaW5uzoMPPrjJ71ywYEGampq6X1OnTu3luwIAAABguBvQEWvf+9738rvf/S6//e1vN7i2fPnyjBkzJhMnTuzRPnny5Cxfvry7z/qh2rrr665tyrx58zJ37tzu8+bmZuEaAAAADIC+nqrZMq+lx2euG6m24swVKY82/ZOXZ8CCtSeeeCKf+MQnctNNN2XcuHH9+t1jx47N2LFj+/U7AQAAgA319VTNTQVy5dFl66rxsg3YVNC77rorK1euzJve9KbU19envr4+t9xyS7785S+nvr4+kydPTltbW5599tke71uxYkWmTJmSJJkyZcoGu4SuO1/XBwAAAAD6woCNWHvHO96R+++/v0fbP//zP2f33XfPWWedlalTp2b06NH5xS9+kWOPPTZJ8vDDD2fZsmWZPn16kmT69On57Gc/m5UrV2bSpElJkptuuimNjY2ZNm1a/94QAAAAUDNTNRnKBixY22qrrbLnnnv2aCuXy9lmm2262z/84Q9n7ty52XrrrdPY2JjTTz8906dPz/77758kOeywwzJt2rQcf/zx+eIXv5jly5fn7LPPzuzZs031BAAAgCGgP6dqlseU7f5JrxrQzQteysKFC1NXV5djjz02a9asycyZM7No0aLu66NGjcoNN9yQU045JdOnT0+5XM6JJ56Y888/fwCrBgAAAGAkKFWr1REf1TY3N6epqSmrVq1KY2PjQJcDAAAAw16lrdK9cUHLvJaUx5Q32gb9rZacaFCPWAMAAABGDlM1GWoGbFdQAAAAABjKjFgDAAAA+kWlrfLCcfvGj5NNb2gAg41gDQAAAOgX69ZPe7HJF0zucW46KEOFqaAAAAAAUIARawAAAEC/aJnX0n1caa90j1RbceaKlEeb/snQI1gDAAAA+sWm1k4rjy5bV40hyVRQAAAAAChAsAYAAAAABZgKCgAAAPS78piy3T8Z8oxYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAYgSptlZTml1KaX0qlrTLQ5cCQJFgDAAAAgAIEawAAAABQQP1AFwAAAAD0j/WnfFbaN36cJOUx5X6rCYYywRoAAACMEA0LGjbaPvmCyT3Oq+dV+6McGPJMBQUAAACAAoxYAwAAgBGiZV5L93GlvdI9Um3FmStSHm36J9RKsAYAAAAjxKbWTiuPLhdeV63SVumeYtoyr8X6bIwopoICAAAAQAGCNQAAAAAowFRQAAAAGIHKY8qFd/+stFVeOG7f+PG674DhTLAGAAAA1GTdmmovtm4zhHWKBncwVJgKCgAAAAAFGLEGAAAA1KRlXkv3caW90j1SbcWZK1IebfonI4dgDQAAAGpQaat0T4VsmdcyItcR29Q9l0eXR+SvByOXqaAAAAAAUIBgDQAAAAAKMBUUAAAAXkKlrfLCcfvGj5NNT5Eczspjynb/ZMQSrAEAAMBLWLem2outW7R/HQETjCymggIAAABAAUasAQAAwEtomdfSfVxpr3SPVFtx5oqUR4+86Z9AF8EaAAAAvIRNrZ1WHl0ekeuqAV1MBQUAAACAAgRrAAAAAFCAqaAAAABQg/KYst0/gSRGrAEAAABAIYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAA9LtKWyWl+aWU5pdSaasMdDlQiGANAAAAAAoQrAEAAABAAfUDXQAAAAAwMqw/5bPSvvHjJCmPKfdbTfByCNYAAACAftGwoGGj7ZMvmNzjvHpetT/KgZfNVFAAAAAAKMCINQAAAKBftMxr6T6utFe6R6qtOHNFyqNN/2ToEawBAAAA/WJTa6eVR5etq8aQZCooAAAAABQgWAMAAACAAkwFBQAAAPpdeUzZ7p8MeUasAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAABgEKm2VlOaXUppfSqWtMtDlsAUGNFhbvHhx9t577zQ2NqaxsTHTp0/PT37yk+7rb3/721MqlXq8Tj755B6fsWzZshx55JGZMGFCJk2alE9+8pNZu3Ztf98KAAAAACNM/UB++Q477JDPf/7z2XXXXVOtVvPtb387Rx99dO6+++7sscceSZKPfvSjOf/887vfM2HChO7jjo6OHHnkkZkyZUp+85vf5Omnn84JJ5yQ0aNH53Of+1y/3w8AAAAAI8eABmtHHXVUj/PPfvazWbx4cW6//fbuYG3ChAmZMmXKRt//s5/9LA899FB+/vOfZ/LkyXnDG96QT3/60znrrLPyqU99KmPGjOnzewAAAAAoav0pn5X2jR8nSXlMud9qYssNaLC2vo6Ojlx11VWpVCqZPn16d/sVV1yRyy+/PFOmTMlRRx2Vc845p3vU2tKlS7PXXntl8uTJ3f1nzpyZU045JQ8++GDe+MY3bvS71qxZkzVr1nSfNzc399FdAQAAAGxaw4KGjbZPvmByj/PqedX+KIcaDXiwdv/992f69OlZvXp1Ghoacu2112batGlJkg996EN59atfne233z733XdfzjrrrDz88MO55pprkiTLly/vEaol6T5fvnz5Jr9zwYIFmT9/fh/dEQAAAAAjwYAHa7vttlvuueeerFq1KldffXVOPPHE3HLLLZk2bVo+9rGPdffba6+9st122+Ud73hHHnnkkey8886Fv3PevHmZO3du93lzc3OmTp36su4DAAAAoFYt81q6jyvtle6RaivOXJHyaNM/B7sBD9bGjBmTXXbZJUmy77775re//W0uuuiifP3rX9+g71ve8pYkyZ/+9KfsvPPOmTJlSu68884efVasWJEkm1yXLUnGjh2bsWPH9tYtAAAAABSyqbXTyqPL1lUbAuoGuoAX6+zs7LH+2fruueeeJMl2222XJJk+fXruv//+rFy5srvPTTfdlMbGxu7ppAAAAADQFwZ0xNq8efNy+OGHZ8cdd8xzzz2XK6+8MjfffHNuvPHGPPLII7nyyitzxBFHZJtttsl9992XOXPm5KCDDsree++dJDnssMMybdq0HH/88fniF7+Y5cuX5+yzz87s2bONSAMAAACgTw1osLZy5cqccMIJefrpp9PU1JS99947N954Y975znfmiSeeyM9//vN86UtfSqVSydSpU3Psscfm7LPP7n7/qFGjcsMNN+SUU07J9OnTUy6Xc+KJJ+b8888fwLsCAAAAqF15TNnun0NMqVqtjvjfsebm5jQ1NWXVqlVpbGwc6HIAAAAAGCC15ESDbo01AAAAABgKBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAwAhUaaukNL+U0vxSKm2VgS6H+D0ZigRrAAAAAFCAYA0AAAAACqgf6AIAAACA/rH+9MJK+8aPk6Q8ptxvNY10fk+GtlK1Wq0OdBEDrbm5OU1NTVm1alUaGxsHuhwAAADoE6X5pS3qVz1vxEcF/cbvyeBTS05kKigAAAAAFGAqKAAAAIwQLfNauo8r7ZVMvmBykmTFmStSHm2q4UDwezK0CdYAAABghNjUOl3l0WVreA0QvydDm6mgAAAAAFCAYA0AAAAACjAVFAAAAEag8piynSYHGb8nQ48RawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAoCaVtkpK80spzS+l0lYZ6HJgwAjWAAAAAKAAwRoAAAAAFFA/0AUAAAAAg9/6Uz4r7Rs/TpLymHK/1QQDTbAGAAAAvKSGBQ0bbZ98weQe59Xzqv1RDgwKpoICAAAAQAFGrAEAAAAvqWVeS/dxpb3SPVJtxZkrUh5t+icjk2ANAAAAeEmbWjutPLpsXTVGLFNBAQAAAKAAwRoAAAAAFGAqKAAAAFCT8pjykN79s9JW6d7ltGVei6msFGbEGgAAAAAUIFgDAAAAgAJMBQUAAACGvUpb5YXj9o0fJ5ve/RQ2RrAGAAAADHvr1lR7sckXTO5xPpTXjqP/mQoKAAAAAAUYsQYAAAAMey3zWrqPK+2V7pFqK85ckfJo0z8pRrAGAAAADHubWjutPLpsXTUKMxUUAAAAAAoQrAEAAABAAaaCAgAAACNKeUzZ7p/0CiPWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAABGoEpbJaX5pZTml1Jpqwx0OTAkCdYAAAAAoADBGgAAAAAUUD/QBQAAAAD9Y/0pn5X2jR8nSXlMud9qgqFMsAYAAAAjRMOCho22T75gco/z6nnV/igHhjxTQQEAAACgACPWAAAAYIRomdfSfVxpr3SPVFtx5oqUR5v+CbUSrAEAAMAIsam108qjy9ZVgwJMBQUAAACAAgRrAAAAAFCAqaAAAAAwApXHlO3+CS+TEWsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKqDlYu+yyy7JmzZoN2tva2nLZZZf1SlEAAAAAMNiVqtVqtZY3jBo1Kk8//XQmTZrUo/1vf/tbJk2alI6Ojl4tsD80Nzenqakpq1atSmNj40CXAwAAAMAAqSUnqnnEWrVaTalU2qD9L3/5S5qammr9OAAAAAAYkuq3tOMb3/jGlEqllEqlvOMd70h9/Qtv7ejoyKOPPpp3vetdfVIkAAAAAAw2Wxysvfe9702S3HPPPZk5c2YaGhq6r40ZMyavec1rcuyxx/Z6gQAAAAAwGG1xsHbeeeclSV7zmtfkH//xHzNu3Lg+KwoAAAAABrua11g78cQTs3r16nzzm9/MvHnz8ve//z1J8rvf/S5PPvlkrxcIAAAAAIPRFo9YW+e+++7LoYcemqampjz22GP56Ec/mq233jrXXHNNli1blssuu6wv6gQAAACAQaXmEWtz5szJSSedlP/6r//qMR30iCOOyK9//eteLQ4AAAAABquaR6z93//7f/ONb3xjg/ZXvepVWb58ea8UBQAAAACDXc0j1saOHZvm5uYN2v/4xz9m22237ZWiAAAAAGCwqzlYe8973pPzzz8/7e3tSZJSqZRly5blrLPOyrHHHlvTZy1evDh77713Ghsb09jYmOnTp+cnP/lJ9/XVq1dn9uzZ2WabbdLQ0JBjjz02K1as6PEZy5Yty5FHHpkJEyZk0qRJ+eQnP5m1a9fWelsAAAAAUJOag7X//b//d1paWjJp0qS0trbmbW97W3bZZZdstdVW+exnP1vTZ+2www75/Oc/n7vuuiv/9//+3xxyyCE5+uij8+CDDybpWs/t+uuvz1VXXZVbbrklTz31VGbNmtX9/o6Ojhx55JFpa2vLb37zm3z729/OpZdemnPPPbfW2wIAAACAmpSq1Wq1yBuXLFmS++67Ly0tLXnTm96UQw89tFcK2nrrrfO//tf/yvve975su+22ufLKK/O+970vSfKHP/whr3/967N06dLsv//++clPfpJ3v/vdeeqppzJ58uQkycUXX5yzzjorzzzzTMaMGbNF39nc3JympqasWrUqjY2NvXIfAAAAAAw9teRENW9esM4BBxyQ/fbbL2PHjk2pVCr6Md06Ojpy1VVXpVKpZPr06bnrrrvS3t7eI7Dbfffds+OOO3YHa0uXLs1ee+3VHaolycyZM3PKKafkwQcfzBvf+MaNfteaNWuyZs2a7vONrRkHAAAAAJtT81TQzs7OfPrTn86rXvWqNDQ05NFHH02SnHPOObnkkktqLuD+++9PQ0NDxo4dm5NPPjnXXnttpk2bluXLl2fMmDGZOHFij/6TJ0/u3n10+fLlPUK1ddfXXduUBQsWpKmpqfs1derUmusGAAAAYGSrOVj7zGc+k0svvTRf/OIXe0y13HPPPfPNb36z5gJ222233HPPPbnjjjtyyimn5MQTT8xDDz1U8+fUYt68eVm1alX364knnujT7wMAAABg+Kk5WLvsssvyjW98I8cdd1xGjRrV3b7PPvvkD3/4Q80FjBkzJrvsskv23XffLFiwIPvss08uuuiiTJkyJW1tbXn22Wd79F+xYkWmTJmSJJkyZcoGu4SuO1/XZ2PGjh3bvRPpuhcAAAAA1KLmYO3JJ5/MLrvsskF7Z2dn2tvbX3ZBnZ2dWbNmTfbdd9+MHj06v/jFL7qvPfzww1m2bFmmT5+eJJk+fXruv//+rFy5srvPTTfdlMbGxkybNu1l1wIAAAAAm1Lz5gXTpk3Lrbfemle/+tU92q+++upNbhawKfPmzcvhhx+eHXfcMc8991yuvPLK3HzzzbnxxhvT1NSUD3/4w5k7d2623nrrNDY25vTTT8/06dOz//77J0kOO+ywTJs2Lccff3y++MUvZvny5Tn77LMze/bsjB07ttZbAwAAAIAtVnOwdu655+bEE0/Mk08+mc7OzlxzzTV5+OGHc9lll+WGG26o6bNWrlyZE044IU8//XSampqy995758Ybb8w73/nOJMnChQtTV1eXY489NmvWrMnMmTOzaNGi7vePGjUqN9xwQ0455ZRMnz495XI5J554Ys4///xabwsAAAAAalKqVqvVWt9066235vzzz8+9996blpaWvOlNb8q5556bww47rC9q7HPNzc1pamrKqlWrrLcGAAAAMILVkhNt0Yi1L3/5y/nYxz6WcePGZdmyZTnggANy00039UqxAAAAADAUbdHmBXPnzk1zc3OSZKeddsozzzzTp0UBAAAAwGC3RSPWtt9++/zgBz/IEUcckWq1mr/85S9ZvXr1RvvuuOOOvVogAAAAAAxGW7TG2je+8Y2cfvrpWbt27Sb7VKvVlEqldHR09GqB/cEaawAAAAAkteVEW7x5wXPPPZfHH388e++9d37+859nm2222Wi/ffbZp/aKB5hgDQAAAICkDzYvSJKtttoqe+65Z771rW9lxowZGTt27MsuFAAAAACGqi0O1tY58cQT+6IOAAAAABhStmhXUAAAAACgJ8EaAAAAABQgWAMAAACAAgoHa21tbXn44Yezdu3a3qwHAAAAAIaEmoO1559/Ph/+8IczYcKE7LHHHlm2bFmS5PTTT8/nP//5Xi8QAAAAAAajmoO1efPm5d57783NN9+ccePGdbcfeuih+f73v9+rxQEAAADAYFVf6xuuu+66fP/738/++++fUqnU3b7HHnvkkUce6dXiAAAAAGCwqnnE2jPPPJNJkyZt0F6pVHoEbQAAAAAwnNUcrO2333758Y9/3H2+Lkz75je/menTp/deZQAAAAAwiNU8FfRzn/tcDj/88Dz00ENZu3ZtLrroojz00EP5zW9+k1tuuaUvagQAhrDW1qS5OWlsTMaPH+hqAACg99Q8Yu2AAw7Ivffem7Vr12avvfbKz372s0yaNClLly7Nvvvu2xc1AgBD0JIlyaxZSUNDMmVK189Zs5LbbhvoygAAoHeUqtVqdUs7t7e351/+5V9yzjnnZKeddurLuvpVc3NzmpqasmrVqjQ2Ng50OQAw5C1enMyenYwalaxd+0J7fX3S0ZEsWpScfPLA1QcAAJtSS05U04i10aNH5wc/+MHLKg4AGN6WLOkK1arVnqFa0nVerSannmrkGgAAQ1/NU0Hf+9735rrrruuDUgCA4eDCC7tGqm3OqFHJwoX9Uw8AAPSVmjcv2HXXXXP++efntttuy7777ptyudzj+sc//vFeKw4AGFpaW5Mf/jDp7Nx8v7Vrk2uv7epvQwMAAIaqmtZYS7LZtdVKpVL+/Oc/v+yi+ps11gCgd6xY0bVRwZZavjyZPLnv6gEAgFrVkhPVPGLt0UcfLVwYADC8NTYmdXUvPWIt6ern77MAABjKal5jDQBgU8aPT44+umv3z82pr0+OOcY0UAAAhraaR6wlyV/+8pf86Ec/yrJly9LW1tbj2oUXXtgrhQEAQ9PcuclL7XPU0ZHMmdMv5QAAQJ+pOVj7xS9+kfe85z157Wtfmz/84Q/Zc88989hjj6VareZNb3pTX9QIAAwhBxyQLFqUnHpq1+6fa9e+cK2+vitUW7QomTFj4GoEAIDeUPNU0Hnz5uXMM8/M/fffn3HjxuUHP/hBnnjiibztbW/L+9///r6oEQAYYk4+Obn11q5poXX//V8bdXVd57fe2nUdAACGupp3Bd1qq61yzz33ZOedd84rXvGKLFmyJHvssUfuvffeHH300Xnsscf6qNS+Y1dQAOg7ra1Jc3PXRgXWVAMAYLCrJSeqecRauVzuXldtu+22yyOPPNJ97a9//WutHwcADHPjxyeTJwvVAAAYfmpeY23//ffPkiVL8vrXvz5HHHFE/vVf/zX3339/rrnmmuy///59USMAAAAADDo1B2sXXnhhWlpakiTz589PS0tLvv/972fXXXe1IygAAAAAI0bNa6wNR9ZYAwAAACDp4zXWAAAAAIACU0Hr6upSKpU2eb2jo+NlFQQAAAAAQ0HNwdq1117b47y9vT133313vv3tb2f+/Pm9VhgAAAAADGa9tsbalVdeme9///v54Q9/2Bsf16+ssQYAAABAMkBrrO2///75xS9+0VsfBwAAAACDWq8Ea62trfnyl7+cV73qVb3xcQAAAAAw6NW8xtorXvGKHpsXVKvVPPfcc5kwYUIuv/zyXi0OAAAAAAarmoO1hQsX9gjW6urqsu222+Ytb3lLXvGKV/RqcQAAAAAwWNUcrJ100kl9UAYAAAAADC01B2v33XffFvfde++9a/14AAAAABgSag7W3vCGN/SYCrox1Wo1pVIpHR0dhQsDAAAAgMGs5l1Br7nmmuy0005ZtGhR7r777tx9991ZtGhRdt555/zgBz/In//85zz66KP585//3Bf1AgAAAMCgUPOItc997nP58pe/nCOOOKK7be+9987UqVNzzjnn5K677urVAgEAAABgMKp5xNr999+fnXbaaYP2nXbaKQ899FCvFAUADB+trcmKFV0/AQBgOKk5WHv961+fBQsWpK2trbutra0tCxYsyOtf//peLQ4AGLqWLElmzUoaGpIpU7p+zpqV3HbbQFcGAAC9o1StVqu1vOHOO+/MUUcdlWq12r3r53333ZdSqZTrr78+//AP/9Anhfal5ubmNDU1ZdWqVWlsbBzocgBgyFu8OJk9Oxk1Klm79oX2+vqkoyNZtCg5+eSBqw8AADallpyo5mAtSSqVSq644or84Q9/SNI1iu1DH/pQyuVysYoHmGANAHrPkiXJQQclm/svjFIpufXWZMaM/qsLAAC2RC05Uc2bFyRJuVzOxz72sULFAQDD24UXbjhS7cVGjUoWLhSsAQAwtNW8xtq3v/3t/PjHP+4+/7d/+7dMnDgxb33rW/P444/3anEAwNDS2pr88IebD9WSruvXXmtDAwAAhraag7XPfe5zGT9+fJJk6dKl+epXv5ovfvGLeeUrX5k5c+b0eoEAwNDR3Jx0dm5Z387Orv4AADBU1TwV9Iknnsguu+ySJLnuuuvyvve9Lx/72McyY8aMvP3tb+/t+gCAIaSxMamr27Jwra6uqz8AAAxVNY9Ya2hoyN/+9rckyc9+9rO8853vTJKMGzcureZzAMCINn58cvTRXbt/bk59fXLMMV39AQBgqKo5WHvnO9+Zj3zkI/nIRz6SP/7xjzniiCOSJA8++GBe85rX9HZ9AMAQM3du0tGx+T4dHYkVJAAAGOpqDta+9rWvZfr06XnmmWfygx/8INtss02S5K677soHP/jBXi8QABhaDjggWbQoKZU2HLlWX9/VvmiRHUEBABj6StVqtTrQRQy05ubmNDU1ZdWqVWm02AsA9IrbbksWLuza/bOzs2tNtWOO6RqpJlQDAGCwqiUnqnnzAgCALTFjRtertbVr98/GRmuqAQAwvAjWAIA+NX68QA0AgOGp5jXWAAAAAIAag7VqtZply5Zl9erVfVUPAAAAAAwJNQdru+yyS5544om+qgcAAAAAhoSagrW6urrsuuuu+dvf/tZX9QAAAADAkFDzGmuf//zn88lPfjIPPPBAX9QDAMNepa2S0vxSSvNLqbRVBrocAACgoJp3BT3hhBPy/PPPZ5999smYMWMy/kXbfP3973/vteIAAAAAYLCqOVj70pe+1AdlAAAAAMDQUnOwduKJJ/ZFHQAwrK0/5bPSvvHjJCmPKfdbTQAAwMtTc7CWJI888ki+9a1v5ZFHHslFF12USZMm5Sc/+Ul23HHH7LHHHr1dIwAMeQ0LGjbaPvmCyT3Oq+dV+6McAACgF9S8ecEtt9ySvfbaK3fccUeuueaatLS0JEnuvffenHfeeb1eIAAAAAAMRjWPWPuf//N/5jOf+Uzmzp2brbbaqrv9kEMOyVe/+tVeLQ4AhouWeS3dx5X2SvdItRVnrkh5tOmfAAAwFNUcrN1///258sorN2ifNGlS/vrXv/ZKUQAw3Gxq7bTy6LJ11QAAYIiqeSroxIkT8/TTT2/Qfvfdd+dVr3pVrxQFAAAAAINdzcHaBz7wgZx11llZvnx5SqVSOjs7c9ttt+XMM8/MCSec0Bc1AgAAAMCgU6pWqzVtP9bW1pbZs2fn0ksvTUdHR+rr69PR0ZEPfehDufTSSzNq1Ki+qrXPNDc3p6mpKatWrUpjY+NAlwMAAADAAKklJ6o5WFtn2bJleeCBB9LS0pI3vvGN2XXXXQsVOxgI1gAAAABIasuJat68YJ0dd9wxU6dOTZKUSqWiHwMAAAAAQ1LNa6wlySWXXJI999wz48aNy7hx47Lnnnvmm9/8Zm/XBgDQa1pbkxUrun4CAEBvqDlYO/fcc/OJT3wiRx11VK666qpcddVVOeqoozJnzpyce+65fVEjAAwrlbZKSvNLKc0vpdJWGehyhr0lS5JZs5KGhmTKlK6fs2Ylt9020JUBADDU1bzG2rbbbpsvf/nL+eAHP9ij/bvf/W5OP/30/PWvf+3VAvuDNdYA6E+VtkoaFjQkSVrmtaQ8pjzAFQ1fixcns2cno0Yla9e+0F5fn3R0JIsWJSefPHD1AQAw+NSSE9U8Yq29vT377bffBu377rtv1q7/X6wAAANoyZKuUK1a7RmqJV3n1Wpy6qlGrgEAUFzNwdrxxx+fxYsXb9D+jW98I8cdd1yvFAUAw02lrfLCq/2F6Z+V9kqPa/SeCy/sGqm2OaNGJQsX9k89AAAMPzVPBT399NNz2WWXZerUqdl///2TJHfccUeWLVuWE044IaNHj+7ue+GFF/ZutX3EVFAA+lpp/pbtoF09r6Z/LbMJra1da6l1dr5037q6pKUlGT++977fdF8AgKGrlpyovtYPf+CBB/KmN70pSfLII48kSV75ylfmla98ZR544IHufqXSlv0PBABAb2tu3rJQLenq19zcu8EaAAAjQ83B2q9+9au+qAMAhrWWeS3dx5X2SiZfMDlJsuLMFSmPNpqptzU2do1E29IRawasAwBQRM3BGgBQu01NBSyPLpsm2AfGj0+OPjq5/voNNy5YX319V7/eGK22/hp5L15Hb31+vwEAhg/BGgAwLM2dm1x33eb7dHQkc+b0zvetW1PtxdaNTlzHOnoAAMNHzbuCAgAMBQcckCxalJRKXSPT1ldf39W+aFEyY8bA1AcAwNBX866gw5FdQQFg+LrttmThwuTaa7vWXKurS445pmukWm+Gai+eCrqpdfRMBQUAGNz6dFdQAIChZMaMrldra9fun42NfbMDqHX0AABGnkJTQb/zne9kxowZ2X777fP4448nSb70pS/lhz/8Ya8WBwDQW8aPTyZP7ptQDQCAkanmYG3x4sWZO3dujjjiiDz77LPp6OhIkkycODFf+tKXers+AAAAABiUag7WvvKVr+Q///M/8x//8R8ZNWpUd/t+++2X+++/v1eLAwAYispjyqmeV031vKppoAAAw1jNwdqjjz6aN77xjRu0jx07NpVKZSPvAAAAAIDhp+Zgbaeddso999yzQftPf/rTvP71r++NmgAAAABg0Kt5V9C5c+dm9uzZWb16darVau68885897vfzYIFC/LNb36zL2oEAAAAgEGn5mDtIx/5SMaPH5+zzz47zz//fD70oQ9l++23z0UXXZQPfOADfVEjAAAAAAw6pWq1Wi365ueffz4tLS2ZNGlSb9bU75qbm9PU1JRVq1alsbFxoMsBAAAAYIDUkhPVPGLt0Ucfzdq1a7PrrrtmwoQJmTBhQpLkv/7rvzJ69Oi85jWvKVQ0AAAAAAwlNW9ecNJJJ+U3v/nNBu133HFHTjrppN6oCQAAAAAGvZqDtbvvvjszZszYoH3//fff6G6hAAAAADAc1RyslUqlPPfccxu0r1q1Kh0dHb1SFAAAAAAMdjUHawcddFAWLFjQI0Tr6OjIggULcsABB/RqcQAAAAAwWNW8ecEXvvCFHHTQQdltt91y4IEHJkluvfXWNDc355e//GWvFwgAw02lrZKGBQ1JkpZ5LSmPKQ9wRQAAQBE1j1ibNm1a7rvvvvyP//E/snLlyjz33HM54YQT8oc//CF77rlnX9QIAAAAAINOzSPWkmT77bfP5z73ud6uBQAAAACGjC0K1u67777sueeeqaury3333bfZvnvvvXevFAYAw0mlrfLCcfvGj5OYFjpMmO4LADAybFGw9oY3vCHLly/PpEmT8oY3vCGlUinVanWDfqVSqaadQRcsWJBrrrkmf/jDHzJ+/Pi89a1vzRe+8IXstttu3X3e/va355Zbbunxvn/5l3/JxRdf3H2+bNmynHLKKfnVr36VhoaGnHjiiVmwYEHq6wsNyAOAXrcuZHmxyRdM7nFePW/Df78CAACD0xYlT48++mi23Xbb7uPecsstt2T27Nl585vfnLVr1+bf//3fc9hhh+Whhx5KufzC3+x+9KMfzfnnn999PmHChO7jjo6OHHnkkZkyZUp+85vf5Omnn84JJ5yQ0aNHm64KAAAAQJ/ZomDt1a9+dZKkvb098+fPzznnnJOddtrpZX/5T3/60x7nl156aSZNmpS77rorBx10UHf7hAkTMmXKlI1+xs9+9rM89NBD+fnPf57JkyfnDW94Qz796U/nrLPOyqc+9amMGTPmZdcJAC9Xy7yW7uNKe6V7pNqKM1ekPNo0weHAdF8AgJGnpl1BR48enR/84Ad9VUtWrVqVJNl66617tF9xxRV55StfmT333DPz5s3L888/331t6dKl2WuvvTJ58gtTaWbOnJnm5uY8+OCDG/2eNWvWpLm5uccLAPpSeUz5hdd6QVp5dLnHNYauhgUN3a/1p/hOvmByj2sAAAwfNQVrSfLe97431113Xa8X0tnZmTPOOCMzZszInnvu2d3+oQ99KJdffnl+9atfZd68efnOd76Tf/qnf+q+vnz58h6hWpLu8+XLl2/0uxYsWJCmpqbu19SpU3v9fgAAAAAY3mpe3X/XXXfN+eefn9tuuy377rtvj7XQkuTjH/94oUJmz56dBx54IEuWLOnR/rGPfaz7eK+99sp2222Xd7zjHXnkkUey8847F/quefPmZe7cud3nzc3NwjUA4GUx3RcAYOSpOVi75JJLMnHixNx111256667elwrlUqFgrXTTjstN9xwQ379619nhx122Gzft7zlLUmSP/3pT9l5550zZcqU3HnnnT36rFixIkk2uS7b2LFjM3bs2JrrBIDeUB5TtvvnMLSpqbzrpvsCADD81Bys9eauoNVqNaeffnquvfba3HzzzVu0IcI999yTJNluu+2SJNOnT89nP/vZrFy5MpMmTUqS3HTTTWlsbMy0adN6rVYAAAAAWF/Nwdr6qtWuv20vlUqF3j979uxceeWV+eEPf5itttqqe020pqamjB8/Po888kiuvPLKHHHEEdlmm21y3333Zc6cOTnooIOy9957J0kOO+ywTJs2Lccff3y++MUvZvny5Tn77LMze/Zso9IAAAAA6DOl6rp0rAaXXHJJFi5cmP/6r/9K0rXu2hlnnJGPfOQjtX35JgK5b33rWznppJPyxBNP5J/+6Z/ywAMPpFKpZOrUqTnmmGNy9tlnp7Gxsbv/448/nlNOOSU333xzyuVyTjzxxHz+859Pff2W5YbNzc1pamrKqlWrenwuAAAAACNLLTlRzcHaueeemwsvvDCnn356pk+fniRZunRpvvrVr2bOnDk5//zzi1c+QARrAAAAACR9HKxtu+22+fKXv5wPfvCDPdq/+93v5vTTT89f//rX2iseYII1APpTpa2ShgUNSbp2krSwPQAADB615ER1tX54e3t79ttvvw3a991336xdu7bWjwMAAACAIanmYO3444/P4sWLN2j/xje+keOOO65XigIAAACAwa7QrqCXXHJJfvazn2X//fdPktxxxx1ZtmxZTjjhhMydO7e734UXXtg7VQLAEFdpq7xw3L7x4ySmhQIAwBBS8xprBx988JZ9cKmUX/7yl4WK6m/WWAOgr5Xmb3wn7BernlfzZt0AAEAvqiUnqnnE2q9+9avChQEAAADAcFFoKigAUJuWeS3dx5X2SiZfMDlJsuLMFSmPNv0TAACGIsEaAPSDTa2dVh5dtq4aAAAMUTXvCgoAAAAACNYAoN/12CG0rbKZngAAwGAmWAOAfrb+1E/TQPtPa2uyYkXXTwAA6A2CNQBgWFuyJJk1K2loSKZM6fo5a1Zy2219952VtkpK80spzS8ZlQgwDPlzHlhHsAYA/aDSVnnh1b7eVND2So9r9K7Fi5ODDkquvz7p7Oxq6+zsOj/wwOTiiwe2PgAAhja7ggJAP2hY0LDR9skXTO5xXj2v2h/ljAhLliSzZyfVarJ2bc9r685PPTXZa69kxoz+rw8AgKFPsAYADEsXXpiMGrVhqLa+UaOShQt7J1jrsSlF+8aPE+vqAQxV/pwHNqZUrVZH/F+NNzc3p6mpKatWrUpjY+NAlwPAMPTi/xhfN1JtxZkrUh5tM4Pe1tratZbauumfm1NXl7S0JOPHv7zvLM0vbVE/oxIBhiZ/zsPIUUtOZMQaAPSDTQVm5dFlYVofaG7eslAt6erX3PzygzUAAEYewRoAMOw0NnaNRNvSEWu9MWC9ZV5L9/HmRiUCMDT5cx7YGMEaADDsjB+fHH101+6fm1tjrb6+q19vjFYzKhFgePPnPLAxgjUA6GflMWXrr/SDuXOT667bfJ+OjmTOnH4pBwCAYahuoAsAAOgLBxyQLFqUlEpdI9PWV1/f1b5oUe/sCAoAwMhkV9DYFRQAhrPbbksWLkyuvbZrzbW6uuSYY7pGqgnVAAB4MbuCAgD8txkzul6trV27fzY22gEUAIDeIVgDAEaE8eMFagAA9C5rrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gCgn1XaKinNL6U0v5RKW2WgywEAAAoSrAEAAABAAYI1AOhn649SM2INAACGrvqBLgAARoIeYVp7z+P1r5XHlPu1LgAAoDjBGgD0g4YFDRttf+2XX9vjvHpetT/KAQAAeoGpoAAAAABQgGANAAAAAAoQrAEAAABAAdZYA4B+0DKvpft4ZWVl99pqf/74nzOpPGmgygIAAF4GwRoA9IP1d/sst613PLpsJ1AAABiiTAUFgH7WI2QTqgEAwJAlWIMhoLU1WbGi6ycAAAAwOAjWYBBbsiSZNStpaEimTOn6OWtWctttA10Z8HKUx5RTPa+a6nlVI9YAAGAIE6zBILV4cXLQQcn11yednV1tnZ1d5wcemFx88cDWBwAAACOdYA0GoSVLktmzk2o1Wbu257W1a7vaTz3VyDVg6Ki0VVKaX0ppfimVtspAlwMAAL1CsAaD0IUXJqNGbb7PqFHJwoX9Uw8AAACwIcEaDDKtrckPf7jhSLUXW7s2ufZaGxrAUGT0FgAADA/1A10A0FNz8wtrqr2Uzs6u/uPH921NAEWsHxpW2jd+nMQGDgAADFmCNRhkGhuTurotC9fq6rr6AwxGDQsaNto++YLJPc6r51X7oxwAAOh1poLCIDN+fHL00Un9S8Te9fXJMccYrQZDRaWt8sLrRaO31r8GAAAMHaVqtTri/5q4ubk5TU1NWbVqVRoN/2EQWLIkOeigrt0/N6VUSm69NZkxo//qAoorzS9tUb/hNHrrxVNB141UW3HmipRHvzD901RQAAAGk1pyIiPWYBA64IBk0aKu8OzFI9fq67vaFy0SqgGDW3lM+YXX+kHa6HKPawAAMFRZYw0GqZNPTvbaK1m4sGv3z87OrjXVjj46mTNHqAZDTcu8lu7jzY3eAgAAhg7BGgxiM2Z0vVpbu3b/bGy0phoMVeuPzFpZWdl9XGmrZFJ50kCUBAAAvEyCNRgCxo8XqAFDW3lMeVitHwcAAIk11gAAAACgECPWAKAfrGx5Yfrn8+3P9zhe/9qkBtNCAQBgqBCsAUA/mPy/J2+0fc/Fe/Y4N10SAACGDlNBAQAAAKAAI9YAoB+s+NcV3cfPPP9M90i1B055INtO2HagygIAAF4GwRoA9INNrZ227YRtrasGAABDlKmgAAAAAFCAYA0AAAAACjAVFAD62aSGSXb/BACAYcCINQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGr2qtTVZsaLrJwAAAMBwJlijVyxZksyalTQ0JFOmdP2cNSu57baBrgyA3lBpq6Q0v5TS/FIqbZVB81kAADCQBjRYW7BgQd785jdnq622yqRJk/Le9743Dz/8cI8+q1evzuzZs7PNNtukoaEhxx57bFasWNGjz7Jly3LkkUdmwoQJmTRpUj75yU9m7dq1/XkrI9rixclBByXXX590dna1dXZ2nR94YHLxxQNbHwAAAEBfGNBg7ZZbbsns2bNz++2356abbkp7e3sOO+ywVCov/O31nDlzcv311+eqq67KLbfckqeeeiqzZs3qvt7R0ZEjjzwybW1t+c1vfpNvf/vbufTSS3PuuecOxC2NOEuWJLNnJ9Vq8uIsc+3arvZTTzVyDQAAABh+StVqtTrQRazzzDPPZNKkSbnlllty0EEHZdWqVdl2221z5ZVX5n3ve1+S5A9/+ENe//rXZ+nSpdl///3zk5/8JO9+97vz1FNPZfLkyUmSiy++OGeddVaeeeaZjBkz5iW/t7m5OU1NTVm1alUaGxv79B6Hm1mzukambW6AYH19cvTRydVX919dALx860/TrLRXMvmCrn/PrjhzRcqjy93XymPKG7y3Lz8LAAD6Ui05UX0/1bRFVq1alSTZeuutkyR33XVX2tvbc+ihh3b32X333bPjjjt2B2tLly7NXnvt1R2qJcnMmTNzyimn5MEHH8wb3/jGDb5nzZo1WbNmTfd5c3NzX93SsNbamvzwhy9M/9yUtWuTa6/t6j9+fP/UBsDL17CgYaPt60KxdarnvfTf0fXmZwEAwGAxaDYv6OzszBlnnJEZM2Zkzz33TJIsX748Y8aMycSJE3v0nTx5cpYvX97dZ/1Qbd31ddc2ZsGCBWlqaup+TZ06tZfvZmRobn7pUG2dzs6u/gAAAADDxaAZsTZ79uw88MADWbJkSZ9/17x58zJ37tzu8+bmZuFaAY2NSV3dloVrdXVd/QEYOlrmtXQfb276Zn9/FgAADBaDIlg77bTTcsMNN+TXv/51dthhh+72KVOmpK2tLc8++2yPUWsrVqzIlClTuvvceeedPT5v3a6h6/q82NixYzN27NhevouRZ/z4rrXTtnSNNdNAAYaWTa13Vh5drnkttN78LAAAGCwGdCpotVrNaaedlmuvvTa//OUvs9NOO/W4vu+++2b06NH5xS9+0d328MMPZ9myZZk+fXqSZPr06bn//vuzcuXK7j433XRTGhsbM23atP65kRFs7tyko2PzfTo6kjlz+qceAAAAgP4yoMHa7Nmzc/nll+fKK6/MVlttleXLl2f58uVpbW1NkjQ1NeXDH/5w5s6dm1/96le566678s///M+ZPn169t9//yTJYYcdlmnTpuX444/PvffemxtvvDFnn312Zs+ebVRaPzjggGTRoqRU6hqZtr76+q72RYuSGTMGpj4AAACAvlKqVqsDtv1WqVTaaPu3vvWtnHTSSUmS1atX51//9V/z3e9+N2vWrMnMmTOzaNGiHtM8H3/88Zxyyim5+eabUy6Xc+KJJ+bzn/986l+c9GxCLduosnG33ZYsXNi1+2dnZ9eaascc0zVSTagGAAAADBW15EQDGqwNFoK13tPa2rX7Z2OjNdUAAACAoaeWnGhQbF7A8DF+vEANAAAAGBkGdI01AAAAABiqBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQD8t0pbJaX5pZTml1Jpqwx0OQAADHKCNQAAAAAoQLAGAAAAAAXUD3QBAAADaf0pn5X2jR8nSXlMud9qAgBgaBCsAQAjWsOCho22T75gco/z6nnV/igHAIAhxFRQAAAAACjAiDUAYERrmdfSfVxpr3SPVFtx5oqUR5v+CQDApgnWAIARbVNrp5VHl62rBgDAZpkKCgAAAAAFCNYAAAAAoABTQQE2o7U1aW5OGhuT8eMHuhqgr5XHlO3+CQDAFjNiDWAjlixJZs1KGhqSKVO6fs6aldx220BXBgAAwGAhWAN4kcWLk4MOSq6/Puns7Grr7Ow6P/DA5OKLB7Y+AAAABgfBGsB6lixJZs9OqtVk7dqe19au7Wo/9VQj1wAAABCsAfRw4YXJqFGb7zNqVLJwYf/UAwAAwOAlWAP4b62tyQ9/uOFItRdbuza59tqu/gAAAIxcgjWA/9bc/MKaai+ls7OrPwAAACOXYA3gvzU2JnVb+KdiXV1XfwAAAEYuwRrAfxs/Pjn66KS+fvP96uuTY47p6g8AAMDIJVgDWM/cuUlHx+b7dHQkc+b0Tz0AAAAMXoI1gPUccECyaFFSKm04cq2+vqt90aJkxoyBqQ8AAIDBQ7AG8CInn5zcemvXtNB1a67V1XWd33pr13UAAAB4iZWEAEamGTO6Xq2tXbt/NjZaUw0AAICeBGsAmzF+vEANAACAjTMVFAAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgBboNJWSWl+KaX5pVTaKgNdDgAAMAgI1gAAAACgAMEaAAAAABRQP9AFAMBgtf6Uz0r7xo+TpDym3G81AQAAg4dgDQA2oWFBw0bbJ18wucd59bxqf5QDAAAMMqaCAgAAAEABRqwBwCa0zGvpPq60V7pHqq04c0XKo03/BACAkU6wBgCbsKm108qjy9ZVAwAATAUFAAAAgCIEawAAAABQgKmgALAFymPKdv8EAAB6MGIN6DWtrcmKFV0/h4vheE8AAAD0DsEa8LItWZLMmpU0NCRTpnT9nDUrue22ga6suOF4TwAAAPQuwRrwsixenBx0UHL99UlnZ1dbZ2fX+YEHJhdfPLD1FTEc7wkAAIDeV6pWqyN+wZjm5uY0NTVl1apVaWxsHOhyYMhYsqQrgNrcnyKlUnLrrcmMGf1X18sxHO8JAACALVdLTmTEGlDYhRcmo0Ztvs+oUcnChf1TT28YjvcEAABA3zBiLUasQRGtrV3rjq2bKrk5dXVJS0syfnzf1/VyDMd7AgAAoDZGrAF9rrl5ywKopKtfc3Pf1tMbhuM9AQAA0HcEa0AhjY1do7a2RF1dV//BbjjeEwAAAH1HsAYUMn58cvTRSX395vvV1yfHHDM0pkwOx3sCAACg7wjWgMLmzk06Ojbfp6MjmTOnf+rpDcPxngAAAOgbgjWgsAMOSBYtSkqlDUd51dd3tS9alMyYMTD1FTEc7wkAAIC+IVgDXpaTT05uvbVrCuW69cnq6rrOb7216/pQs/49lUpdbaXS0L4nAAAAet9LrCQE8NJmzOh6tbZ27ZTZ2Dj01x+rVrt2/iyVuo5LpS3fMRQAAICRwYg1oNeMH59Mnjz0Q7XFi5ODDkquv/6FMK2zs+v8wAOTiy8e2PoAAAAYHARrAOtZsiSZPbtrlNratT2vrV3b1X7qqclttw1MfQAAAAwegjWA9Vx4YTJq1Ob7jBqVLFzYP/UAAAAweAnWhqHW1mTFiq6fwJZrbU1++MMNR6q92Nq1ybXX+mcMAABgpBOsDSNLliSzZiUNDcmUKV0/Z80yZQ22VHPzlm9Q0NnZ1R8AAICRS7A2TFhsHV6+xsakbgv/VKyr6+oPAADAyCVYGwYstg69Y/z45Oijk/r6zferr0+OOWbo734KAADAyyNYGwYstg69Z+7cpKNj8306OpI5c/qnHgAAAAYvwdoQZ7F16F0HHJAsWpSUShuOXKuv72pftCiZMWNg6gMAAGDwEKwNcRZbh9538snJrbd2TQtdt+ZaXV3X+a23dl0HAACAl1hJiMFu3WLrWxKuWWwdttyMGV2v1tauQLqx0ZpqAAAA9GTE2hBnsXXoW+PHJ5Mn+2cHAACADQnWhgGLrQMAAAD0P8HaMGCxdQAAAID+J1gbJiy2DgAAANC/bF4wjFhsHQAAAKD/CNaGofHjBWoAAAAAfc1UUAAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsEavam1NVqzo+gkAAAAwnAnW6BVLliSzZiUNDcmUKV0/Z81KbrttoCsDePkqbZWU5pdSml9Kpa0y0OUAAACDxIAGa7/+9a9z1FFHZfvtt0+pVMp1113X4/pJJ52UUqnU4/Wud72rR5+///3vOe6449LY2JiJEyfmwx/+cFpaWvrxLli8ODnooOT665POzq62zs6u8wMPTC6+eGDrAwAAAOgLAxqsVSqV7LPPPvna1762yT7vete78vTTT3e/vvvd7/a4ftxxx+XBBx/MTTfdlBtuuCG//vWv87GPfayvS+e/LVmSzJ6dVKvJ2rU9r61d29V+6qlGrgEAAADDT/1Afvnhhx+eww8/fLN9xo4dmylTpmz02u9///v89Kc/zW9/+9vst99+SZKvfOUrOeKII3LBBRdk++237/Wa6enCC5NRozYM1dY3alSycGEyY0b/1QXwcq0/5bPSvvHjJCmPKfdbTQAAwOAyoMHalrj55pszadKkvOIVr8ghhxySz3zmM9lmm22SJEuXLs3EiRO7Q7UkOfTQQ1NXV5c77rgjxxxzzEY/c82aNVmzZk33eXNzc9/exDDV2pr88IcvTP/clLVrk2uv7eo/fnz/1AbwcjUsaNho++QLJvc4r55X7Y9yAACAQWhQb17wrne9K5dddll+8Ytf5Atf+EJuueWWHH744eno6EiSLF++PJMmTerxnvr6+my99dZZvnz5Jj93wYIFaWpq6n5NnTq1T+9juGpufulQbZ3Ozq7+AAAAAMPFoB6x9oEPfKD7eK+99sree++dnXfeOTfffHPe8Y53FP7cefPmZe7cud3nzc3NwrUCGhuTurotC9fq6rr6AwwVLfNe2Ain0l7pHqm24swVKY82/RMAABjkI9Ze7LWvfW1e+cpX5k9/+lOSZMqUKVm5cmWPPmvXrs3f//73Ta7LlnSt29bY2NjjRe3Gj0+OPjqpf4l4tr4+OeYY00CBoaU8pvzCa70grTy63OMaAAAwcg2pYO0vf/lL/va3v2W77bZLkkyfPj3PPvts7rrrru4+v/zlL9PZ2Zm3vOUtA1XmiDJ3bvLfM3M3qaMjmTOnf+oBAAAA6C8DGqy1tLTknnvuyT333JMkefTRR3PPPfdk2bJlaWlpySc/+cncfvvteeyxx/KLX/wiRx99dHbZZZfMnDkzSfL6178+73rXu/LRj340d955Z2677bacdtpp+cAHPmBH0H5ywAHJokVJqbThyLX6+q72RYvsCAoAAAAMP6VqtTpg25ndfPPNOfjggzdoP/HEE7N48eK8973vzd13351nn30222+/fQ477LB8+tOfzuTJL+zI9ve//z2nnXZarr/++tTV1eXYY4/Nl7/85TQ0bHw3t41pbm5OU1NTVq1aZVpoQbfdlixc2LX7Z2dn15pqxxzTNVJNqAYAAAAMFbXkRAMarA0WgrXe09ratftnY6M11QAAAIChp5acaFDvCsrQM368QA0AAAAYGYbU5gUAAAAAMFgI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNaAl9TamqxY0fUTAAAA6CJYAzZpyZJk1qykoSGZMqXr56xZyW231f5ZlbZKSvNLKc0vpdJW6f1iAQAAoJ8J1oCNWrw4Oeig5Prrk87OrrbOzq7zAw9MLr54YOsDAACAgSZYAzawZEkye3ZSrSZr1/a8tnZtV/uppxYbuQYAAADDhWAN2MCFFyajRm2+z6hRycKFm+9Taau88Gp/Yfpnpb3S4xoAAAAMRaVqtVod6CIGWnNzc5qamrJq1ao0NjYOdDkwoFpbu9ZSWzf9c3Pq6pKWlmT8+I1fL80vbdF3Vs8b8X8MAQAAMEjUkhMZsQb00Ny8ZaFa0tWvublv6wEAAIDBqn6gC4DBqrW1KzRqbNz0iKzhqLGxayTalo5Y21x43zKvpfu40l7J5AsmJ0lWnLki5dHll1sqAAAADCgj1uBFlixJZs3qmg45ZUrXz1mzRs5C/ePHJ0cfndS/ROxeX58cc8zmQ8fymPILr/WCtPLoco9rAAAAMBQJ1mA9ixcnBx2UXH/9CyO2Oju7zg88MLn44oGtr7/MnZt0dGy+T0dHMmdO/9QDAAAAg5FgDf7bkiXJ7NlJtZqsXdvz2tq1Xe2nnjoyRq4dcECyaFFSKm04cq2+vqt90aJkxoyBqQ8AAAAGA8Ea/LcLL0xGjdp8n1GjkoUL+6eegXbyycmtt3ZNC6377z8p6uq6zm+9tet6LcpjyqmeV031vKrpnwAAAAwLpWq1Wh3oIgZaLduoMjy1tnatpbalC/a3tIysDQ1G6kYOAAAAjDy15ER2BYV0hUZbEqolXf2am0dWwDR+/Mi6XwAAANgSpoJCukZi1W3hPw11dV39AQAAgJFNsAbpGo119NEbLtT/YvX1yTHHGL0FAAAACNag29y5SUfH5vt0dCRz5vRPPQAAAMDgJliD/3bAAcmiRUmptOHItfr6rvZFi5IZMwamPgAAAGBwEazBek4+Obn11q5poevWXKur6zq/9dau6wAAAACJXUFhAzNmdL1aW7t2/2xstKYaAAAAsCHBGmzC+PECNQAAAGDTTAUFAAAAgAIEawAAAABQgGANAAAAAAoQrAEAAABAAYI1AAAAAChAsAYAAAAABQjWAAAAAKAAwRoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKwBAAAAQAGCNQAAAAAoQLAGAAAAAAUI1gAAAACgAMEaAAAAABQgWAMAAACAAgRrAAAAAFCAYA0AAAAAChCsAQAAAEABgjUAAAAAKECwBgAAAAAFCNYAAAAAoADBGgAAAAAUIFgDAAAAgAIEawAAAABQQP1AFzAYVKvVJElzc/MAVwIAAADAQFqXD63LizZHsJbkueeeS5JMnTp1gCsBAAAAYDB47rnn0tTUtNk+peqWxG/DXGdnZ5566qlstdVWKZVKA13OkNbc3JypU6fmiSeeSGNj40CXwzDi2aKveLboK54t+opni77i2aKveLboK331bFWr1Tz33HPZfvvtU1e3+VXUjFhLUldXlx122GGgyxhWGhsb/YFJn/Bs0Vc8W/QVzxZ9xbNFX/Fs0Vc8W/SVvni2Xmqk2jo2LwAAAACAAgRrAAAAAFCAYI1eNXbs2Jx33nkZO3bsQJfCMOPZoq94tugrni36imeLvuLZoq94tugrg+HZsnkBAAAAABRgxBoAAAAAFCBYAwAAAIACBGsAAAAAUIBgDQAAAAAKEKyxgV//+tc56qijsv3226dUKuW6667rvtbe3p6zzjore+21V8rlcrbffvuccMIJeeqpp3p8xt///vccd9xxaWxszMSJE/PhD384LS0tPfrcd999OfDAAzNu3LhMnTo1X/ziF/vj9hhAm3u2Xuzkk09OqVTKl770pR7tni02Zkuerd///vd5z3vek6amppTL5bz5zW/OsmXLuq+vXr06s2fPzjbbbJOGhoYce+yxWbFiRY/PWLZsWY488shMmDAhkyZNyic/+cmsXbu2r2+PAfRSz1ZLS0tOO+207LDDDhk/fnymTZuWiy++uEcfzxYbs2DBgrz5zW/OVlttlUmTJuW9731vHn744R59euvZufnmm/OmN70pY8eOzS677JJLL720r2+PAfRSz9bf//73nH766dltt90yfvz47Ljjjvn4xz+eVatW9fgczxYvtiV/bq1TrVZz+OGHb/TfnZ4tXmxLn62lS5fmkEMOSblcTmNjYw466KC0trZ2Xx+o/1cUrLGBSqWSffbZJ1/72tc2uPb888/nd7/7Xc4555z87ne/yzXXXJOHH34473nPe3r0O+644/Lggw/mpptuyg033JBf//rX+djHPtZ9vbm5OYcddlhe/epX56677sr/+l//K5/61KfyjW98o8/vj4GzuWdrfddee21uv/32bL/99htc82yxMS/1bD3yyCM54IADsvvuu+fmm2/Offfdl3POOSfjxo3r7jNnzpxcf/31ueqqq3LLLbfkqaeeyqxZs7qvd3R05Mgjj0xbW1t+85vf5Nvf/nYuvfTSnHvuuX1+fwycl3q25s6dm5/+9Ke5/PLL8/vf/z5nnHFGTjvttPzoRz/q7uPZYmNuueWWzJ49O7fffntuuummtLe357DDDkulUunu0xvPzqOPPpojjzwyBx98cO65556cccYZ+chHPpIbb7yxX++X/vNSz9ZTTz2Vp556KhdccEEeeOCBXHrppfnpT3+aD3/4w92f4dliY7bkz611vvSlL6VUKm3Q7tliY7bk2Vq6dGne9a535bDDDsudd96Z3/72tznttNNSV/dCrDVg/69Yhc1IUr322ms32+fOO++sJqk+/vjj1Wq1Wn3ooYeqSaq//e1vu/v85Cc/qZZKpeqTTz5ZrVar1UWLFlVf8YpXVNesWdPd56yzzqrutttuvX8TDEqberb+8pe/VF/1qldVH3jggeqrX/3q6sKFC7uvebbYEht7tv7xH/+x+k//9E+bfM+zzz5bHT16dPWqq67qbvv9739fTVJdunRptVqtVv9//7//X7Wurq66fPny7j6LFy+uNjY29njeGL429mztscce1fPPP79H25ve9Kbqf/zHf1SrVc8WW27lypXVJNVbbrmlWq323rPzb//2b9U99tijx3f94z/+Y3XmzJl9fUsMEi9+tjbm//yf/1MdM2ZMtb29vVqterbYMpt6tu6+++7qq171qurTTz+9wb87PVtsiY09W295y1uqZ5999ibfM5D/r2jEGi/bqlWrUiqVMnHixCRdSfLEiROz3377dfc59NBDU1dXlzvuuKO7z0EHHZQxY8Z095k5c2Yefvjh/L//9//6tX4Gj87Ozhx//PH55Cc/mT322GOD654tiujs7MyPf/zjvO51r8vMmTMzadKkvOUtb+kxLeGuu+5Ke3t7Dj300O623XffPTvuuGOWLl2apOvZ2muvvTJ58uTuPjNnzkxzc3MefPDBfrsfBpe3vvWt+dGPfpQnn3wy1Wo1v/rVr/LHP/4xhx12WBLPFltu3TS8rbfeOknvPTtLly7t8Rnr+qz7DIa/Fz9bm+rT2NiY+vr6JJ4ttszGnq3nn38+H/rQh/K1r30tU6ZM2eA9ni22xIufrZUrV+aOO+7IpEmT8ta3vjWTJ0/O2972tixZsqT7PQP5/4qCNV6W1atX56yzzsoHP/jBNDY2JkmWL1+eSZMm9ehXX1+frbfeOsuXL+/us/4fpkm6z9f1YeT5whe+kPr6+nz84x/f6HXPFkWsXLkyLS0t+fznP593vetd+dnPfpZjjjkms2bNyi233JL/f3v3HhRV+f8B/L1yR7mJoiCxeFnMCwoo0UqJIyp4QcDUchxEu0ypjKaIZSri9DXUBEJGk1KXcpzBTDHSYkQuo6KSIeA1ReTS5CpeQiAvsPD8/vDL+bWyyMLXIOX9mtkZzjnPec7nLJ+Bs5/z7HmAx7lhbGws3SBo1KtXL+YWPVVCQgIGDx4MR0dHGBsbw9/fH1u2bMHo0aMBMLdIPw0NDfjwww/h7e2NoUOHAnh2udNcm6qqKq3n0tCLSVduPen27dv49NNPtb4uxdyiljSXW0uWLMGoUaMQGBiocz/mFrVEV25du3YNABAVFYX33nsPaWlp8PDwgK+vL4qKigB07GdFwzbvSZ1eXV0dZs6cCSEEvvzyy44Oh55zeXl5iI+Px5kzZ3Q+j4GorRoaGgAAgYGBWLJkCQDAzc0NJ06cwLZt2+Dj49OR4dFzLiEhAadOnUJqairkcjmOHj2KhQsXwsHBocnddqLmLFy4EOfPn9e68070LLSUW1VVVZg8eTIGDx6MqKio9g2Onmu6cis1NRWZmZnIz8/vwMjoeacrtxqv599//33MmzcPAODu7o6MjAzs3LkT0dHRHRJrI45YozZpLKqVlZUhPT1dGq0GAL1790ZFRYVWe41Gg7t370rDgXv37t1kVqvGZV1DhunFd+zYMVRUVMDJyQmGhoYwNDREWVkZwsPD4ezsDIC5RW3To0cPGBoaYvDgwVrrBw0aJM0K2rt3b9TW1qKyslKrzc2bN5lb1KwHDx7gk08+QWxsLAICAjBs2DCEhYXhzTffxKZNmwAwt6hlYWFhOHjwILKysuDo6Citf1a501wbS0tLmJmZPevToX+R5nKrUXV1Nfz9/WFhYYGUlBQYGRlJ25hb9DTN5VZmZiaKi4thbW0tXc8DwBtvvIExY8YAYG7R0zWXW/b29gDQ4vV8R31WZGGNWq2xqFZUVIQjR47A1tZWa7tSqURlZSXy8vKkdZmZmWhoaICXl5fU5ujRo6irq5PapKenY+DAgbCxsWmfE6F/lZCQEJw9exYFBQXSy8HBAREREdIMQMwtagtjY2N4eno2mbL7ypUrkMvlAIARI0bAyMgIGRkZ0vbLly+jvLwcSqUSwOPcOnfunNY/7MYbC0/+k6fOoa6uDnV1dVqzUQGAgYGBdGeVuUXNEUIgLCwMKSkpyMzMRN++fbW2P6vcUSqVWn00tmnsg148LeUW8P8z4xkbGyM1NVVrlmyAuUW6tZRbH3/8cZPreQCIi4uDSqUCwNwi3VrKLWdnZzg4ODz1er5DPyv+T1Mf0Aupurpa5Ofni/z8fAFAxMbGivz8fFFWViZqa2vF1KlThaOjoygoKBBqtVp6/X1mDX9/f+Hu7i5yc3PF8ePHhUKhELNmzZK2V1ZWil69eomQkBBx/vx5kZycLMzNzUViYmJHnDK1k6flli5PzgoqBHOLdGspt/bv3y+MjIzEV199JYqKikRCQoIwMDAQx44dk/r44IMPhJOTk8jMzBS//vqrUCqVQqlUSts1Go0YOnSomDBhgigoKBBpaWmiZ8+eYsWKFe1+vtR+WsotHx8fMWTIEJGVlSWuXbsmVCqVMDU1FVu3bpX6YG6RLvPnzxdWVlYiOztb63rq/v37UptnkTvXrl0T5ubmIiIiQly6dEls2bJFGBgYiLS0tHY9X2o/LeXWvXv3hJeXl3B1dRVXr17VaqPRaIQQzC3STZ+/W0/CE7OCMrdIF31yKy4uTlhaWoq9e/eKoqIisWrVKmFqaiquXr0qtemoz4osrFETWVlZAkCTV2hoqCgpKdG5DYDIysqS+rhz546YNWuW6Natm7C0tBTz5s0T1dXVWscpLCwUr732mjAxMRF9+vQR69evb+czpfb2tNzSRVdhjblFuuiTWzt27BADBgwQpqamYvjw4eLAgQNafTx48EAsWLBA2NjYCHNzcxEcHCzUarVWm9LSUjFx4kRhZmYmevToIcLDw0VdXV17nCJ1kJZyS61Wi7lz5woHBwdhamoqBg4cKGJiYkRDQ4PUB3OLdGnuekqlUkltnlXuZGVlCTc3N2FsbCz69eundQx68bSUW839XQMgSkpKpH6YW/Qkff5u6drn74U1IZhb1JS+uRUdHS0cHR2Fubm5UCqVWjfJhei4z4qy/54EERERERERERERtQKfsUZERERERERERNQGLKwRERERERERERG1AQtrREREREREREREbcDCGhERERERERERURuwsEZERERERERERNQGLKwRERERERERERG1AQtrREREREREREREbcDCGhERERERERERURuwsEZERET0D5DJZDhw4EBHh9Fubty4gfHjx6Nr166wtrbu6HCIiIiI2gULa0RERPRCk8lkT31FRUU1u29paSlkMhkKCgraLd7nVVxcHNRqNQoKCnDlypVn1m9nK1ASERHR88WwowMgIiIi+iep1Wrp5z179iAyMhKXL1+W1nXr1q0jwvpXqq+vh0wmQ5curb/3WlxcjBEjRkChUPwDkT17tbW1MDY27ugwiIiI6DnHEWtERET0Quvdu7f0srKygkwmk5bt7OwQGxsLR0dHmJiYwM3NDWlpadK+ffv2BQC4u7tDJpNhzJgxAIDTp09j/Pjx6NGjB6ysrODj44MzZ860Kq7vv/8erq6uMDMzg62tLcaNG4e//voLwOMC19KlS2FtbQ1bW1ssX74coaGhCAoKkvZ3dnbGF198odWnm5ub1gi82NhYuLq6omvXrnjppZewYMEC1NTUSNuTkpJgbW2N1NRUDB48GCYmJigvL8ejR4+wbNky9OnTB127doWXlxeys7ObPRdnZ2fs27cP3377LWQyGebOnQsAqKysxLvvvouePXvC0tISY8eORWFhoda+P/zwAzw8PGBqaop+/fph7dq10Gg0Ur8AEBwcDJlMJi3r8tFHH8HFxQXm5ubo168fVq9ejbq6Oml7VFQU3NzcsH37dvTt2xempqZ6xVhcXIzAwED06tUL3bp1g6enJ44cOdJsHERERNS5sLBGREREnVZ8fDxiYmKwadMmnD17Fn5+fpg6dSqKiooAAL/88gsA4MiRI1Cr1di/fz8AoLq6GqGhoTh+/DhOnToFhUKBSZMmobq6Wq/jqtVqzJo1C2+//TYuXbqE7OxsTJs2DUIIAEBMTAySkpKwc+dOHD9+HHfv3kVKSkqrz69Lly7YvHkzLly4gG+++QaZmZlYvny5Vpv79+9jw4YN2L59Oy5cuAA7OzuEhYXh5MmTSE5OxtmzZzFjxgz4+/tL78uTTp8+DX9/f8ycORNqtRrx8fEAgBkzZqCiogI///wz8vLy4OHhAV9fX9y9excAcOzYMcyZMweLFy/GxYsXkZiYiKSkJKxbt07qFwBUKhXUarW0rIuFhQWSkpJw8eJFxMfH4+uvv0ZcXJxWm6tXr2Lfvn3Yv3+/9PXelmKsqanBpEmTkJGRgfz8fPj7+yMgIADl5eWt/G0QERHRC0kQERERdRIqlUpYWVlJyw4ODmLdunVabTw9PcWCBQuEEEKUlJQIACI/P/+p/dbX1wsLCwvx448/SusAiJSUFJ3t8/LyBABRWlqqc7u9vb3YuHGjtFxXVyccHR1FYGCgtE4ul4u4uDit/YYPHy7WrFnTbJx79+4Vtra20rJKpRIAREFBgbSurKxMGBgYiD/++ENrX19fX7FixYpm+w4MDBShoaHS8rFjx4SlpaV4+PChVrv+/fuLxMREqc/PPvtMa/uuXbuEvb29tPy09/FpPv/8czFixAhpec2aNcLIyEhUVFS0KkZdhgwZIhISElodExEREb14+Iw1IiIi6pSqqqpw/fp1eHt7a6339vZu8nXFJ928eROrVq1CdnY2KioqUF9fj/v37+s9imn48OHw9fWFq6sr/Pz8MGHCBEyfPh02Nja4d+8e1Go1vLy8pPaGhoYYOXKkNKJNX0eOHEF0dDR+++03VFVVQaPR4OHDh7h//z7Mzc0BAMbGxhg2bJi0z7lz51BfXw8XFxetvh49egRbW1u9j11YWIiampom+zx48ADFxcVSm5ycHGmEGvD4a7BPxqiPPXv2YPPmzSguLkZNTQ00Gg0sLS212sjlcvTs2bNVMdbU1CAqKgqHDh2CWq2GRqPBgwcPOGKNiIiIAHDyAiIiIqJWCw0NxZ07dxAfHw+5XA4TExMolUrU1tbqtb+BgQHS09Nx4sQJHD58GAkJCVi5ciVyc3PRvXt3vfro0qVLk0Lb358pVlpaiilTpmD+/PlYt24dunfvjuPHj+Odd95BbW2tVLQyMzODTCaT9qupqYGBgQHy8vJgYGCg1X9rJnqoqamBvb29zmezWVtbS23Wrl2LadOmNWnT+Aw0fZw8eRKzZ8/G2rVr4efnBysrKyQnJyMmJkarXdeuXVsd47Jly5Ceno5NmzZhwIABMDMzw/Tp0/X+XRMREdGLjYU1IiIi6pQsLS3h4OCAnJwc+Pj4SOtzcnLwyiuvAIA0a2R9fb3Wvjk5Odi6dSsmTZoEAPj9999x+/btVh1fJpPB29sb3t7eiIyMhFwuR0pKCpYuXQp7e3vk5uZi9OjRAACNRiM9/6tRz549tWY8raqqQklJibScl5eHhoYGxMTESLN8fvfddy3G5e7ujvr6elRUVOD1119v1Tn9nYeHB27cuAFDQ8NmJx3w8PDA5cuXMWDAgGb7MTIyavL+P+nEiROQy+VYuXKltK6srOyZxJiTk4O5c+ciODgYwONiXGlpaYt9ExERUefAwhoRERF1WhEREVizZg369+8PNzc3qFQqFBQUYPfu3QAAOzs7mJmZIS0tDY6OjjA1NYWVlRUUCgV27dqFkSNHoqqqChERETAzM9P7uLm5ucjIyMCECRNgZ2eH3Nxc3Lp1C4MGDQIALF68GOvXr4dCocDLL7+M2NhYVFZWavUxduxYJCUlISAgANbW1oiMjNQaYTZgwADU1dUhISEBAQEByMnJwbZt21qMzcXFBbNnz8acOXMQExMDd3d33Lp1CxkZGRg2bBgmT56s1zmOGzcOSqUSQUFB2LhxI1xcXHD9+nUcOnQIwcHBGDlyJCIjIzFlyhQ4OTlh+vTp6NKlCwoLC3H+/Hn85z//AfB4ZtCMjAx4e3vDxMQENjY2TY6lUChQXl6O5ORkeHp64tChQ3pN9qBPjAqFAvv370dAQABkMhlWr16NhoYGvd4DIiIievFxVlAiIiLqtBYtWoSlS5ciPDwcrq6uSEtLQ2pqKhQKBYDHzzbbvHkzEhMT4eDggMDAQADAjh078Oeff8LDwwMhISFYtGgR7Ozs9D6upaUljh49ikmTJsHFxQWrVq1CTEwMJk6cCAAIDw9HSEgIQkNDoVQqYWFhIY2YarRixQr4+PhgypQpmDx5MoKCgtC/f39p+/DhwxEbG4sNGzZg6NCh2L17N6Kjo/WKT6VSYc6cOQgPD8fAgQMRFBSE06dPw8nJSe9zlMlk+OmnnzB69GjMmzcPLi4ueOutt1BWVoZevXoBAPz8/HDw4EEcPnwYnp6eePXVVxEXFwe5XC71ExMTg/T0dLz00ktwd3fXeaypU6diyZIlCAsLg5ubG06cOIHVq1c/kxhjY2NhY2ODUaNGISAgAH5+flojB4mIiKhzk4nWPgWXiIiIiNrd3LlzUVlZiQMHDnR0KERERET0XxyxRkRERERERERE1AYsrBEREREREREREbUBvwpKRERERERERETUBhyxRkRERERERERE1AYsrBEREREREREREbUBC2tERERERERERERtwMIaERERERERERFRG7CwRkRERERERERE1AYsrBEREREREREREbUBC2tERERERERERERtwMIaERERERERERFRG/wfAZtNN7LzhtUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "def plot_scatter_chart(df,location):\n",
        "    bhk2=df[(df.location==location)& (df.BHK==2)]\n",
        "    bhk3=df[(df.location==location)& (df.BHK==3)]\n",
        "    matplotlib.rcParams['figure.figsize']=(15,10)\n",
        "    plt.scatter(bhk2.total_sqft,bhk2.price,color='blue',label='2bhk',s=50)\n",
        "    plt.scatter(bhk3.total_sqft,bhk3.price,color='green',marker='+',label='3bhk',s=50)\n",
        "    plt.xlabel(\"Total square feet area\")\n",
        "    plt.ylabel('price per square feet')\n",
        "    plt.title(location)\n",
        "    plt.legend()\n",
        "plot_scatter_chart(df7,'Rajaji Nagar')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "f80a8943",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f80a8943",
        "outputId": "c1bd8e61-4f6e-4e3b-8b68-a31b513c51c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7329, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "def remove_bhk_outliers(df):\n",
