Price_Prediction_Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eanXtpJDDbCN"
      },
      "source": [
        "# Data exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQYaw5oIDUwt"
      },
      "source": [
        "Importing necessary library of data exploration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AZMf-fMlDIJI"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H482D2xIH8zI",
        "outputId": "2800d7f2-f095-4696-8d1e-7a271e7cac53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colors.LinearSegmentedColormap at 0x7feccc4a9dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "sns.color_palette(\"Spectral\", as_cmap=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s_g2f48NDRYD"
      },
      "outputs": [],
      "source": [
        "file_path = \"/content/melb_data.csv\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Op89whWPEOAf"
      },
      "outputs": [],
      "source": [
        "data = pd.read_csv(file_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dc8agPi2ESMC",
        "outputId": "900128f8-6d4c-42af-d124-60c960c175fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
              "0  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
              "1  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
              "2  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
              "3  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
              "4  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
              "\n",
              "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
              "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
              "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
              "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
              "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
              "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
              "\n",
              "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
              "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
              "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
              "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
              "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
              "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
              "\n",
              "  Propertycount  \n",
              "0        4019.0  \n",
              "1        4019.0  \n",
              "2        4019.0  \n",
              "3        4019.0  \n",
              "4        4019.0  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <td>Abbotsford</td>\n",
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              "      <td>1480000.0</td>\n",
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              "      <td>4019.0</td>\n",
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              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
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              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
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              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8079</td>\n",
              "      <td>144.9934</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8093</td>\n",
              "      <td>144.9944</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>40 Federation La</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.7969</td>\n",
              "      <td>144.9969</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>55a Park St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>4/06/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8072</td>\n",
              "      <td>144.9941</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 21 columns</p>\n",
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          "metadata": {},
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        "data.head()"
      ]
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        "colab": {
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
              "count  13580.000000  1.358000e+04  13580.000000  13580.000000  13580.000000   \n",