        "    exclude_indices=np.array([])\n",
        "    for location,location_df in df.groupby('location'):\n",
        "        bhk_stats={}\n",
        "        for bhk,bhk_df in location_df.groupby('BHK'):\n",
        "            bhk_stats[bhk]={\n",
        "                'mean':np.mean(bhk_df.price_per_sqft),\n",
        "                'std':np.std(bhk_df.price_per_sqft),\n",
        "                'count':bhk_df.shape[0]\n",
        "            }\n",
        "        for bhk,bhk_df in location_df.groupby('BHK'):\n",
        "            stats=bhk_stats.get(bhk-1)\n",
        "            if stats and stats['count']>5:\n",
        "                exclude_indices=np.append(exclude_indices,bhk_df[bhk_df.price_per_sqft<(stats['mean'])].index.values)\n",
        "    return df.drop(exclude_indices,axis='index')\n",
        "\n",
        "df8=remove_bhk_outliers(df7)\n",
        "df8.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "7901d6e9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "7901d6e9",
        "outputId": "dffef649-5cb2-44c9-b95e-f82560d051dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plot_scatter_chart(df8,'Hebbal')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "236f61c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 875
        },
        "id": "236f61c1",
        "outputId": "2330cd92-b1bd-4fb8-f98a-36c92da2ae49"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 548., 3591., 2300.,  457.,  253.,  115.,   39.,   16.,    5.,\n",
              "           5.]),\n",
              " array([ 1300.        ,  3620.98039216,  5941.96078431,  8262.94117647,\n",
              "        10583.92156863, 12904.90196078, 15225.88235294, 17546.8627451 ,\n",
              "        19867.84313725, 22188.82352941, 24509.80392157]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib\n",
        "matplotlib.rcParams['figure.figsize']=(20,10)\n",
        "plt.hist(df8.price_per_sqft,rwidth=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "0179f986",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0179f986",
        "outputId": "6a161333-0af5-441b-96c9-b551355273ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.,  3.,  2.,  5.,  8.,  1.,  6.,  7.,  9., 12., 16., 13.])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df8.bath.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "5f7e36a6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "5f7e36a6",
        "outputId": "dde1e0b5-bbaf-4ce3-d2d6-e898d0816b6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABmIAAANGCAYAAADuxZHwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJp0lEQVR4nO3de7RVdb3//9fmKqJ7IygbOYJQloLh3XSnmRaxMzRNreyYkpqlgQmYouPrlS6g5T2VbobfysrGCVP5ChEKVuIlPJSYcMw0KAQ8KWw1BYT1+6PB+rnDNJAPS9iPxxhryJrzs+Z6T5hjj+jJXKuuUqlUAgAAAAAAwEbXrtYDAAAAAAAAbKmEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEKEGAAAAAAAgEJqGmIuueSS1NXVtXrstttu1f0vv/xyhg8fnh49emSbbbbJsccemyVLlrQ6xoIFCzJ06NBsvfXW6dmzZ84555y88sorrdbMmDEj++yzTzp37pxddtklEydO3BSnBwAAAAAAtHE1vyNm9913z9NPP119/PrXv67uGzVqVO6444789Kc/zcyZM7No0aIcc8wx1f2rV6/O0KFDs3Llytx33325+eabM3HixFx00UXVNU8++WSGDh2aww47LHPmzMnIkSPzmc98JlOnTt2k5wkAAAAAALQ9dZVKpVKrN7/kkkty2223Zc6cOevsW758eXbYYYfccsstOe6445Ik8+bNy4ABAzJr1qwceOCBueuuu3LEEUdk0aJFaWxsTJJMmDAhY8aMyTPPPJNOnTplzJgxmTx5cubOnVs99vHHH59ly5ZlypQpm+Q8AQAAAACAtqlDrQd4/PHH07t372y11VZpamrKuHHj0rdv38yePTurVq3K4MGDq2t322239O3btxpiZs2alUGDBlUjTJI0NzfnjDPOyKOPPpq99947s2bNanWMtWtGjhz5L2dasWJFVqxYUX2+Zs2aPPvss+nRo0fq6uo23skDAAAAAACbnUqlkueffz69e/dOu3av/+FjNQ0xBxxwQCZOnJhdd901Tz/9dC699NK8973vzdy5c7N48eJ06tQp3bp1a/WaxsbGLF68OEmyePHiVhFm7f61+15vTUtLS1566aV06dJlnbnGjRuXSy+9dGOdJgAAAAAAsAVauHBhdtppp9ddU9MQc/jhh1d/vccee+SAAw7IzjvvnFtvvfU1A8mmcv7552f06NHV58uXL0/fvn2zcOHC1NfX12wuAAAAAACg9lpaWtKnT59su+22b7i25h9N9mrdunXLO9/5zvzxj3/MBz/4waxcuTLLli1rdVfMkiVL0qtXryRJr1698uCDD7Y6xpIlS6r71v537bZXr6mvr/+Xsadz587p3LnzOtvr6+uFGAAAAAAAIEn+ra8zef0PLtvEXnjhhTzxxBPZcccds++++6Zjx46ZPn16df/8+fOzYMGCNDU1JUmampryyCOPZOnSpdU106ZNS319fQYOHFhd8+pjrF2z9hgAAAAAAACl1DTEfPGLX8zMmTPz1FNP5b777stHP/rRtG/fPp/85CfT0NCQU089NaNHj84999yT2bNn5+STT05TU1MOPPDAJMmQIUMycODAnHjiifnd736XqVOn5oILLsjw4cOrd7Scfvrp+dOf/pRzzz038+bNyw033JBbb701o0aNquWpAwAAAAAAbUBNP5rsL3/5Sz75yU/mb3/7W3bYYYccfPDBuf/++7PDDjskSa666qq0a9cuxx57bFasWJHm5ubccMMN1de3b98+d955Z84444w0NTWla9euGTZsWMaOHVtd079//0yePDmjRo3KNddck5122inf+c530tzcvMnPFwAAAAAAaFvqKpVKpdZDvNW1tLSkoaEhy5cv9x0xAAAAAADQxq1PN3hLfUcMAAAAAADAlkSIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKESIAQAAAAAAKKRDrQdg89fvvMm1HoGN5KnxQ2s9AgAAAADAFsUdMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIUIMQAAAAAAAIW8ZULM+PHjU1dXl5EjR1a3vfzyyxk+fHh69OiRbbbZJscee2yWLFnS6nULFizI0KFDs/XWW6dnz54555xz8sorr7RaM2PGjOyzzz7p3Llzdtlll0ycOHETnBEAAAAAANDWvSVCzEMPPZRvfvOb2WOPPVptHzVqVO6444789Kc/zcyZM7No0aIcc8wx1f2rV6/O0KFDs3Llytx33325+eabM3HixFx00UXVNU8++WSGDh2aww47LHPmzMnIkSPzmc98JlOnTt1k5wcAAAAAALRNNQ8xL7zwQk444YR8+9vfznbbbVfdvnz58nz3u9/NlVdemfe///3Zd999873vfS/33Xdf7r///iTJL37xi/zhD3/ID37wg+y11145/PDD86UvfSnXX399Vq5cmSSZMGFC+vfvnyuuuCIDBgzIiBEjctxxx+Wqq66qyfkCAAAAAABtR81DzPDhwzN06NAMHjy41fbZs2dn1apVrbbvtttu6du3b2bNmpUkmTVrVgYNGpTGxsbqmubm5rS0tOTRRx+trvnnYzc3N1eP8VpWrFiRlpaWVg8AAAAAAID11aGWb/7jH/84Dz/8cB566KF19i1evDidOnVKt27dWm1vbGzM4sWLq2teHWHW7l+77/XWtLS05KWXXkqXLl3Wee9x48bl0ksv3eDzAgAAAAAASGp4R8zChQtz1lln5Yc//GG22mqrWo3xms4///wsX768+li4cGGtRwIAAAAAADZDNQsxs2fPztKlS7PPPvukQ4cO6dChQ2bOnJlrr702HTp0SGNjY1auXJlly5a1et2SJUvSq1evJEmvXr2yZMmSdfav3fd6a+rr61/zbpgk6dy5c+rr61s9AAAAAAAA1lfNQswHPvCBPPLII5kzZ071sd9+++WEE06o/rpjx46ZPn169TXz58/PggUL0tTUlCRpamrKI488kqVLl1bXTJs2LfX19Rk4cGB1zauPsXbN2mMAAAAAAACUUrPviNl2223zrne9q9W2rl27pkePHtXtp556akaPHp3u3bunvr4+Z555ZpqamnLggQcmSYYMGZKBAwfmxBNPzOWXX57FixfnggsuyPDhw9O5c+ckyemnn55vfOMbOffcc3PKKafk7rvvzq233prJkydv2hMGAAAAAADanJqFmH/HVVddlXbt2uXYY4/NihUr0tzcnBtuuKG6v3379rnzzjtzxhlnpKmpKV27ds2wYcMyduzY6pr+/ftn8uTJGTVqVK655prstNNO+c53vpPm5uZanBIAAAAAANCG1FUqlUqth3ira2lpSUNDQ5YvX+77Yl5Dv/PcXbSleGr80FqPAAAAAADwlrc+3aBm3xEDAAAAAACwpRNiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAAChFiAAAAAAAACqlpiLnxxhuzxx57pL6+PvX19Wlqaspdd91V3f/yyy9n+PDh6dGjR7bZZpsce+yxWbJkSatjLFiwIEOHDs3WW2+dnj175pxzzskrr7zSas2MGTOyzz77pHPnztlll10yceLETXF6AAAAAABAG1fTELPTTjtl/PjxmT17dn7729/m/e9/f4466qg8+uijSZJRo0bljjvuyE9/+tPMnDkzixYtyjHHHFN9/erVqzN06NCsXLky9913X26++eZMnDgxF110UXXNk08+maFDh+awww7LnDlzMnLkyHzmM5/J1KlTN/n5AgAAAAAAbUtdpVKp1HqIV+vevXu+9rWv5bjjjssOO+yQW265Jccdd1ySZN68eRkwYEBmzZqVAw88MHfddVeOOOKILFq0KI2NjUmSCRMmZMyYMXnmmWfSqVOnjBkzJpMnT87cuXOr73H88cdn2bJlmTJlyr81U0tLSxoaGrJ8+fLU19dv/JPezPU7b3KtR2AjeWr80FqPAAAAAADwlrc+3eAt8x0xq1evzo9//OO8+OKLaWpqyuzZs7Nq1aoMHjy4uma33XZL3759M2vWrCTJrFmzMmjQoGqESZLm5ua0tLRU76qZNWtWq2OsXbP2GK9lxYoVaWlpafUAAAAAAABYXzUPMY888ki22WabdO7cOaeffnomTZqUgQMHZvHixenUqVO6devWan1jY2MWL16cJFm8eHGrCLN2/9p9r7empaUlL7300mvONG7cuDQ0NFQfffr02RinCgAAAAAAtDE1DzG77rpr5syZkwceeCBnnHFGhg0blj/84Q81nen888/P8uXLq4+FCxfWdB4AAAAAAGDz1KHWA3Tq1Cm77LJLkmTffffNQw89lGuuuSaf+MQnsnLlyixbtqzVXTFLlixJr169kiS9evXKgw8+2Op4S5Ysqe5b+9+12169pr6+Pl26dHnNmTp37pzOnTtvlPMDAAAAAADarprfEfPP1qxZkxUrVmTfffdNx44dM3369Oq++fPnZ8GCBWlqakqSNDU15ZFHHsnSpUura6ZNm5b6+voMHDiwuubVx1i7Zu0xAAAAAAAASqnpHTHnn39+Dj/88PTt2zfPP/98brnllsyYMSNTp05NQ0NDTj311IwePTrdu3dPfX19zjzzzDQ1NeXAAw9MkgwZMiQDBw7MiSeemMsvvzyLFy/OBRdckOHDh1fvaDn99NPzjW98I+eee25OOeWU3H333bn11lszefLkWp46AAAAAADQBtQ0xCxdujQnnXRSnn766TQ0NGSPPfbI1KlT88EPfjBJctVVV6Vdu3Y59thjs2LFijQ3N+eGG26ovr59+/a58847c8YZZ6SpqSldu3bNsGHDMnbs2Oqa/v37Z/LkyRk1alSuueaa7LTTTvnOd76T5ubmTX6+AAAAAABA21JXqVQqtR7ira6lpSUNDQ1Zvnx56uvraz3OW06/89xdtKV4avzQWo8AAAAAAPCWtz7d4C33HTEAAAAAAABbCiEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgECEGAAAAAACgkA0KMW9729vyt7/9bZ3ty5Yty9ve9rY3PRQAAAAAAMCWYINCzFNPPZXVq1evs33FihX561//+qaHAgAAAAAA2BJ0WJ/Ft99+e/XXU6dOTUNDQ/X56tWrM3369PTr12+jDQcAAAAAALA5W68Qc/TRRydJ6urqMmzYsFb7OnbsmH79+uWKK67YaMMBAAAAAABsztYrxKxZsyZJ0r9//zz00EPZfvvtiwwFAAAAAACwJVivELPWk08+ubHnAAAAAAAA2OJsUIhJkunTp2f69OlZunRp9U6ZtW666aY3PRgAAAAAAMDmboNCzKWXXpqxY8dmv/32y4477pi6urqNPRcAAAAAAMBmb4NCzIQJEzJx4sSceOKJG3seAAAAAACALUa7DXnRypUr8573vGdjzwIAAAAAALBF2aAQ85nPfCa33HLLxp4FAAAAAABgi7JBH0328ssv51vf+lZ++ctfZo899kjHjh1b7b/yyis3ynAAAAAAAACbsw0KMb///e+z1157JUnmzp3bal9dXd2bHgoAAAAAAGBLsEEh5p577tnYcwAAAAAAAGxxNug7YgAAAAAAAHhjG3RHzGGHHfa6H0F29913b/BAAAAAAAAAW4oNCjFrvx9mrVWrVmXOnDmZO3duhg0btjHmAgAAAAAA2OxtUIi56qqrXnP7JZdckhdeeOFNDQQAAAAAALCl2KjfEfOpT30qN91008Y8JAAAAAAAwGZro4aYWbNmZautttqYhwQAAAAAANhsbdBHkx1zzDGtnlcqlTz99NP57W9/mwsvvHCjDAYAAAAAALC526AQ09DQ0Op5u3btsuuuu2bs2LEZMmTIRhkMAAAAAABgc7dBIeZ73/vexp4DAAAAAABgi7NBIWat2bNn57HHHkuS7L777tl77703ylAAAAAAAABbgg0KMUuXLs3xxx+fGTNmpFu3bkmSZcuW5bDDDsuPf/zj7LDDDhtzRgAAAAAAgM1Suw150Zlnnpnnn38+jz76aJ599tk8++yzmTt3blpaWvKFL3xhY88IAAAAAACwWdqgO2KmTJmSX/7ylxkwYEB128CBA3P99ddnyJAhG204AAAAAACAzdkG3RGzZs2adOzYcZ3tHTt2zJo1a970UAAAAAAAAFuCDQox73//+3PWWWdl0aJF1W1//etfM2rUqHzgAx/YaMMBAAAAAABszjYoxHzjG99IS0tL+vXrl7e//e15+9vfnv79+6elpSXXXXfdxp4RAAAAAABgs7RB3xHTp0+fPPzww/nlL3+ZefPmJUkGDBiQwYMHb9ThAAAAAAAANmfrdUfM3XffnYEDB6alpSV1dXX54Ac/mDPPPDNnnnlm9t9//+y+++751a9+VWpWAAAAAACAzcp6hZirr746p512Wurr69fZ19DQkM997nO58sorN9pwAAAAAAAAm7P1CjG/+93v8qEPfehf7h8yZEhmz579pocCAAAAAADYEqxXiFmyZEk6duz4L/d36NAhzzzzzJseCgAAAAAAYEuwXiHmP/7jPzJ37tx/uf/3v/99dtxxxzc9FAAAAAAAwJZgvULMhz/84Vx44YV5+eWX19n30ksv5eKLL84RRxyx0YYDAAAAAADYnHVYn8UXXHBBfvazn+Wd73xnRowYkV133TVJMm/evFx//fVZvXp1/s//+T9FBgUAAAAAANjcrFeIaWxszH333Zczzjgj559/fiqVSpKkrq4uzc3Nuf7669PY2FhkUAAAAAAAgM3NeoWYJNl5553z//7f/8tzzz2XP/7xj6lUKnnHO96R7bbbrsR8AAAAAAAAm631DjFrbbfddtl///035iwAAAAAAABblHa1HgAAAAAAAGBLJcQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUIsQAAAAAAAAUUtMQM27cuOy///7Zdttt07Nnzxx99NGZP39+qzUvv/xyhg8fnh49emSbbbbJsccemyVLlrRas2DBggwdOjRbb711evbsmXPOOSevvPJKqzUzZszIPvvsk86dO2eXXXbJxIkTS58eAAAAAADQxtU0xMycOTPDhw/P/fffn2nTpmXVqlUZMmRIXnzxxeqaUaNG5Y477shPf/rTzJw5M4sWLcoxxxxT3b969eoMHTo0K1euzH333Zebb745EydOzEUXXVRd8+STT2bo0KE57LDDMmfOnIwcOTKf+cxnMnXq1E16vgAAAAAAQNtSV6lUKrUeYq1nnnkmPXv2zMyZM3PIIYdk+fLl2WGHHXLLLbfkuOOOS5LMmzcvAwYMyKxZs3LggQfmrrvuyhFHHJFFixalsbExSTJhwoSMGTMmzzzzTDp16pQxY8Zk8uTJmTt3bvW9jj/++CxbtixTpkx5w7laWlrS0NCQ5cuXp76+vszJb8b6nTe51iOwkTw1fmitRwAAAAAAeMtbn27wlvqOmOXLlydJunfvniSZPXt2Vq1alcGDB1fX7Lbbbunbt29mzZqVJJk1a1YGDRpUjTBJ0tzcnJaWljz66KPVNa8+xto1a4/xz1asWJGWlpZWDwAAAAAAgPX1lgkxa9asyciRI3PQQQflXe96V5Jk8eLF6dSpU7p169ZqbWNjYxYvXlxd8+oIs3b/2n2vt6alpSUvvfTSOrOMGzcuDQ0N1UefPn02yjkCAAAAAABty1smxAwfPjxz587Nj3/841qPkvPPPz/Lly+vPhYuXFjrkQAAAAAAgM1Qh1oPkCQjRozInXfemXvvvTc77bRTdXuvXr2ycuXKLFu2rNVdMUuWLEmvXr2qax588MFWx1uyZEl139r/rt326jX19fXp0qXLOvN07tw5nTt33ijnBgAAAAAAtF01vSOmUqlkxIgRmTRpUu6+++7079+/1f599903HTt2zPTp06vb5s+fnwULFqSpqSlJ0tTUlEceeSRLly6trpk2bVrq6+szcODA6ppXH2PtmrXHAAAAAAAAKKGmd8QMHz48t9xyS37+859n2223rX6nS0NDQ7p06ZKGhoaceuqpGT16dLp37576+vqceeaZaWpqyoEHHpgkGTJkSAYOHJgTTzwxl19+eRYvXpwLLrggw4cPr97Vcvrpp+cb3/hGzj333Jxyyim5++67c+utt2by5Mk1O3cAAAAAAGDLV9M7Ym688cYsX748hx56aHbcccfq4yc/+Ul1zVVXXZUjjjgixx57bA455JD06tUrP/vZz6r727dvnzvvvDPt27dPU1NTPvWpT+Wkk07K2LFjq2v69++fyZMnZ9q0adlzzz1zxRVX5Dvf+U6am5s36fkCAAAAAABtS12lUqnUeoi3upaWljQ0NGT58uWpr6+v9ThvOf3Oc2fRluKp8UNrPQIAAAAAwFve+nSDmt4RAwAAAAAAsCUTYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAAoRYgAAAAAAAArpUOsBgLat33mTaz0CG8lT44fWegQAAAAAeMtxRwwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhQgwAAAAAAEAhNQ0x9957b4488sj07t07dXV1ue2221rtr1Qqueiii7LjjjumS5cuGTx4cB5//PFWa5599tmccMIJqa+vT7du3XLqqafmhRdeaLXm97//fd773vdmq622Sp8+fXL55ZeXPjUAAAAAAIDahpgXX3wxe+65Z66//vrX3H/55Zfn2muvzYQJE/LAAw+ka9euaW5uzssvv1xdc8IJJ+TRRx/NtGnTcuedd+bee+/NZz/72er+lpaWDBkyJDvvvHNmz56dr33ta7nkkkvyrW99q/j5AQAAAAAAbVuHWr754YcfnsMPP/w191UqlVx99dW54IILctRRRyVJ/u///b9pbGzMbbfdluOPPz6PPfZYpkyZkoceeij77bdfkuS6667Lhz/84Xz9619P796988Mf/jArV67MTTfdlE6dOmX33XfPnDlzcuWVV7YKNgAAAAAAABvbW/Y7Yp588sksXrw4gwcPrm5raGjIAQcckFmzZiVJZs2alW7dulUjTJIMHjw47dq1ywMPPFBdc8ghh6RTp07VNc3NzZk/f36ee+65TXQ2AAAAAABAW1TTO2Jez+LFi5MkjY2NrbY3NjZW9y1evDg9e/Zstb9Dhw7p3r17qzX9+/df5xhr92233XbrvPeKFSuyYsWK6vOWlpY3eTYAAAAAAEBb9Ja9I6aWxo0bl4aGhuqjT58+tR4JAAAAAADYDL1lQ0yvXr2SJEuWLGm1fcmSJdV9vXr1ytKlS1vtf+WVV/Lss8+2WvNax3j1e/yz888/P8uXL68+Fi5c+OZPCAAAAAAAaHPesiGmf//+6dWrV6ZPn17d1tLSkgceeCBNTU1JkqampixbtiyzZ8+urrn77ruzZs2aHHDAAdU19957b1atWlVdM23atOy6666v+bFkSdK5c+fU19e3egAAAAAAAKyvmoaYF154IXPmzMmcOXOSJE8++WTmzJmTBQsWpK6uLiNHjsyXv/zl3H777XnkkUdy0kknpXfv3jn66KOTJAMGDMiHPvShnHbaaXnwwQfzm9/8JiNGjMjxxx+f3r17J0n+8z//M506dcqpp56aRx99ND/5yU9yzTXXZPTo0TU6awAAAAAAoK3oUMs3/+1vf5vDDjus+nxtHBk2bFgmTpyYc889Ny+++GI++9nPZtmyZTn44IMzZcqUbLXVVtXX/PCHP8yIESPygQ98IO3atcuxxx6ba6+9trq/oaEhv/jFLzJ8+PDsu+++2X777XPRRRfls5/97KY7UQAAAAAAoE2qq1QqlVoP8VbX0tKShoaGLF++3MeUvYZ+502u9QhsJE+NH7rJ39P1s+WoxfUDAAAAALWwPt3gLfsdMQAAAAAAAJs7IQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKCQDrUeAAA2RL/zJtd6BDaSp8YPrfUIAAAAAMW4IwYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKAQIQYAAAAAAKCQDrUeAABgU+t33uRaj8BG8tT4obUeAQAAAF6XO2IAAAAAAAAKEWIAAAAAAAAKEWIAAAAAAAAKEWIAAAAAAAAKEWIAAAAAAAAKEWIAAAAAAAAKEWIAAAAAAAAK6VDrATal66+/Pl/72teyePHi7Lnnnrnuuuvy7ne/u9ZjAQCwGel33uRaj8BG8tT4obUeAQAAaAPazB0xP/nJTzJ69OhcfPHFefjhh7Pnnnumubk5S5curfVoAAAAAADAFqrNhJgrr7wyp512Wk4++eQMHDgwEyZMyNZbb52bbrqp1qMBAAAAAABbqDYRYlauXJnZs2dn8ODB1W3t2rXL4MGDM2vWrBpOBgAAAAAAbMnaxHfE/O///m9Wr16dxsbGVtsbGxszb968ddavWLEiK1asqD5fvnx5kqSlpaXsoJupNSv+XusR2EhqcY27frYcm/r6ce1sOfzs4c1w/fBm1OL6edfFUzf5e1LG3Eubaz0C/Nv87Nly+NkD8Nax9u8TlUrlDde2iRCzvsaNG5dLL710ne19+vSpwTSw6TRcXesJ2Jy5fthQrh3eDNcPb4brhzfD9QPUgp89AG89zz//fBoaGl53TZsIMdtvv33at2+fJUuWtNq+ZMmS9OrVa531559/fkaPHl19vmbNmjz77LPp0aNH6urqis9L29LS0pI+ffpk4cKFqa+vr/U4sEm5/mnLXP+0Va592jLXP22Z65+2zPVPW7WlX/uVSiXPP/98evfu/YZr20SI6dSpU/bdd99Mnz49Rx99dJJ/xJXp06dnxIgR66zv3LlzOnfu3Gpbt27dNsGktGX19fVb5A8k+He4/mnLXP+0Va592jLXP22Z65+2zPVPW7UlX/tvdCfMWm0ixCTJ6NGjM2zYsOy3335597vfnauvvjovvvhiTj755FqPBgAAAAAAbKHaTIj5xCc+kWeeeSYXXXRRFi9enL322itTpkxJY2NjrUcDAAAAAAC2UG0mxCTJiBEjXvOjyKCWOnfunIsvvnidj8ODtsD1T1vm+qetcu3Tlrn+actc/7Rlrn/aKtf+/6+uUqlUaj0EAAAAAADAlqhdrQcAAAAAAADYUgkxAAAAAAAAhQgxAAAAAAAAhQgxAAAAAAAAhQgxUCPjxo3L/vvvn2233TY9e/bM0Ucfnfnz59d6LNjkxo8fn7q6uowcObLWo8Am8de//jWf+tSn0qNHj3Tp0iWDBg3Kb3/721qPBcWtXr06F154Yfr3758uXbrk7W9/e770pS+lUqnUejTY6O69994ceeSR6d27d+rq6nLbbbe12l+pVHLRRRdlxx13TJcuXTJ48OA8/vjjtRkWNrLXu/5XrVqVMWPGZNCgQenatWt69+6dk046KYsWLardwLCRvNHP/lc7/fTTU1dXl6uvvnqTzQcl/TvX/2OPPZaPfOQjaWhoSNeuXbP//vtnwYIFm37YGhFioEZmzpyZ4cOH5/7778+0adOyatWqDBkyJC+++GKtR4NN5qGHHso3v/nN7LHHHrUeBTaJ5557LgcddFA6duyYu+66K3/4wx9yxRVXZLvttqv1aFDcZZddlhtvvDHf+MY38thjj+Wyyy7L5Zdfnuuuu67Wo8FG9+KLL2bPPffM9ddf/5r7L7/88lx77bWZMGFCHnjggXTt2jXNzc15+eWXN/GksPG93vX/97//PQ8//HAuvPDCPPzww/nZz36W+fPn5yMf+UgNJoWN641+9q81adKk3H///endu/cmmgzKe6Pr/4knnsjBBx+c3XbbLTNmzMjvf//7XHjhhdlqq6028aS1U1fxT9DgLeGZZ55Jz549M3PmzBxyyCG1HgeKe+GFF7LPPvvkhhtuyJe//OXstdde/jUQW7zzzjsvv/nNb/KrX/2q1qPAJnfEEUeksbEx3/3ud6vbjj322HTp0iU/+MEPajgZlFVXV5dJkybl6KOPTvKPu2F69+6ds88+O1/84heTJMuXL09jY2MmTpyY448/vobTwsb1z9f/a3nooYfy7ne/O3/+85/Tt2/fTTccFPSvrv2//vWvOeCAAzJ16tQMHTo0I0eO9OkQbHFe6/o//vjj07Fjx3z/+9+v3WA15o4YeItYvnx5kqR79+41ngQ2jeHDh2fo0KEZPHhwrUeBTeb222/Pfvvtl4997GPp2bNn9t5773z729+u9ViwSbznPe/J9OnT8z//8z9Jkt/97nf59a9/ncMPP7zGk8Gm9eSTT2bx4sWt/jdQQ0NDDjjggMyaNauGk0FtLF++PHV1denWrVutR4Gi1qxZkxNPPDHnnHNOdt9991qPA5vMmjVrMnny5Lzzne9Mc3NzevbsmQMOOOB1P75vSyTEwFvAmjVrMnLkyBx00EF517veVetxoLgf//jHefjhhzNu3LhajwKb1J/+9KfceOONecc73pGpU6fmjDPOyBe+8IXcfPPNtR4NijvvvPNy/PHHZ7fddkvHjh2z9957Z+TIkTnhhBNqPRpsUosXL06SNDY2ttre2NhY3Qdtxcsvv5wxY8bkk5/8ZOrr62s9DhR12WWXpUOHDvnCF75Q61Fgk1q6dGleeOGFjB8/Ph/60Ifyi1/8Ih/96EdzzDHHZObMmbUeb5PpUOsBgH/cGTB37tz8+te/rvUoUNzChQtz1llnZdq0aW3qs0Ah+Ud432+//fLVr341SbL33ntn7ty5mTBhQoYNG1bj6aCsW2+9NT/84Q9zyy23ZPfdd8+cOXMycuTI9O7d2/UP0AatWrUqH//4x1OpVHLjjTfWehwoavbs2bnmmmvy8MMPp66urtbjwCa1Zs2aJMlRRx2VUaNGJUn22muv3HfffZkwYULe97731XK8TcYdMVBjI0aMyJ133pl77rknO+20U63HgeJmz56dpUuXZp999kmHDh3SoUOHzJw5M9dee206dOiQ1atX13pEKGbHHXfMwIEDW20bMGBAFixYUKOJYNM555xzqnfFDBo0KCeeeGJGjRrl7kjanF69eiVJlixZ0mr7kiVLqvtgS7c2wvz5z3/OtGnT3A3DFu9Xv/pVli5dmr59+1b/HvznP/85Z599dvr161fr8aCo7bffPh06dGjzfxd2RwzUSKVSyZlnnplJkyZlxowZ6d+/f61Hgk3iAx/4QB555JFW204++eTstttuGTNmTNq3b1+jyaC8gw46KPPnz2+17X/+53+y884712gi2HT+/ve/p1271v8OrH379tV/IQdtRf/+/dOrV69Mnz49e+21V5KkpaUlDzzwQM4444zaDgebwNoI8/jjj+eee+5Jjx49aj0SFHfiiSeu8/2ozc3NOfHEE3PyySfXaCrYNDp16pT999+/zf9dWIiBGhk+fHhuueWW/PznP8+2225b/TzohoaGdOnSpcbTQTnbbrvtOt+F1LVr1/To0cN3JLHFGzVqVN7znvfkq1/9aj7+8Y/nwQcfzLe+9a1861vfqvVoUNyRRx6Zr3zlK+nbt2923333/Pd//3euvPLKnHLKKbUeDTa6F154IX/84x+rz5988snMmTMn3bt3T9++fTNy5Mh8+ctfzjve8Y70798/F154YXr37p2jjz66dkPDRvJ61/+OO+6Y4447Lg8//HDuvPPOrF69uvp34e7du6dTp061GhvetDf62f/P0bFjx47p1atXdt111009Kmx0b3T9n3POOfnEJz6RQw45JIcddlimTJmSO+64IzNmzKjd0JtYXaVSqdR6CGiL/tVngn7ve9/Lpz/96U07DNTYoYcemr322itXX311rUeB4u68886cf/75efzxx9O/f/+MHj06p512Wq3HguKef/75XHjhhZk0aVKWLl2a3r1755Of/GQuuugi/8cbW5wZM2bksMMOW2f7sGHDMnHixFQqlVx88cX51re+lWXLluXggw/ODTfckHe+8501mBY2rte7/i+55JJ/+WkQ99xzTw499NDC00E5b/Sz/5/169cvI0eOzMiRI8sPB4X9O9f/TTfdlHHjxuUvf/lLdt1111x66aU56qijNvGktSPEAAAAAAAAFNLujZcAAAAAAACwIYQYAAAAAACAQoQYAAAAAACAQoQYAAAAAACAQoQYAAAAAACAQoQYAAAAAACAQoQYAAAAAACAQoQYAACg5p566qnU1dVlzpw5tR6lat68eTnwwAOz1VZbZa+99nrNNYceemhGjhxZ5P1LHhsAANh0hBgAACCf/vSnU1dXl/Hjx7faftttt6Wurq5GU9XWxRdfnK5du2b+/PmZPn16sfeZMWNG6urqsmzZsmLvAQAA1I4QAwAAJEm22mqrXHbZZXnuuedqPcpGs3Llyg1+7RNPPJGDDz44O++8c3r06LERpypn1apVtR4BAAD4J0IMAACQJBk8eHB69eqVcePG/cs1l1xyyTof03X11VenX79+1eef/vSnc/TRR+erX/1qGhsb061bt4wdOzavvPJKzjnnnHTv3j077bRTvve9761z/Hnz5uU973lPttpqq7zrXe/KzJkzW+2fO3duDj/88GyzzTZpbGzMiSeemP/93/+t7j/00EMzYsSIjBw5Mttvv32am5tf8zzWrFmTsWPHZqeddkrnzp2z1157ZcqUKdX9dXV1mT17dsaOHZu6urpccskl//L35JVXXsmIESPS0NCQ7bffPhdeeGEqlUp1//e///3st99+2XbbbdOrV6/853/+Z5YuXZrkHx/JdthhhyVJtttuu9TV1eXTn/50qznPPffcdO/ePb169Vpnjrq6utx44435yEc+kq5du+YrX/lKkuTGG2/M29/+9nTq1Cm77rprvv/977d63YIFC3LUUUdlm222SX19fT7+8Y9nyZIl1f1r/5xvuumm9O3bN9tss00+//nPZ/Xq1bn88svTq1ev9OzZs/p+SVKpVHLJJZekb9++6dy5c3r37p0vfOEL//L3DQAA2gohBgAASJK0b98+X/3qV3PdddflL3/5y5s61t13351Fixbl3nvvzZVXXpmLL744RxxxRLbbbrs88MADOf300/O5z31unfc555xzcvbZZ+e///u/09TUlCOPPDJ/+9vfkiTLli3L+9///uy999757W9/mylTpmTJkiX5+Mc/3uoYN998czp16pTf/OY3mTBhwmvOd8011+SKK67I17/+9fz+979Pc3NzPvKRj+Txxx9Pkjz99NPZfffdc/bZZ+fpp5/OF7/4xX95rjfffHM6dOiQBx98MNdcc02uvPLKfOc736nuX7VqVb70pS/ld7/7XW677bY89dRT1djSp0+f/Nd//VeSZP78+Xn66adzzTXXtDp2165d88ADD+Tyyy/P2LFjM23atFbvf8kll+SjH/1oHnnkkZxyyimZNGlSzjrrrJx99tmZO3duPve5z+Xkk0/OPffck+Qfceeoo47Ks88+m5kzZ2batGn505/+lE984hOtjvvEE0/krrvuypQpU/KjH/0o3/3udzN06ND85S9/ycyZM3PZZZflggsuyAMPPJAk+a//+q9cddVV+eY3v5nHH388t912WwYNGvQvf98AAKDNqAAAAG3esGHDKkcddVSlUqlUDjzwwMopp5xSqVQqlUmTJlVe/deGiy++uLLnnnu2eu1VV11V2XnnnVsda+edd66sXr26um3XXXetvPe9760+f+WVVypdu3at/OhHP6pUKpXKk08+WUlSGT9+fHXNqlWrKjvttFPlsssuq1QqlcqXvvSlypAhQ1q998KFCytJKvPnz69UKpXK+973vsree+/9hufbu3fvyle+8pVW2/bff//K5z//+erzPffcs3LxxRe/7nHe9773VQYMGFBZs2ZNdduYMWMqAwYM+JeveeihhypJKs8//3ylUqlU7rnnnkqSynPPPbfOsQ8++OB1ZhwzZkz1eZLKyJEjW615z3veUznttNNabfvYxz5W+fCHP1ypVCqVX/ziF5X27dtXFixYUN3/6KOPVpJUHnzwwUql8o8/56233rrS0tJSXdPc3Fzp16/fOn+u48aNq1QqlcoVV1xReec731lZuXLlvzx3AABoi9wRAwAAtHLZZZfl5ptvzmOPPbbBx9h9993Trt3//9eNxsbGVndHtG/fPj169Kh+RNdaTU1N1V936NAh++23X3WO3/3ud7nnnnuyzTbbVB+77bZbkn/cvbHWvvvu+7qztbS0ZNGiRTnooINabT/ooIM26JwPPPDA1NXVtTqHxx9/PKtXr06SzJ49O0ceeWT69u2bbbfdNu973/uS/OPjwd7IHnvs0er5jjvuuM7v2X777dfq+WOPPfa65/bYY4+lT58+6dOnT3X/wIED061bt1bn369fv2y77bbV542NjRk4cOA6f65r5/nYxz6Wl156KW9729ty2mmnZdKkSXnllVfe8BwBAGBLJ8QAAACtHHLIIWlubs7555+/zr527dq1+v6T5LW/IL5jx46tntfV1b3mtjVr1vzbc73wwgs58sgjM2fOnFaPxx9/PIccckh1XdeuXf/tY5b24osvprm5OfX19fnhD3+Yhx56KJMmTUqSrFy58g1f/+/8npU63/X9M+zTp0/mz5+fG264IV26dMnnP//5HHLIIa95fQAAQFsixAAAAOsYP3587rjjjsyaNavV9h122CGLFy9uFWPmzJmz0d73/vvvr/76lVdeyezZszNgwIAkyT777JNHH300/fr1yy677NLqsT4xor6+Pr17985vfvObVtt/85vfZODAges989rvSHn1ObzjHe9I+/btM2/evPztb3/L+PHj8973vje77bbbOne0dOrUKUmqd9C8WQMGDHjdcxswYEAWLlyYhQsXVvf/4Q9/yLJlyzbo/F+tS5cuOfLII3PttddmxowZmTVrVh555JE3dUwAANjcCTEAAMA6Bg0alBNOOCHXXnttq+2HHnponnnmmVx++eV54okncv311+euu+7aaO97/fXXZ9KkSZk3b16GDx+e5557LqecckqSZPjw4Xn22WfzyU9+Mg899FCeeOKJTJ06NSeffPJ6R4xzzjknl112WX7yk59k/vz5Oe+88zJnzpycddZZ6z3zggULMnr06MyfPz8/+tGPct1111WP07dv33Tq1CnXXXdd/vSnP+X222/Pl770pVav33nnnVNXV5c777wzzzzzTF544YX1nuGfz23ixIm58cYb8/jjj+fKK6/Mz372s3zxi19MkgwePLj65/vwww/nwQcfzEknnZT3ve9963zM2fqYOHFivvvd72bu3Ln505/+lB/84Afp0qVLdt555zd1PgAAsLkTYgAAgNc0duzYdT4Ga8CAAbnhhhty/fXXZ88998yDDz5Y/T/4N4bx48dn/Pjx2XPPPfPrX/86t99+e7bffvskqd7Fsnr16gwZMiSDBg3KyJEj061bt1bfW/Lv+MIXvpDRo0fn7LPPzqBBgzJlypTcfvvtecc73rHeM5900kl56aWX8u53vzvDhw/PWWedlc9+9rNJ/nEH0cSJE/PTn/40AwcOzPjx4/P1r3+91ev/4z/+I5deemnOO++8NDY2ZsSIEes9w6sdffTRueaaa/L1r389u+++e775zW/me9/7Xg499NAk//g4sZ///OfZbrvtcsghh2Tw4MF529velp/85Cdv6n27deuWb3/72znooIOyxx575Je//GXuuOOO9OjR400dFwAANnd1lX/+gGcAAAAAAAA2CnfEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFCLEAAAAAAAAFPL/Aeo4z5KFrmTgAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.hist(df8.bath,rwidth=0.8)\n",
        "plt.xlabel(\"Number of bathrooms\")\n",
        "plt.ylabel(\"Count\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "a4d8cc0b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a4d8cc0b",
        "outputId": "e217c656-0425-4326-b6e3-3a1b03b613d8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            location    size  total_sqft  bath  price  BHK  price_per_sqft\n",
              "5277  Neeladri Nagar  10 BHK      4000.0  12.0  160.0   10     4000.000000\n",
              "8486           other  10 BHK     12000.0  12.0  525.0   10     4375.000000\n",
              "8575           other  16 BHK     10000.0  16.0  550.0   16     5500.000000\n",
              "9308           other  11 BHK      6000.0  12.0  150.0   11     2500.000000\n",
              "9639           other  13 BHK      5425.0  13.0  275.0   13     5069.124424"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe10caac-212f-4201-9441-d2a66f8b2c6c\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5277</th>\n",
              "      <td>Neeladri Nagar</td>\n",
              "      <td>10 BHK</td>\n",
              "      <td>4000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>10</td>\n",