              "mean       2.937997  1.075684e+06     10.137776   3105.301915      2.914728   \n",
              "std        0.955748  6.393107e+05      5.868725     90.676964      0.965921   \n",
              "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
              "25%        2.000000  6.500000e+05      6.100000   3044.000000      2.000000   \n",
              "50%        3.000000  9.030000e+05      9.200000   3084.000000      3.000000   \n",
              "75%        3.000000  1.330000e+06     13.000000   3148.000000      3.000000   \n",
              "max       10.000000  9.000000e+06     48.100000   3977.000000     20.000000   \n",
              "\n",
              "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
              "count  13580.000000  13518.000000   13580.000000   7130.000000  8205.000000   \n",
              "mean       1.534242      1.610075     558.416127    151.967650  1964.684217   \n",
              "std        0.691712      0.962634    3990.669241    541.014538    37.273762   \n",
              "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
              "25%        1.000000      1.000000     177.000000     93.000000  1940.000000   \n",
              "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
              "75%        2.000000      2.000000     651.000000    174.000000  1999.000000   \n",
              "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
              "\n",
              "          Lattitude    Longtitude  Propertycount  \n",
              "count  13580.000000  13580.000000   13580.000000  \n",
              "mean     -37.809203    144.995216    7454.417378  \n",
              "std        0.079260      0.103916    4378.581772  \n",
              "min      -38.182550    144.431810     249.000000  \n",
              "25%      -37.856822    144.929600    4380.000000  \n",
              "50%      -37.802355    145.000100    6555.000000  \n",
              "75%      -37.756400    145.058305   10331.000000  \n",
              "max      -37.408530    145.526350   21650.000000  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Price</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
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              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Propertycount</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13580.000000</td>\n",
              "      <td>1.358000e+04</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13518.000000</td>\n",
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              "      <td>8205.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.937997</td>\n",
              "      <td>1.075684e+06</td>\n",
              "      <td>10.137776</td>\n",
              "      <td>3105.301915</td>\n",
              "      <td>2.914728</td>\n",
              "      <td>1.534242</td>\n",
              "      <td>1.610075</td>\n",
              "      <td>558.416127</td>\n",
              "      <td>151.967650</td>\n",
              "      <td>1964.684217</td>\n",
              "      <td>-37.809203</td>\n",
              "      <td>144.995216</td>\n",
              "      <td>7454.417378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.955748</td>\n",
              "      <td>6.393107e+05</td>\n",
              "      <td>5.868725</td>\n",
              "      <td>90.676964</td>\n",
              "      <td>0.965921</td>\n",
              "      <td>0.691712</td>\n",
              "      <td>0.962634</td>\n",
              "      <td>3990.669241</td>\n",
              "      <td>541.014538</td>\n",
              "      <td>37.273762</td>\n",
              "      <td>0.079260</td>\n",
              "      <td>0.103916</td>\n",
              "      <td>4378.581772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.500000e+04</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1196.000000</td>\n",
              "      <td>-38.182550</td>\n",
              "      <td>144.431810</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.500000e+05</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>3044.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>1940.000000</td>\n",
              "      <td>-37.856822</td>\n",
              "      <td>144.929600</td>\n",