              "      <td>4000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8486</th>\n",
              "      <td>other</td>\n",
              "      <td>10 BHK</td>\n",
              "      <td>12000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>525.0</td>\n",
              "      <td>10</td>\n",
              "      <td>4375.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8575</th>\n",
              "      <td>other</td>\n",
              "      <td>16 BHK</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>550.0</td>\n",
              "      <td>16</td>\n",
              "      <td>5500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9308</th>\n",
              "      <td>other</td>\n",
              "      <td>11 BHK</td>\n",
              "      <td>6000.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>11</td>\n",
              "      <td>2500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9639</th>\n",
              "      <td>other</td>\n",
              "      <td>13 BHK</td>\n",
              "      <td>5425.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>275.0</td>\n",
              "      <td>13</td>\n",
              "      <td>5069.124424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe10caac-212f-4201-9441-d2a66f8b2c6c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fe10caac-212f-4201-9441-d2a66f8b2c6c 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-fe10caac-212f-4201-9441-d2a66f8b2c6c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-68fa9838-5a9f-49a8-8e97-f40c87c8d81e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-68fa9838-5a9f-49a8-8e97-f40c87c8d81e')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-68fa9838-5a9f-49a8-8e97-f40c87c8d81e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "df8[df8.bath>10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "b71df73f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "b71df73f",
        "outputId": "1390c2d9-580e-4fae-b768-fb991fd2e74d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           location       size  total_sqft  bath   price  BHK  price_per_sqft\n",
              "1626  Chikkabanavar  4 Bedroom      2460.0   7.0    80.0    4     3252.032520\n",
              "5238     Nagasandra  4 Bedroom      7000.0   8.0   450.0    4     6428.571429\n",
              "6711    Thanisandra      3 BHK      1806.0   6.0   116.0    3     6423.034330\n",
              "8411          other      6 BHK     11338.0   9.0  1000.0    6     8819.897689"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83359c2b-9193-497e-a55c-a4554b3dfe01\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1626</th>\n",
              "      <td>Chikkabanavar</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>2460.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3252.032520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5238</th>\n",
              "      <td>Nagasandra</td>\n",
              "      <td>4 Bedroom</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>4</td>\n",
              "      <td>6428.571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6711</th>\n",
              "      <td>Thanisandra</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1806.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>116.0</td>\n",
              "      <td>3</td>\n",
              "      <td>6423.034330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8411</th>\n",
              "      <td>other</td>\n",
              "      <td>6 BHK</td>\n",
              "      <td>11338.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>6</td>\n",
              "      <td>8819.897689</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83359c2b-9193-497e-a55c-a4554b3dfe01')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-83359c2b-9193-497e-a55c-a4554b3dfe01 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-83359c2b-9193-497e-a55c-a4554b3dfe01');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-aa3c76db-09b1-4e1b-a17f-087d6614590c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-aa3c76db-09b1-4e1b-a17f-087d6614590c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-aa3c76db-09b1-4e1b-a17f-087d6614590c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df8[df8.bath>df8.BHK+2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "id": "37e60779",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37e60779",
        "outputId": "ed77e9c8-b516-4cc7-b98e-872e8835a079"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7251, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df9 = df8[df8.bath<df8.BHK+2]\n",
        "df9.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "id": "b3965162",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "b3965162",
        "outputId": "f9bda44a-6a2e-4a0f-dfe3-e3d2346efb2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              location   size  total_sqft  bath  price  BHK  price_per_sqft\n",
              "0  1st Block Jayanagar  4 BHK      2850.0   4.0  428.0    4    15017.543860\n",
              "1  1st Block Jayanagar  3 BHK      1630.0   3.0  194.0    3    11901.840491"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4fda63ad-a352-4f6f-a0f6-8342ec8c2370\" 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>location</th>\n",
              "      <th>size</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>price_per_sqft</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>4 BHK</td>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
              "      <td>15017.543860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>3 BHK</td>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
              "      <td>11901.840491</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4fda63ad-a352-4f6f-a0f6-8342ec8c2370')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4fda63ad-a352-4f6f-a0f6-8342ec8c2370 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-4fda63ad-a352-4f6f-a0f6-8342ec8c2370');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-210b6b48-8f04-4837-9746-b3e01989d169\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-210b6b48-8f04-4837-9746-b3e01989d169')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-210b6b48-8f04-4837-9746-b3e01989d169 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "df9.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "6c4407bb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "6c4407bb",
        "outputId": "d8be5d2d-bf9e-471d-e497-2e622d2d5a8b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              location  total_sqft  bath  price  BHK\n",
              "0  1st Block Jayanagar      2850.0   4.0  428.0    4\n",
              "1  1st Block Jayanagar      1630.0   3.0  194.0    3\n",
              "2  1st Block Jayanagar      1875.0   2.0  235.0    3"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-96216261-a1d4-4ed2-a03b-b1a2ea1da151\" 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>location</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-96216261-a1d4-4ed2-a03b-b1a2ea1da151')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-96216261-a1d4-4ed2-a03b-b1a2ea1da151 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-96216261-a1d4-4ed2-a03b-b1a2ea1da151');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bc8740dc-6f0f-4b87-8b6a-3f0914c91c58\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc8740dc-6f0f-4b87-8b6a-3f0914c91c58')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bc8740dc-6f0f-4b87-8b6a-3f0914c91c58 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df10 = df9.drop(['size','price_per_sqft'],axis='columns')\n",
        "df10.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "2f8205d6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "2f8205d6",
        "outputId": "d8c7187a-6414-40ea-8fdc-53bb8720d982"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   1st Block Jayanagar  1st Phase JP Nagar  2nd Phase Judicial Layout  \\\n",
              "0                    1                   0                          0   \n",
              "1                    1                   0                          0   \n",
              "2                    1                   0                          0   \n",
              "\n",
              "   2nd Stage Nagarbhavi  5th Block Hbr Layout  5th Phase JP Nagar  \\\n",
              "0                     0                     0                   0   \n",
              "1                     0                     0                   0   \n",
              "2                     0                     0                   0   \n",
              "\n",
              "   6th Phase JP Nagar  7th Phase JP Nagar  8th Phase JP Nagar  \\\n",
              "0                   0                   0                   0   \n",
              "1                   0                   0                   0   \n",
              "2                   0                   0                   0   \n",
              "\n",
              "   9th Phase JP Nagar  ...  Vishveshwarya Layout  Vishwapriya Layout  \\\n",
              "0                   0  ...                     0                   0   \n",
              "1                   0  ...                     0                   0   \n",
              "2                   0  ...                     0                   0   \n",
              "\n",
              "   Vittasandra  Whitefield  Yelachenahalli  Yelahanka  Yelahanka New Town  \\\n",
              "0            0           0               0          0                   0   \n",
              "1            0           0               0          0                   0   \n",
              "2            0           0               0          0                   0   \n",
              "\n",
              "   Yelenahalli  Yeshwanthpur  other  \n",
              "0            0             0      0  \n",
              "1            0             0      0  \n",
              "2            0             0      0  \n",
              "\n",
              "[3 rows x 242 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4ffc65b9-c030-469a-8a28-1760aa97dae1\" 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>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>7th Phase JP Nagar</th>\n",
              "      <th>8th Phase JP Nagar</th>\n",
              "      <th>9th Phase JP Nagar</th>\n",
              "      <th>...</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "      <th>other</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 242 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ffc65b9-c030-469a-8a28-1760aa97dae1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4ffc65b9-c030-469a-8a28-1760aa97dae1 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-4ffc65b9-c030-469a-8a28-1760aa97dae1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-587663be-0b0b-48a9-bfc3-46faaafa143c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-587663be-0b0b-48a9-bfc3-46faaafa143c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-587663be-0b0b-48a9-bfc3-46faaafa143c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "dummies = pd.get_dummies(df10.location)\n",