              "      <td>4380.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.030000e+05</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>3084.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>1970.000000</td>\n",
              "      <td>-37.802355</td>\n",
              "      <td>145.000100</td>\n",
              "      <td>6555.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.330000e+06</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>651.000000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>-37.756400</td>\n",
              "      <td>145.058305</td>\n",
              "      <td>10331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000e+06</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>3977.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>433014.000000</td>\n",
              "      <td>44515.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>-37.408530</td>\n",
              "      <td>145.526350</td>\n",
              "      <td>21650.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ce07e7ce-6d85-4d16-ac98-64345ea51259')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ce07e7ce-6d85-4d16-ac98-64345ea51259 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-ce07e7ce-6d85-4d16-ac98-64345ea51259');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HKSzGdhqFFWf",
        "outputId": "24b7bc7d-e8dd-4eba-f382-6ed3981c46e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13580"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "len(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZLERwxxJEcgE",
        "outputId": "158a35b5-5bd1-41a8-fca5-0e0c590bd38f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Suburb              0\n",
              "Address             0\n",
              "Rooms               0\n",
              "Type                0\n",
              "Price               0\n",
              "Method              0\n",
              "SellerG             0\n",
              "Date                0\n",
              "Distance            0\n",
              "Postcode            0\n",
              "Bedroom2            0\n",
              "Bathroom            0\n",
              "Car                62\n",
              "Landsize            0\n",
              "BuildingArea     6450\n",
              "YearBuilt        5375\n",
              "CouncilArea      1369\n",
              "Lattitude           0\n",
              "Longtitude          0\n",
              "Regionname          0\n",
              "Propertycount       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "data.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HfEYVsEyEs7t",
        "outputId": "306002c8-6559-4859-aa8a-a828069f4785",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "YearBuilt       -0.323617\n",
              "Lattitude       -0.212934\n",
              "Distance        -0.162522\n",
              "Propertycount   -0.042153\n",
              "Landsize         0.037507\n",
              "BuildingArea     0.090981\n",
              "Postcode         0.107867\n",
              "Longtitude       0.203656\n",
              "Car              0.238979\n",
              "Bathroom         0.467038\n",
              "Bedroom2         0.475951\n",
              "Rooms            0.496634\n",
              "Price            1.000000\n",
              "Name: Price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "data.corr()[\"Price\"].sort_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ht3meQyuL5Wl",
        "outputId": "e38ce625-b12e-4803-8dfc-9dc5925cba5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecb10b9f10>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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MPAGcA1BVO5K8B7i1jbuwqna+aC5JGrOxhUVVvXWGRSfuZmwB582wnSuBK2exNUnSXvIT3JKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldY7t1dj75hf9x1UT2e9sHzp7IfiVptnlkIUnqMiwkSV37xWkoabb9yTv/70T2+7Y//I2J7FfyyEKS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUtc98U16SVcAlwAHA5VX1vgm3JM0rF5355ons93c/fu1E9qu5tU8cWSQ5APhT4BTgSOCtSY6cbFeStP/YV44sjgU2V9X9AEmuBlYD90y0K0l7dO9FX5jIfv/1754wkf0+n6WqJt1DV5I3A6uq6j+3+bOA46rqbUNj1gJr2+wRwDdnafeHAH8/S9uaLfY0uvnYlz2Nxp5GN1t9/UxVLd7dgn3lyKKrqi4DLpvt7SbZVFVTs73d58KeRjcf+7Kn0djT6Oair33imgWwFThsaH5Zq0mS5sC+Eha3AiuSHJ7kRcAZwPoJ9yRJ+4194jRUVT2V5G3ADQxunb2yqu6eo93P+qmtWWBPo5uPfdnTaOxpdGPva5+4wC1Jmqx95TSUJGmCDAtJUpdhMYMkVybZluSuSfcyLclhSW5Mck+Su5O8Yx70dGCSW5J8vfX0+5PuaVqSA5LcnuQzk+4FIMm3k9yZ5GtJNk26H4AkC5Ncm+QbSe5N8vp50NMR7d9o+vV4kvPnQV//vf1//K4kn0xy4AR62OX3UpKDk2xIcl97XzSOfRsWM/sosGrSTezkKeCdVXUkcDxw3jx47MmTwAlVdRRwNLAqyfET7mnaO4B7J93ETn61qo6eR/fqXwJ8rqpeAxzFPPj3qqpvtn+jo4FfAJ4ArptkT0mWAv8NmKqq1zK40eaMCbTyUXb9vfQuYGNVrQA2tvlZZ1jMoKq+DOyYdB/Dquqhqvpqm/4+g/+wl064p6qqf2izL2yvid81kWQZ8GvA5ZPuZb5K8nLgjcAVAFX1w6r63mS72sWJwN9W1QOTboTB3aMvTrIAeAnwd3PdwAy/l1YD69r0OuC0cezbsNhHJVkOvA64ebKdPHO652vANmBDVU28J+CPgd8GfjTpRoYU8Pkkt7XH00za4cB24CPtdN3lSQ6adFM7OQP45KSbqKqtwB8A3wEeAh6rqs9PtqtnLKmqh9r0w8CScezEsNgHJflp4FPA+VX1+KT7qaqn2ymDZcCxSV47yX6S/Dqwrapum2Qfu/FLVXUMg6cnn5fkjRPuZwFwDHBpVb0O+EfGdArj2WgfwH0T8JfzoJdFDP6CPxx4JXBQkjMn29WuavBZiLEc2RsW+5gkL2QQFJ+oqk9Pup9h7RTGjUz+Ws8bgDcl+TZwNXBCko9PtqVn/jqlqrYxOAd/7GQ7YguwZehI8FoG4TFfnAJ8taoemXQjwL8DvlVV26vqn4FPA7844Z6mPZLkUID2vm0cOzEs9iFJwuD88r1V9UeT7gcgyeIkC9v0i4GTgG9MsqeqendVLauq5QxOY3yhqib6V2CSg5K8dHoaWAlM9E67qnoYeDDJEa10IvPrsf9vZR6cgmq+Axyf5CXtv8MTmQc3AzTrgTVteg1w/Th2YljMIMknga8ARyTZkuTcSffE4C/msxj8pTx9W+GpE+7pUODGJHcweIbXhqqaF7eqzjNLgL9O8nXgFuCvqupzE+4J4O3AJ9r/fkcD/3vC/QDPBOpJDP6Cn7h29HUt8FXgTga/O+f80R8z/F56H3BSkvsYHAGN5VtEfdyHJKnLIwtJUpdhIUnqMiwkSV2GhSSpy7CQJHXtE9+UJ80nSZ5mcPvkAuBbwFnz8JlK0qzyyELae//Unor6WgYPdTtv0g1J42ZYSM/NV2hP/k1ydJKbktyR5Lrp7xXYQ/2LSS5Osql9l8S/SfLp9r0E721jDkryV+37Qu5K8h8m9pNqv2ZYSM9SkgMYPPZhfStdBfxOVf08g9NUF3TqAD9s323xZwwe03Ae8Frgt5K8gsFztv6uqo5qRzLz4VPf2g8ZFtLee3F7JPv046A3tO+GWFhVX2pj1gFvnKk+tK3poLkTuLt9Z8mTwP3AYa1+UpL3J/nlqnpsvD+atHuGhbT3/qk9kv1ngPDcrlk82d5/NDQ9Pb+gqv4fgyfB3gm8N8nvPYd9Sc+aYSE9S1X1BIOv2nwng++CeDTJL7fFZwFfakcCu9RH3UeSVwJPVNXHgQ8wvx4hrv2It85Kz0FV3d6e2PpWBo+H/rMkL2FwGumcNmym+ih+DvhAkh8B/wz811lrXtoLPnVWktTlaShJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktT1/wGiqBG2IaME4gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.countplot(x=\"Rooms\", data=data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RaB1zdsqFmEI",
        "outputId": "322a2775-6806-4525-e9b4-daf9570b0c85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecb0b4ae90>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x=\"Longtitude\", y=\"Lattitude\", data=data, hue=\"Price\", palette=\"coolwarm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSFyoSATGM3F",
        "outputId": "e00b89c4-5edf-4c67-f878-8fdad1c9c24d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=\"Regionname\", y=\"Price\", data=data)\n",
        "plt.xticks(rotation=90)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ALEUh9QyLuNc"
      },
      "source": [
        "# Handling missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "n3daDbZTJAqR",
        "outputId": "61a26a98-d419-4839-ac89-ae224a0ed2d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecb0a5f6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(data.isna(), cbar=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tNnyEnwyKBwy"
      },
      "outputs": [],
      "source": [
        "data[\"BuildingArea\"].fillna(data[\"BuildingArea\"].mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XOXh7Yr6K9W9",
        "outputId": "df23e5dd-019a-4901-a7a8-2edcd5316b46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecac7cac10>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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dLOl3ktat3WtROX9mWT8WERERMSL0e5qyhLWOrafvA4dRbXc01fbtkj4JTLG9v6RtgWtsPyPpE8A2tvcs93qqa8ukpZVpyoiI6Iss4I/B1Ns0Zb8X8PeUvl++Vi7lqwAPlvN/X7v8auDD/a07IiKir9JBik7VbLRFd+n7HwUukvQs8ASwRTeXfgS4uPZ+eUkzqJ6kPMH2Bc20KyIiImKoaGoBv+1FtidRhbROkbQxcDiws+0JwKnAt+vXSPowMJlq78ou69qeTLWX5XclbdBdfZIOkjRD0ozFi59upukRERERHaFl0RaSvgw8Cxxse4NStg5wie2Nyvt3Aj8A3mF7fg/3+Snwa9vn9FZf1oxFRETEUNGWNWM9pe8Dq0ja0Padpez2cv5bgR8DO9U7YpJWA56x/ZykVwFvB77e33ZFRER0Jwv4o1M1s2as2/R9SR8DzpW0GPgHcGA5/xvAK4D/qh7E5G+23wu8EfhxOX8U1Zqx25poV0RExMukgxSdKgn8EREREW2WBP6IiIiIDtV0Z0zSaEk3Svp1eX+6pDskzZJ0SgmERdI2kh6vJe1/uXaPnco1syUd1WybIiIiIoaKpnLGisOoFul3Bb2ezouBrmcAHwVOLO//aHuX+sVlzdl/Ui32nwNcJ+nCrBuLiIhWygL+6FTNhr5OAN4DHA98BsD2RbXj11JlkPVmCjDb9j3lmrOAqUA6YxER0TLpIEWnanZk7LvA54CVGg+U6cl9qEbOuvyTpJuotkj6rO1bgfHAA7Vz5gCbN9muiIiIl8jIWHSqZnLGdgHm275e0jbdnPJD4CrbXX/6b6BK2n9K0s7ABcDE/tYfERHRF+