        "dummies.head(3)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dummies.to_csv('cols.csv')"
      ],
      "metadata": {
        "id": "4BTyqEIFMl_2"
      },
      "id": "4BTyqEIFMl_2",
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "dc1edb7c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "dc1edb7c",
        "outputId": "8278bf8b-c8fe-4aaf-a0d7-6031ec78c0a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              location  total_sqft  bath  price  BHK  1st Block Jayanagar  \\\n",
              "0  1st Block Jayanagar      2850.0   4.0  428.0    4                    1   \n",
              "1  1st Block Jayanagar      1630.0   3.0  194.0    3                    1   \n",
              "2  1st Block Jayanagar      1875.0   2.0  235.0    3                    1   \n",
              "3  1st Block Jayanagar      1200.0   2.0  130.0    3                    1   \n",
              "4  1st Block Jayanagar      1235.0   2.0  148.0    2                    1   \n",
              "\n",
              "   1st Phase JP Nagar  2nd Phase Judicial Layout  2nd Stage Nagarbhavi  \\\n",
              "0                   0                          0                     0   \n",
              "1                   0                          0                     0   \n",
              "2                   0                          0                     0   \n",
              "3                   0                          0                     0   \n",
              "4                   0                          0                     0   \n",
              "\n",
              "   5th Block Hbr Layout  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
              "0                     0  ...            0                     0   \n",
              "1                     0  ...            0                     0   \n",
              "2                     0  ...            0                     0   \n",
              "3                     0  ...            0                     0   \n",
              "4                     0  ...            0                     0   \n",
              "\n",
              "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
              "0                   0            0           0               0          0   \n",
              "1                   0            0           0               0          0   \n",
              "2                   0            0           0               0          0   \n",
              "3                   0            0           0               0          0   \n",
              "4                   0            0           0               0          0   \n",
              "\n",
              "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
              "0                   0            0             0  \n",
              "1                   0            0             0  \n",
              "2                   0            0             0  \n",
              "3                   0            0             0  \n",
              "4                   0            0             0  \n",
              "\n",
              "[5 rows x 246 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1587f3c1-2d1b-4bae-9628-cc64133c4187\" 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>location</th>\n",
              "      <th>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>...</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>130.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1st Block Jayanagar</td>\n",
              "      <td>1235.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>148.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 246 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1587f3c1-2d1b-4bae-9628-cc64133c4187')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1587f3c1-2d1b-4bae-9628-cc64133c4187 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-1587f3c1-2d1b-4bae-9628-cc64133c4187');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ad9b4d6f-3aa0-4392-9dd6-2126d5008e00\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ad9b4d6f-3aa0-4392-9dd6-2126d5008e00')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ad9b4d6f-3aa0-4392-9dd6-2126d5008e00 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "df11 = pd.concat([df10,dummies.drop('other',axis='columns')],axis='columns')\n",
        "df11.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "0ae5afda",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "0ae5afda",
        "outputId": "87856666-226a-45af-dad6-9c1ead3ab88c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_sqft  bath  price  BHK  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
              "0      2850.0   4.0  428.0    4                    1                   0   \n",
              "1      1630.0   3.0  194.0    3                    1                   0   \n",
              "\n",
              "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
              "0                          0                     0                     0   \n",
              "1                          0                     0                     0   \n",
              "\n",
              "   5th Phase JP Nagar  ...  Vijayanagar  Vishveshwarya Layout  \\\n",
              "0                   0  ...            0                     0   \n",
              "1                   0  ...            0                     0   \n",
              "\n",
              "   Vishwapriya Layout  Vittasandra  Whitefield  Yelachenahalli  Yelahanka  \\\n",
              "0                   0            0           0               0          0   \n",
              "1                   0            0           0               0          0   \n",
              "\n",
              "   Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
              "0                   0            0             0  \n",
              "1                   0            0             0  \n",
              "\n",
              "[2 rows x 245 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2bf36834-6e4e-4db5-a5e7-5f9916074989\" 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>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>price</th>\n",
              "      <th>BHK</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>...</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>428.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>194.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 245 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2bf36834-6e4e-4db5-a5e7-5f9916074989')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2bf36834-6e4e-4db5-a5e7-5f9916074989 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-2bf36834-6e4e-4db5-a5e7-5f9916074989');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b19e3fc2-1802-476f-bf8e-015d7d5ecdfe\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b19e3fc2-1802-476f-bf8e-015d7d5ecdfe')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b19e3fc2-1802-476f-bf8e-015d7d5ecdfe button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "df12 = df11.drop('location',axis='columns')\n",
        "df12.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "c64ab39f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c64ab39f",
        "outputId": "32856bb3-2d7d-4343-959e-04ae1fade361"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7251, 245)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "df12.shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "61550c89",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "61550c89",
        "outputId": "3e9836fc-6604-473a-f17c-802b0225e4e6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_sqft  bath  BHK  1st Block Jayanagar  1st Phase JP Nagar  \\\n",
              "0      2850.0   4.0    4                    1                   0   \n",
              "1      1630.0   3.0    3                    1                   0   \n",
              "2      1875.0   2.0    3                    1                   0   \n",
              "\n",
              "   2nd Phase Judicial Layout  2nd Stage Nagarbhavi  5th Block Hbr Layout  \\\n",
              "0                          0                     0                     0   \n",
              "1                          0                     0                     0   \n",
              "2                          0                     0                     0   \n",
              "\n",
              "   5th Phase JP Nagar  6th Phase JP Nagar  ...  Vijayanagar  \\\n",
              "0                   0                   0  ...            0   \n",
              "1                   0                   0  ...            0   \n",
              "2                   0                   0  ...            0   \n",
              "\n",
              "   Vishveshwarya Layout  Vishwapriya Layout  Vittasandra  Whitefield  \\\n",
              "0                     0                   0            0           0   \n",
              "1                     0                   0            0           0   \n",
              "2                     0                   0            0           0   \n",
              "\n",
              "   Yelachenahalli  Yelahanka  Yelahanka New Town  Yelenahalli  Yeshwanthpur  \n",
              "0               0          0                   0            0             0  \n",
              "1               0          0                   0            0             0  \n",
              "2               0          0                   0            0             0  \n",
              "\n",
              "[3 rows x 244 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4368c0e-ac88-4398-b954-717e6638ad6a\" 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>total_sqft</th>\n",
              "      <th>bath</th>\n",
              "      <th>BHK</th>\n",
              "      <th>1st Block Jayanagar</th>\n",
              "      <th>1st Phase JP Nagar</th>\n",
              "      <th>2nd Phase Judicial Layout</th>\n",
              "      <th>2nd Stage Nagarbhavi</th>\n",
              "      <th>5th Block Hbr Layout</th>\n",
              "      <th>5th Phase JP Nagar</th>\n",
              "      <th>6th Phase JP Nagar</th>\n",
              "      <th>...</th>\n",
              "      <th>Vijayanagar</th>\n",
              "      <th>Vishveshwarya Layout</th>\n",
              "      <th>Vishwapriya Layout</th>\n",
              "      <th>Vittasandra</th>\n",
              "      <th>Whitefield</th>\n",