kgRadqZmTs7cB7S8dqeWBlSb+w/WFJxwBrAB/vOtn2E7XXF0n6YQl5nQusXbvvhFL2MpIOAg4C0OhVGDVqbBPNj4iIkSQjY9GpWpIzVkbGPmt7l7JR+IHA9rafrZ3zauAh25Y0BTgHWBcYDdwJbE/VCbsO+FCZwuxRcsYiIqKvBqpDls5YNGrLdki9+BFwP/CXkrR/nu3jgD2AT0haSLWH5TRXPcGFkg4FLqXqmJ2ypI5YREREf6STFJ0oCfwRERERbZYE/oiIiIgO1Y4Efkk6XtKdkm6X9OlSfkQtfX+WpEWSVi/H7pN0Szk2o9k2RURERAwV7Ujg35/q6cg32F4saU0A298AvgEgaVfgcNuP1u6zre2HW9CeiIiIiCGjqZGxWgL/SbXiTwDH2V4MYHt+N5fuBZzZTN0RERERw0Gz05RdCfyLa2UbAHtKmiHpYkkvCXaVtCKwE3BurdjAZZKuL1liERERESNCvztj9QT+hkPLAf9nezLwE+CUhuO7Av/TMEW5pe1NgXcDh0jaur/tioiIiBhKmhkZ60rgvw84C9hO0i+o9pY8r5xzPvDmhuum0TBFaXtu+T6/XDOluwolHVRG3GYsXvx0E02PiIiI6Az97ozZPtr2BNvrUXWwrrD9Yao9J7ctp72DKl0fAEmrlLJf1crGSlqp6zWwIzCrhzqn255se3K2QoqIiIjhoB0J/CcAp0s6HHgK+Gjt2O7AZbbrw1prAeeXtP4xwBm2L2lDuyIiIiI6ThL4IyJiRMhG4TGYksAfERER0aHSGYuIiBEho1XRqVqxHdLLtjKS9AFJt0paLGlyw/lHS5ot6Q5J76qV71TKZks6qtl2RURE1A3kNGVEX7RqAX/jVkazgPcBP66fJGkjqicv3wS8BvitpA3L4f8EdqCKxrhO0oW2b2tR+yIiYoTLyFh0qrZMU9q+3fYd3RyaCpxl+znb9wKzqTLFpgCzbd9jewFVbtnUdrQtIiJGpoyMRadqRWesL1sZjQceqL2fU8p6Ko+IiGiJjIxFp2rFNOWWtudKWhO4XNJfbV/Vgvu+TOnsHQSg0auQ4NeIiIgY6poeGVvarYyKucDatfcTSllP5Y11JYE/IiIihpWmOmN92cqouBCYJmk5SesDE4FrgeuAiZLWl7Qs1SL/C5tpW0RERMRQ0Ow0ZbdbGUnaHfgBsAbwG0kzbb/L9q2SzgZuAxYCh9heBCDpUOBSYDRwiu1bm2xbRERERMfLdkgRERERbdbbdkjt2Cg8IiKi42RvyuhUza4Z6y59/1hJc0vZTEk7l/IdSvzFLeX7drX7XFnS97uuWbO5jxUREfFS6SBFp2rFyFhj+j7Ad2x/s6HsYWBX2w9K2phqfVg9S2xv2zNa0J6IiIiXSehrdKoBm6a0fWPt7a3ACpKWs/3cQLUhIiJGroyMRadqNmesp/T9QyXdLOkUSat1c937gRsaOmKnlinKL6k8nhkREREx3DX1NKWk8fX0feBTwB1UU5IGvgKMs31g7Zo3UWWI7Wj77ob7rAScC/zC9mnd1FdP4N8swa8RERExFPT2NGXLoi0kHQs8VV8rJmk94Ne2Ny7vJwBXAAfY/p8e7rM/MNn2ob3Vl2iLiIiIGCraEm1REvdH2X6ylr5/nKRxtueV03anJPJLWhX4DXBUvSMmaQywqu2HJS0D7AL8tr/tioiI6E6iLaJT9XtkTNJrqfaihBfT94+X9HNgEtU05X3Ax23Pk/RF4GjgrtptdgSeBq4ClqFK3/8t8JmuZP6eZGQsIiIihooBmaYcaOmMRURExFCRBP6IiBjxMk0ZnaodCfxvkfSXUv7fklYu5etJeraWsv+j2n02K+fPlvT9RFtERETESNFszhhUCfyTbE8u70+iWqS/CdWasiNq595dzp1k++Ba+YnAx4CJ5WunFrQrIiLiBRmtik7VjmnKDakW5EOVPXYp8KWeTpY0DljZ9tXl/WnAbsDFbWhbRESMYOmQRSdqtjPWlcBv4Me2p1NtdTQVuAD4ALB27fz1Jd0IPAF80fYfqfannFM7Zw4v3bMyIiKiaVkzFp2q2c7YlvUEfkl/BQ4Evi/pS1RJ+wvKufOAdWw/Imkz4IKSxr/UGhL4SQJ/REREDHVNdcZszy3f50s6H5hSEvh3BJC0IfCecs5zwHPl9fWS7qaa0pwLTKjddkIp666+6cB0SLRFRET0TUarolP1ewG/pLFlL8muNP4dgVlllAxJo4AvAj8q79eQNLq8fi3VQv17Slr/E5K2KE9R7gv8qonPFBERETFkNPM05VrAnyTdBFwL/Mb2JcBeku4E/go8CJxazt8auFnSTOAc4GDbj5Zjn6R6CnM2cDdZvB8REREjRBL4IyIiItqstwT+VuSMRUREREQ/NbWAX9KqVNOLG1PFXBwI7EwVbbEYmA/sb/tBSUcAe9fqfSOwhu1HJd0HPAksAhbWAmQjIiIihrWmpikl/Qz4o+2TJC0LrAgstv1EOf5pYKOGtH0k7Qocbnu78v4+YLLth5e27kxTRkRExFDRlmlKSatQLco/GcD2AtuPdXXEirFUI2aN9gLO7G/dERERfTWQoa8RfdHMNOX6wN+BUyW9BbgeOMz205KOp4qoeBzYtn6RpBWp9p48tFbcXZJ/RERESw1UhyyZZtEX/Z6mlDQZuBp4u+1rJH0PeML2l2rnHA0sb/uYWtmewIdt71orG19P8gc+ZfsqepFpyoiIiBgq2vU05Rxgju1ryvtzgE0bzjkdeH9D2TQapijrSf7A+cCU7iqUdJCkGZJmLF78dBNNj4iIiOgM/e6M2f5f4AFJry9F2wO3SZpYO20qVfgr8MI6s3dQS9jvKcm/hzqn255se3L2pYyIiIjhoNmNwj8FnF6epLwHOAA4qXTQFgP3A/UnKXcHLrNdH9ZaCzi/2gmJMcAZJck/IiIiYthLAn9ERIwIA/k0ZRbwR6Mk8EdERER0qHTGIiJiRMhoVXSqpqcpe9gS6VngR8DywELgk7avVbUw7HtUWyY9Q7VV0g3lPvsBXyy3/artn/VWb6YpIyIiYqjobZqy2QX8UHWuLrG9R21LpLOBf7N9saSdga8D2wDvBiaWr82BE4HNJa0OHANMpurQXS/pQtv/aEH7IiIiIjpWsxuFd22JtD9UWyIBC0qS/srltFWAB8vrqcBprobjrpa0qqRxVB21y20/Wu57OVVKf7ZMioiIlkkCf3SiZkfGut0SCfgX4FJJ36Ral/bP5fzxwAO16+eUsp7KIyIiWiJ7U0ananYB/xiq1P0Tbb8VeBo4CvgEcLjttYHDKZuJNysJ/BERETHcNDsy1t2WSEcBW1KNkAH8F9UCf4C5wNq16yeUsrlUU5X18isbKysbiE+HLOCPiIi+y/RhdKKmRsZ62hKJao3YO0rZdsBd5fWFwL6qbAE8bnsecCmwo6TVJK1GtSXSpc20LSIioi4dsehUrXiasrstkX4FfE/SGOD/gIPKuRdRxVrMpoq2OADA9qOSvgJcV847rmsxf0RERCskgT86VbZDioiIiGizbIcUERER0aH6PU1Z1on9slb0WuDLVJEUuwILgLuBA2w/JmkH4ARg2XLsCNtXlHtdCYyjSu4H2NH2/P62LSIiImKoaMk0paTRVE9Ebg68HrjC9kJJXwOwfaSktwIP2X5Q0sbApbbHl+uvBD5re8bS1plpyoiI6IusGYvB1O7tkKB6ivJu2/cD99fKrwb2ALB9Y638VmAFScvZfq5FbYiIiIgYclq1Zmwa3W9ddCBwcTfl7wduaOiInSpppqQvlQ3FIyIiWiajVdGpmp6mLJEWDwJvsv1QrfwLVBt/v8+1SiS9iSpvbEfbd5ey8bbnSloJOBf4he3TuqnrIEpMhkavstmoUWObantERETEQOhtmrIVnbGpwCG2d6yV7Q98HNje9jO18gnAFVSL+v+nh/vtD0y2fWhv9WbNWERE9EXWjMVgane0xV7Upigl7QR8DnhvQ0dsVeA3wFH1jpikMZJeVV4vA+wCzGpBuyIiIl6QDlJ0qqZGxiSNBf4GvNb246VsNrAc8Eg57WrbB0v6InA0L26NBNW2R08DVwHLAKOB3wKfsb2ot7ozMhYRERFDRVunKQdLOmMREdFXAzVVmVG4aJQE/oiIGPEGcs1YRF/0e2SspwR+29+V9CngEGAR8Bvbn5O0HnA7cEc5/2rbB5d7bQb8FFiBajPxw7yEhmVkLCIiIoaKtoS+2r4DmAQvSeA/X9K2wFTgLbafk7Rm7bK7bU/q5nYnAh8DrqHqjO1E9/lkEREREcNKq6Yp6wn8nwBO6Ap0XdIek5LGASvbvrqMhp0G7NaidkVERER0tHYk8G8IbCXpGkl/kPS22nnrS7qxlHetbhwPzKmdM6eURURERAx7Te9NWRL430sVW9F1z9WBLYC3AWdLei0wD1jH9iNljdgFJY2/L3XVE/hJAn9ERCythL5Gp2rFRuHvptpnsmsrpDnAeWXK8VpJi4FX2f470DV1eb2ku6lG0eYCE2r3m1DKXsb2dGA6ZAF/RET0TTpI0alansAPXABsCyBpQ2BZ4GFJa5SF/pSRsonAPbbnAU9I2qJsEL4v8KsWtCsiIiKi4zU1MlYS+Heg2oeyyynAKZJmAQuA/Wxb0tbAcZKeBxYDB9t+tFzzSV6MtriYPEkZEREtlmnK6FRJ4I+IiBEhnbEYTNkOKSIiImIQtSX0FUDS4cBHAQO3AAcA44CzgFcC1wP72F4g6TuUtWTAisCatlct91lUrgf4m+33NtOuiIiIRhkZi07V786YpPHAp4GNbD8r6WyqvLGdge/YPkvSj4CPACfaPrx27aeAt9Zu92wPyfwRERERw1qzT1OOAVaQNIZqtGsesB1wTjn+M7pP0298AjMiIqKtMloVnaqZvSnnSvom8DfgWeAyqmnJx2wvLKe9LE1f0rrA+sAVteLlJc0AFlJtpXRBf9sVERHRk3TIohM1M025GtWG4OsDjwH/RbXB95JMA86xvahWtm7p3L0WuELSLbbv7qbOJPBHRETEsNLMAv53AveWZH0knQe8HVhV0pgyOtZdmv404JB6ge255fs9kq6kWk/2ss5YEvgjIqK/soA/OlUznbG/AVtIWpFqmnJ7YAbwe2APqicq96OWpi/pDcBqwF9qZasBz9h+TtKrqDp0X2+iXRERES+TDlJ0qmbWjF0j6RzgBqq1XjdSjVr9BjhL0ldL2cm1y6YBZ/ml4WZvBH5c9rAcRbVm7Lb+tisiIqI7GRmLTpXQ14iIGDEGqthl+SkAACAASURBVEOWzlg06i30tRUbhUdERHS8gRwZi+iLpjpjkg6XdKukWZLOlLS8pJ9KulfSzPI1qZwrSd+XNFvSzZI2rd1nP0l3la/9mv1QERERjTJaFZ2q39OUJYH/T7w0gf8iYBvg17bPaTh/Z+BTVAn9mwPfs725pNWpFv5PptpW6XpgM9v/6K3+TFNGRETEUNG2vSl5MYH/eaoE/gd7OXcqcFpZvH+1pFUljaPqvF1u+1EASZdT5ZUloT8iIlomC/ijU7U0gd/2ZZI+BBwv6cvA74CjbD9HlcT/QO0WXen8PZVHRES0TDpI0alamsAv6cPA0cD/AstSRV0cCRzXfFMjIiL6LyNj0alancD/z7Z/UY4/J+lU4LPl/Vxg7dr1Xen8c6mmKuvlV3ZXYbZDioiI/koHKTpVM09TvpDAL0lUCfy3l3VglLLdgFnl/AuBfctTlVsAj9ueB1wK7ChptTLatmMpexnb021Ptj05HbGIiOiLRFtEp2pHAv/FktYABMwEDi6XXET1JOVs4BnggHKfRyV9BbiunHdc12L+iIiIVkroa3SiJPBHREREtFkS+CMiIiI6VDpjEREREYOoHdshbSfphlL2M0ljyrl7l22QbpH0Z0lvqd3nvlI+U9KMZj9URERExFDR785Y2Q7p08Bk2xsDo4EPAT8DppWy+4GuvSbvBd5hexPgK1SL/eu2tT3J9uT+tikiIiJiqGn1dkhPAwts31mOX04VAnuy7T/XrruaKk8sIiJiQCT0NTpVv0fGbM8FurZDmgc8DpwNjJHUNbq1By8Neu3yEeDi+u2AyyRdX4JdIyIiWiodpOhULd0OCdgbmAZ8R9JywGXAoobrtqXqjG1ZK96y7HW5JnC5pL/avqqbOpPAHxER/ZLQ1+hUzSzgf2E7JNvPA13bIf3F9la2pwBXAV1Tlkh6M3ASMNX2I13lZZQN2/OB84Ep3VWYBP6IiIgYbppZM/bCdkjAs1TbIc2QtKbt+WVk7EjgeABJ61B12PaprSlD0lhglO0ny+sdycbiERHRYpmmjE7Vju2QvippF6pRtxNtX1Eu+TLwSuCH1baVLCxPTq4FnF/KxgBn2L6kv+2KiIiIGEqyHVJEREREm2U7pIiIiIgO1VTOmKTDgI8BAn5i+7uSvkL1lOViYD6wv+0HJW0D/Ioq/BXgPNvHlfvsBHyPKjj2JNsnNNOuiIiIRskZi07V72lKSRsDZ1E9+bgAuAQ4GJhv+4lyzqeBjWwfXDpjn7W9S8N9RlM9cbkDMAe4DtjL9m291Z9pyoiIiBgq2jVN+UbgGtvP2F4I/AF4X1dHrBhLFejamynAbNv32F5A1cGb2kS7IiIiIoaMZqYpZwHHS3olVbTFzsAMAEnHA/tSpfJvW7vmnyTdBDxINUp2KzAeeKB2zhxg8ybaFRER8TKZpoxO1Uy0xe2SvkaVsv80MJOStm/7C8AXJB0NHAocQxWBsa7tpyTtDFwATOxLnUngj4iI/koHKTpVy6ItJP07MMf2D2tl6wAX2d64m/PvAyZTdciOtf2uUn40gO3/11t9WTMWERF9kZGxGEy9rRlr9mnKrrT9dYD3USXyT7R9VzllKvDXcu6rgYdsW9IUqvVqj1DtazlR0vrAXKq9LT/UTLsiIiIapYMUnaqpzhhwblkz9jxwiO3HJJ0s6fVU0Rb3Uz1hCbAH8AlJC6nWmE1zNSy3UNKhwKVU0RanlLVkEREREcNeEvgjIiIi2qxt05QRERFDRdaMRadqRwL/L4HXl1NWBR6zPUnS3sARtcvfDGxqe6akK4FxVNOXADvant9M2yIiIhqlkxSdqOUJ/LZn1875FvB417ZHtfJNgAtsb1DeX0mVOzZjaevPNGVEREQMFe2apnwhgR9A0h+onqj8enkv4IPAdt1cuxdVRy4iImLADNRUZUbgoi+a2Q5pFrCVpFdKWpEqgX/t2vGtqKIs7urm2j2BMxvKTpU0U9KXSkcuIiKiZQZyzVhEX/S7M2b7dqArgf8Sagn8xV68vMOFpM2BZ2zPqhXvbXsTqg7cVsA+3dUp6SBJMyTNWLz46f42PSIiIqJjtCWBX9IYqgDXzWzPaTjvO8Dfbf97D/fZH5hs+9De6suasYiIiBgqBjSBvxx6J/DXbjpio6jWkW1VKxsDrGr7YUnLALsAv22mXREREY0SbRGdquUJ/KV8Gt1MUQJbAw/YvqdWthxwaemIjabqiP2kyXZFREREDAlJ4I+IiBEhI2MxmHqbpkxnLCIiIqLNml4zJukUqrVc821vXMpWB34JrAfcB3zQ9j9K0v6RVKn8TwKfsH1TuWYn4HtU05En2T6hlK9PlTv2SuB6YB/bC/r8SSMiInqQkbHoVEs1MiZpa+Ap4LRaZ+zrwKO2T5B0FLCa7SMl/TNwe+mYvRs41vbmkkYDdwI7AHOA64C9bN8m6WzgPNtnSfoRcJPtE3trU0bGIiIiYqjobWRsqXLGbF8FPNpQPBX4WXn9M2C3cu6fbf+jlF8NTCivpwCzbd9TRr3OAqaWgNftgHMa7xUREREx3DWTwL+W7Xnl9f8Ca3VzzkeAi8vr8cADtWNzStkrqTYTX9hQHhERETHsNRttAYBtS3rJtKGkbak6Y1u2oo6IiIiI4aiZkbGHJI0DKN/ndx2Q9GbgJGCq7UdK8VxeunflhFL2CLBqCX+tl79MtkOKiIiI4aaZztiFwH7l9X7ArwBKGv95VE9E3lk7/zpgoqT1JS1LFQx7oasnCH4P7NF4r0a2p9uebHvyqFFjm2h6RERERGdY2qcpzwS2AV4FPAQcA1wAnA2sA9xPFW3xqKSTgPeXMoCFtieX++wMfJcq2uIU28eX8tdSLehfHbgR+LDt53prU56mjIiIvki0RQymhL5GREREDKK2bRQeERExVGRkLDrVEteMSTpF0nxJs2plq0u6XNJd5ftqtWPbSJop6VZJfyhla0v6vaTbSvlhtfOPlTS3XDOzTGVGREREjAhLnKbsY/r+qsCfgZ1s/03Smrbnl6ctx9m+QdJKVFse7VbS948FnrL9zb40PNOUERERMVQ0lcDfl/R94ENU2xr9rVw7v3yfZ/uG8vpJ4HYS7BoRERHR7zVjPaXvbwgsI+lKYCXge7ZPq18oaT3grcA1teJDJe0LzAD+tbadUkREREtkzVh0qmZyxoAqfR/omjIcA2wGvAd4F/AlSRt2nSvpFcC5wL/YfqIUnwhsAEwC5gHfarZNERERjdJBik7V385YT+n7c4BLbT9t+2HgKuAt5bxlqDpip9s+r+tGth+yvcj2YuAnVBuKdysJ/BER0V8DOTIW0Rf9nabsSt8/gZcm5v8K+I+ytdGywObAdyQJOBm43fa36zeSNK425bk7MIse2J4OTIcs4I+IiL7JyFh0qiV2xurp+5LmUKXvnwCcLekjlPR9ANu3S7oEuBlYDJxke5akLYF9gFskzSy3/rzti4CvS5pENdV5H/DxFn6+iIiIiI6WBP6IiBgRsoA/BlO2Q4qIiIgYRE3ljEVERAwHWcAfnaq/2yF9oGxrtFjS5Ibzj5Y0W9Idkt5VK79P0i1ly6MZtfIet1aKiIholUwdRqdampGxnwI7NZTNAt5HFV3xAkkbAdOAN5VrfihpdO2UbW1Psl3vwB0F/M72ROB35X1ERETEiLDEpyltX1VS8+tltwNUiRUvMRU4y/ZzwL2SZlPlhv2llyqmUj2tCdXWSlcCRy6x5REREX00UFOVGYWLvmj1mrHxwAO193N4cQ9KA5dJul7SQbVzetpaKSIiomWyZiw61UAu4N/S9qbAu4FDJG3deELD1kovkwT+iIjor4xWRafqbwJ/T+YCa9feTyhl2O76Pl/S+VTTl1dRtlayPa9ha6WXSQJ/REQ0Ix2y6EStHhm7EJgmaTlJ6wMTgWsljZW0EoCkscCOvLjtUdfWSvDSrZUiIiIihr3+bof0KPADYA3gN5Jm2n6X7VslnQ3cBiwEDrG9SNJawPllwf8Y4Azbl5Qqut1aKSIiImIkSAJ/RESMCNkOKQZTtkOKiIiIGERNbYfU1wT+cnwdSU9J+mx5//qSvN/19YSkfynHjpU0t3Zs5/59zIiIiIihp6UJ/DXfBi7uemP7jpK8PwnYDHgGOL92/ne6jtu+aGkbHxERETHUtTqBH0m7AfcCPQWBbQ/cbfv+PrY1IiIiYthpabSFpFdQbWX0b72cNg04s6HsUEk3lynRbBQeERERI0arc8aOpZpyfKq7g5KWBd4L/Fet+ERgA2ASMA/4Vk83TwJ/REREDDetTuDfHNhD0teBVYHFkv7P9n+U4+8GbrD9UNcF9deSfgL8uqebJ4E/IiIihpuWdsZsvxCsIulY4KlaRwxgLxqmKLu2Qipvd+fFZP6IiIiWSc5YdKqWJvAv4T5jgR2Ajzcc+rqkSVQbhN/XzfGIiIiIYSuhrxERMWIM1OhYRsaiUVOhrxEREcPBQE5TRvTFEkfGJJ0C7ALMt71xKfsA1ZOTbwSm2J5RypcFfgxMBhYDh9m+shzbjCpAdgXgonLMklYHfgmsRzVN+UHb/1hSwzMyFhEREUNFsyNjP2XpE/g/BmB7E6r1Yd+S1FXHieX4xPLVdc+jgN/Zngj8rryPiIhoqYyMRadaYmfM9lVUC/brZbfbvqOb0zcCrijnzAceAyZLGgesbPtqV0NxpwG7lWumAj8rr39WK4+IiGiZrOOKTtXqNWM3Ae+VNEbS+lT7UK4NjAfm1M6bU8oA1qpFW/wvsFaL2xQRERHRsVod+noK1TqyGcD9wJ+BRUt7cVlD1uNaMEkHAQcBaPQqjBo1trnWRkRERAyyVoe+LgQO73ov6c/AncA/gAm1UycAc8vrh7qCX8t05vxe7p8E/oiI6JeEvkanamlnTNKKVE9oPi1pB2Ch7dvKsSckbQFcA+xLFRoLcCGwH3BC+f6rVrYpIiIC0kGKzrU00RYvJPADD/HyBP7HgJm23yVpPeBSqliLucBHbN9f7jOZF6MtLgY+VaYlXwmcDaxDNbX5QdsveWCgOxkZi4iIiKGit2iLJPBHREREtFkS+CMiIiI61BI7Y5JOkTRf0qxa2Tck/VXSzZLOl7RqKZ8iaWb5uknS7qV8bUm/l3SbpFslHVa717GS5tau27kdHzQiIiKiEy3NmrGtgaeA02rbIe0IXGF7oaSvAdg+sizgX1DKx1Hljr2Gam3ZONs3SFoJuB7YzfZtko4FnrL9zb40PNOUERHRqbIheTTqbZpyiU9T2r6qLMyvl11We3s1sEcpf6ZWvjzgUj4PmFdePynpdqrQ19uW6hNEREQ0KdshRadqRbTFgVQbfQMgaXOq8Nd1gX1K9hi14+sBb6WKuOhyqKR9qcJi/3VpNgqPiIjoi4EcRUrHL/qiqQX8kr4ALARO7yqzfY3tNwFvA46WtHzt/FcA5wL/YvuJUnwisAEwiWr07FvNtCkiIiJiKOn3yJik/YFdgO3dzcIz27dLegrYGJghaRmqjtjpts+rnfdQ7Z4/AX7dS53ZDikiIvolo1XRqfo1MiZpJ+BzwHvr68QkrS9pTHm9LvAG4D5JAk4Gbrf97YZ7jau93R2YRQ9sT7c92fbkdMQiIiJiOOhvAv/RwHLAI+W0q20fLGkf4CjgeaoU/uNsXyBpS+CPwC2lHODzti+S9HOqKUoD9wEfLwv+e5WnKSMioq+G4+hYnqgcGpLAHxERI146YjGYksAfERER0aGWZpryFKqF+vNroa/fAHYFFgB3AwfYfkzS3sARtcvfDGxqe6akK4FxwLPl2I6250taDjgN2Ixq2nNP2/ctqeEZGYuIiE41kKNwGR0bGpqapuxLAn/DdZsAF9jeoLy/Evis7RkN530SeHNZczYN2N32nkv6UOmMRUREX2SaMgbTgCXwN9gLOGsp2jcVOLa8Pgf4D0nqLi4jIiKivxL6Gp2qFWvGDgQu7qZ8T+DMhrJTy2bgXypxF1Bti/QAQEnrfxx4ZQvaFRER8YJ0kKJTNbUdUncJ/KV8c+AZ2/XMsL1tzy0bhZ8L7EO1ViwiImJApEMWnahdCfzTaBgVsz23fH9S0hnAFKrO2FxgbWBOCYxdhRfzyxrrTAJ/RET0S6Ypo1P1qzNWS+B/Rz2BvxwbBXwQ2KpWNgZY1fbDZVukXYDflsMXAvsBf6Fae3ZFT+vFbE8HpkMW8EfEyDRQ/8lnYXjEwFliZ6yewC9pDi9N4L+8LP262vbB5ZKtgQds31O7zXLApaUjNpqqI/aTcuxk4OeSZgOPUo2qRUREN9JJihh+ksAfERHRYskZi0ZNRVtEREQMB8NxHVc6YsNDOmMRETEiZAF/dKr+bof0Faqw1sXAfGB/2w9KegNwKrAp8AXb3yznvx74Ze22rwW+bPu7ko4FPgb8vRz7vO2LltTwTFNGRERfDcdOUkbHhoZ2bIe0su0nyutPAxuV7YzWBNYFdgP+0dUZa7jfaKo4i81t3186Y091d25v0hmLiIhOladeo1FvnbElJvDbvorqKcd62RO1t2MBl/L5tq8Dnu/lltsDd9u+f0l1R0RERAx3zYS+Hg/sS7V90bZ9uPRlgbDAoZL2BWYA/2r7H/1tV0TEcJYRl+YMx2nKGPqWKtqibBT+665pyoZjRwPL2z6mVnYs3Uw9SloWeBB4k+2HStlawMNUo2tfAcbZPrCHdtQT+DdLAn9ERHSidJqjUVPTlEvhdOD9S3nuu4EbujpiALYfsr3I9mKqINgpPV1se7rtybYnpyMWERERw0F/t0OaaPuu8nYq8NelvHQvGqYoJY2zPa+83R2Y9bKrIiIimpQpyuhUS/M05QvbIQEPUW2HtDPweqpoi/uBg23PlfRqqnVfK5djT1E9afmEpLHA34DX2n68dv+fA5OopinvAz5e65z1KE9TRkREp8o0ZTRqKtqiU6UzFhERfTUcR8fSIRsa0hmLiIgYQBkZi0btXsAfEREREf2UzlhERMQQNRynXUeidMYiIiKGqExTDg/pjEVEREQMJtsj5gs4aLjVNRw/U379hkY9+UxDp658pqFR13D8TPn1W7qvkTYydtAwrGs4fqaBrCufaWjUNRw/00DWlc80NOoajp9pIOsasp9ppHXGIiIiIjpKOmMRERERg2ikdcamD8O6huNnGsi68pmGRl3D8TMNZF35TEOjruH4mQayriH7mYZsAn9ERETEcDDSRsYiIiIiOko6YxERERGDKJ2x6JWkFSS9frDbERFLT5W1B7sdQ5Wkry1NWUSrjJjOmKSVJa3UxvtvIGm58nobSZ+WtGq76hsIknYFZgKXlPeTJF04uK3qfJJGS3pF7f0WkrYuX+38M7iupHeW1ysMh7ryw0D/uFoMfNFA1jmQv1eS1pS0TtdXG6rYoZuyd7ehngEnaUtJB5TXa0hav831rdjO+w8kSb9bmrL+GPadMUlvk3QLcDMwS9JNkjZrQ1XnAoskvY7qKYu1gTPaUA+SPtD1n5+kL0o6T9KmbajqWGAK8BiA7ZlA2/7iStpQ0u8kzSrv3yzpiy28/2d6+2pVPcDXgE/W3