              "      <th>Yelachenahalli</th>\n",
              "      <th>Yelahanka</th>\n",
              "      <th>Yelahanka New Town</th>\n",
              "      <th>Yelenahalli</th>\n",
              "      <th>Yeshwanthpur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2850.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1630.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1875.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 244 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4368c0e-ac88-4398-b954-717e6638ad6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c4368c0e-ac88-4398-b954-717e6638ad6a 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-c4368c0e-ac88-4398-b954-717e6638ad6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-095b8115-73d9-4038-b2e3-80835cd2163f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-095b8115-73d9-4038-b2e3-80835cd2163f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-095b8115-73d9-4038-b2e3-80835cd2163f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "X = df12.drop(['price'],axis='columns')\n",
        "X.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "1121c77a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1121c77a",
        "outputId": "9623bf44-e8b3-479a-af6c-75395d2d5e15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7251, 244)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df12.to_csv(\"output.csv\")"
      ],
      "metadata": {
        "id": "iNmj8R7H3vVG"
      },
      "id": "iNmj8R7H3vVG",
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df12['location']"
      ],
      "metadata": {
        "id": "d3ArEzIZ6TfA",
        "outputId": "45eb7649-3d4a-4a77-912f-ddfc724b5bdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "id": "d3ArEzIZ6TfA",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'location'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-2de9faf41839>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf12\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'location'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'location'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "e9fca6dd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9fca6dd",
        "outputId": "4171dffa-5c0a-4f80-af21-db6429b5916b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    428.0\n",
              "1    194.0\n",
              "2    235.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "y = df12.price\n",
        "y.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "f6aa9a39",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f6aa9a39",
        "outputId": "18dcc27b-bcbb-4784-a3a1-223009723a26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7251"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "len(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "89a321df",
      "metadata": {
        "id": "89a321df"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "c652c34a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c652c34a",
        "outputId": "654f1b03-dfbc-4739-ef15-8c35158d9145"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8452277697874376"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "lr_clf = LinearRegression()\n",
        "lr_clf.fit(X_train,y_train)\n",
        "lr_clf.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "74b3961b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74b3961b",
        "outputId": "599f6239-ac5c-4ffd-9f9a-37f87dcdfc84"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.82430186, 0.77166234, 0.85089567, 0.80837764, 0.83653286])"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "from sklearn.model_selection import ShuffleSplit\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
        "\n",
        "cross_val_score(LinearRegression(), X, y, cv=cv)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "d867e574",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "d867e574",
        "outputId": "68369920-b372-42c2-bfa1-8baa0eba9728"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py:378: FitFailedWarning: \n",
            "10 fits failed out of a total of 20.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "10 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_validation.py\", line 686, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\", line 1247, in fit\n",
            "    super().fit(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/tree/_classes.py\", line 177, in fit\n",
            "    self._validate_params()\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/base.py\", line 600, in _validate_params\n",
            "    validate_parameter_constraints(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/sklearn/utils/_param_validation.py\", line 97, in validate_parameter_constraints\n",
            "    raise InvalidParameterError(\n",
            "sklearn.utils._param_validation.InvalidParameterError: The 'criterion' parameter of DecisionTreeRegressor must be a str among {'squared_error', 'friedman_mse', 'poisson', 'absolute_error'}. Got 'mse' instead.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/model_selection/_search.py:952: UserWarning: One or more of the test scores are non-finite: [       nan        nan 0.70950553 0.70991611]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               model  best_score  \\\n",
              "0  linear_regression    0.818354   \n",
              "1              lasso    0.687429   \n",
              "2      decision_tree    0.709916   \n",
              "\n",
              "                                         best_params  \n",
              "0                                                 {}  \n",
              "1                {'alpha': 1, 'selection': 'cyclic'}  \n",
              "2  {'criterion': 'friedman_mse', 'splitter': 'ran...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e702deb3-4bf2-4ffa-b8ee-2ca92e477e94\" 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>model</th>\n",
              "      <th>best_score</th>\n",
              "      <th>best_params</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>linear_regression</td>\n",
              "      <td>0.818354</td>\n",
              "      <td>{}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>lasso</td>\n",
              "      <td>0.687429</td>\n",
              "      <td>{'alpha': 1, 'selection': 'cyclic'}</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>decision_tree</td>\n",
              "      <td>0.709916</td>\n",
              "      <td>{'criterion': 'friedman_mse', 'splitter': 'ran...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e702deb3-4bf2-4ffa-b8ee-2ca92e477e94')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e702deb3-4bf2-4ffa-b8ee-2ca92e477e94 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-e702deb3-4bf2-4ffa-b8ee-2ca92e477e94');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bc678a4a-eae7-4a78-9434-d716e6fb510c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bc678a4a-eae7-4a78-9434-d716e6fb510c')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bc678a4a-eae7-4a78-9434-d716e6fb510c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from sklearn.linear_model import Lasso\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "def find_best_model_using_gridsearchcv(X,y):\n",
        "    algos = {\n",
        "        'linear_regression' : {\n",
        "            'model': LinearRegression(),\n",
        "            'params': {\n",
        "\n",
        "            }\n",
        "        },\n",
        "        'lasso': {\n",
        "            'model': Lasso(),\n",
        "            'params': {\n",
        "                'alpha': [1,2],\n",
        "                'selection': ['random', 'cyclic']\n",
        "            }\n",
        "        },\n",
        "        'decision_tree': {\n",
        "            'model': DecisionTreeRegressor(),\n",
        "            'params': {\n",
        "                'criterion' : ['mse','friedman_mse'],\n",
        "                'splitter': ['best','random']\n",
        "            }\n",
        "        }\n",
        "    }\n",
        "    scores = []\n",
        "    cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n",
        "    for algo_name, config in algos.items():\n",
        "        gs =  GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)\n",
        "        gs.fit(X,y)\n",
        "        scores.append({\n",
        "            'model': algo_name,\n",
        "            'best_score': gs.best_score_,\n",
        "            'best_params': gs.best_params_\n",
        "        })\n",
        "\n",
        "    return pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
        "\n",
        "find_best_model_using_gridsearchcv(X,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "790ff07a",
      "metadata": {
        "id": "790ff07a"
      },
      "outputs": [],
      "source": [
        "def predict_price(location,sqft,bath,bhk):\n",
        "    loc_index = np.where(X.columns==location)[0][0]\n",
        "\n",
        "    x = np.zeros(len(X.columns))\n",
        "    x[0] = sqft\n",
        "    x[1] = bath\n",
        "    x[2] = bhk\n",
        "    if loc_index >= 0:\n",
        "        x[loc_index] = 1\n",
        "\n",
        "    return lr_clf.predict([x])[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "22bdd8e0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22bdd8e0",
        "outputId": "bc48fb93-e215-45f5-abd8-8c76cffe259e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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": [
              "83.49904677206221"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "predict_price('1st Phase JP Nagar',1000, 2, 2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "fae1b329",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fae1b329",
        "outputId": "49d4f1c6-e15c-4cbe-a848-430431d5766d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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": [
              "86.80519395233001"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "predict_price('1st Phase JP Nagar',1000, 3, 3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "bf938d46",
      "metadata": {
        "id": "bf938d46"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "with open('banglore_home_prices_model.pickle','wb') as f:\n",
        "    pickle.dump(lr_clf,f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "5bd324f8",
      "metadata": {
        "id": "5bd324f8"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "columns = {\n",
        "    'data_columns' : [col.lower() for col in X.columns]\n",
        "}\n",
        "with open(\"columns.json\",\"w\") as f:\n",
        "    f.write(json.dumps(columns))"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}