p8JHAF8CWjZ56mT9DHgHODHpWgCcMFQrmugfhgoP6ht0E35m1tdV7nvLpJulPSopCckPSnpiTZUdYOkt7Xhvi8zgL9X75V0F3Av8AfgPuDiFt7/E+X/i9dLurn2dS/V/yEtV35Yu07SU5IWSFrUpj8PSDoGOBI4uhQtA/yiTXX9s6TbgL+W92+R9MM21LOWpJMlXVzebyTpIy2uY3lJqwOvkrSapNXL13rA+JZUMlBbBwzWF9VfoK1q77cEbm5DPTeU70cAnyqvb2zXZ6p9liuB9wDXtKGeqxs/Rzt+7Wr3/gNV569e36wW3v+Y8nUGcBfwrfJ1J/CLFtZzIzCm/r58F/CnNv3azQSWbfi1u2Uo1wVcD6zSznqADwIPls90K/C22rEb2vTrNxt4M+Vp9nZ9Uf0nuBC4u/w7eEu7/v4OxO9VuedNwCtrf6e2BU5u4f1XAdaj+gFq3drX6m38fZoBvK78uzEaOAD4f22qa2b5d6jt/6YD11ANSrTl3/PaPS8uf49vKu/HtOHficOofgB4DrinvL63/Hk8tBV1jGH4W2T7j11vbP9J0sI21PO8pL2A/YBdS9kybagHYFH5/h5guu3fSPpqG+q5VdKHgNGSJgKfBv7chnq6rGj7Wkn1spb9Xtn+NwBJVwGb2n6yvD8W+E2r6gFG2a63+8hSv+vTly32nO0FXb92ksYA7cqtGai6nrf9eMOfh1bX83lgM9vzJE0Bfi7paNvnU/2n1Q4PUP2n1O5coXe1+f51A/F71VXPI5JGSRpl+/eSvtvC+48GngAOaTwgaXXbj7awrhfYni1ptO1FwKmSbuTF0atWWlD+Hap+OpTGtqGOF9h+oOHPxKKezm3Cq2yfLenoUudCSS2tx/b3gO9J+pTtH7Ty3l2GbWesNm33B0k/pvpJx8CeVKNJrXYAcDBwvO17yzz8z9tQD8Dc8pl2AL6maq1aO6acPwV8geqngTOBS4GvtKGeLg+X6aKufyj2AOa1oZ61gAW19wtKWassK2mlrs6e7csAJK0CLN/Ceur+IOnzwAqSdqCaJv3vIV7XQPwwMNr2PIDyg8C2wK9VLX5vV2fpc8BFkv5A9XeLUv+3W1mJ7fuhWl9F+/7cdRmoH9weKz/Q/BE4XdJ84OkW3v96Xvx9b+yMG/5/e2ceZldRre/3SwIE0EAURFGQyYCKIIjK7IDwgAMqF0FkUFGmiwyK6FV/CoKzoHDxgorCRUQURJwBmQRkMIYhYdD8VBRFEUSRWTHw3T9W7fTuk9OdkK7aTZ/U+zz9dO/dfWqd02ef2lVr+BZrZbTV8JCkpYEbJH2GmPNKpRCdle4dK6Z0g72BkwvZ+qOkzQFLWorwLv2ygJ0HJT2VofvGpsC9Bexg+4T0mtagtX6y/bWxjj2woq+SLh3l17b9yoK2pwOr2S6VY7AcsD3hiv21pGcAL2hu+gXsTSP+Z/eXGL9lZy0i325z4B7CDbyH7d9ntvMhwq3deD9eD3zL9iczjf8e4FXA/rb/kM49GzgJuMT2MTns9NicBLwD2I54TRfYLjLJ9rMFfCW3pydd5x/qsXO07X9mtHEVsKft37bOPZnIgdvS9jK5bLXG/wnwABE2fKw533huM9rZkQjDrwrcRYTbfmn7+TntJFvt9wrivfpYzvcq2VkeeJhYrOxOhBXPsP23nHa6JM0NdxKh/3cTr+lE278pZG9bhs8TFxaysxJwPDEXCvgJcEju9yo5Xk4A1gduAlYGdi5x/5V0OrA2Ee5tvG+2ffCYxx7UxRjMv2nsbPusDmz9FNiRWC1fS0x+V9rOmRjetjediMe3V+fXZbbxYuAUoKmUuxfY2/a1Oe30sbs8EeortvhLH+CtiN3UFbavzzz+/kQIrAkDPAB8yvZJOe207B2SXOmjnstka3ngnymkgqTJwDK2H8ptqzSSNgQe7L3xpZ38LrbPKGDzJtvr5x63j53ZwCuBi2xvlLx+e9jOmtycbG0FXNVcE+ncxrnnpDTus4Hn2L4oLQIn55orJK1n+1caoSCqxOtJdpcFVrc9t8T4g05KlViXWPTNtf3vQnZ+CTyvRIrBQC/GACTNsr1JB3auTxPeOwmv2BGS5tjOXpEl6WjgbURibvMGZvf2SZoDHNjk3Enaktixlaoy+wTwGdv/SMfTgcNsZ69ATDfhrRlajM3OPP4k4E0keYEOvIrX2d6459z1tjcqYOsa4FW2H0jHTwJ+YnvzTOP/gFFChLZ3zGFnBNvTGL7ByZ4jlEJRF5XyZLfszLK9SVqUbWT7MUmzbW9YwNZDwC+AN9m+K51b4JrMYGcfYF8ioX7tFBL9ou1tMo3/Zdv7jhBZKRJRUVSiHgMsbXtNSS8Ejsp5nUu6n9E/U9My2jphIbbG7EXqsTeZyJ9eg+Gf3axh/2TrbODgJrUhJwObM9biIknvBb5FK7egwCQ7JYULdyHc9SXZBVjb9iML/cux0VXxQ8MOtj/YsnePpFeTWQ5C0iHAPoQciYCvp0k4W2JmuvEdbvtbucbsh6Jo5C3AmhouJfBkoEiyMTC1WYgB2H5AebWEmjDuTsDTGSq9340I52RH0n7AR4F/0trgUCZH6ADgvZIeAZodvHPeEBNNftXllMmvajMX+CyRT/gO21dRpgDiQKLi+ucAKU3jabkGt71v+v6KXGMuAkcSr+mnyfYNyqz9ZbuRQjqayEk7nXh/dgeekdMWUR0KsAXwPOLeC7E5vSWzLYh81X/SE/YvxErALZJmMjzfc8wL5yVhMbZr+t6ujikxyR5F5ElcafsXKf/p15ltNNwErEiEQktymfoUPzQu/AIu+8mSlrH9L5jvus+es0PkO73U9oPJzqeBq4m8g5x0sRG4iphcVyLygxrup5AuEpEwOz8EpdDtezjX4LYvS+Me2+PV/oGkWSM8bKy8F1jf9t2Fxp9Pc2MshULrcBUiF/JhIg9pdyJn7KBCZm37h5LmAt+SdAplCiA6qeSVtFe/8zkStfvQVSUqwI49ntGTkuf0I7kM2D4NQrONyLucl46/SBRe5OZZpaI1fTiy1MADvxizXVRduGXnbODs1vGtwH8UMvdJ4HqFOGrW1XkPzYf2iJ7zGxGTRW6X/RnAxZJOTcdvB07LbANiR9gufX6UMrv44hsBR8XcbcBmucZcBA4Fzpb0Z+L/9nSGXmtOlpe0VvoskbwFpUrxfwt0lvOWkuu3Toc/tf3DjMMfB3yg2WwQ3oLTJL0A+ARD0js5Ecz3VG1N5JqWuEFepm4qedtiuVOBbYDrgBKLsS4lhB6UtDvwTWIu2o1y3tLpwDSGPPRPSudyc56k7UqH/WFoo1iCJSFnrJMdjqQZRLXcKrbXV6h372g7u/6XpJsJ9fPeaqysF4qGdG86Q9IOxMQHcKHtCwrYeA+hB3duw6R8cQAAHSpJREFUOvUG4H9t59Qr6hRFOfcJwHOJqqzJRGJ67tBXY28pImEWCiXMStqeqK69lbjZPxvYt8SkK2kj4FQi/NXe4GTNb0m2PkXc7JvigN2AWbaz6EpJ+oXtvsr7km60/YIcdhbheazuVE2ccUwB76RwJW8fuysC37S9fYGxO6lETbbWICoctyAWY1cChzpzxXqy9XbCk3Qp8V5tDRzZeM4y2nkjkcowiQj7izJh/97cu6UJLdEs8+ySsBhrh57m73Bs75zZzmWE+v6XmqRpFaqaGm2yzWznViKv6hTbJfRhxo0UWtsiHWavpkw2lgPeQ1RJ7Zt2vetm9oI0tmYBbya8s5sAewEzct3g+9grorXTx84ywHrp8FdNCLuAnZnAz1hwg5PdM5sKY15o+7F0PJlQKc/iSZL0a9vPGeF3v7G9Tg47abz32f6MpP/u9/uci9n0f7rZ9noL/ePMpM3HTbaz9t5Mr+mijnPUOkPS04GXpsOf2/5LARu/I0LyN5ZelPfYbWSRNrX9X2Mdb0kIUw7LkWh2OAVMFVWP7+EKSZ8Evs/wXXzuHK4NiRv8VxXVgacQu8OsfdMk/cz2ln0qfortcAidmDtIn4ESu3jC03ItoZsG8CdisZR9MQbdqXhrBK0dModw0g1wP1rhPElfKuGFA5ZyIRmaEViRofDNCpnHniVpH/fozCkqvXPL0jSbtKJyNwC2H5U0t9BndRgaXtE7iUhEzy6RlF7TY5JWsF1EqLRNSgFZYMFie+8CtprP7T3p+wxJM2xfntlUVx0thpHsfVfR77MuxhaDB6FIs+uu1OMhcrYANm2dy5bDJWmK7XkOOYaTgZMlvYzo6fh5Sd8mxDeziBLa3jJ9L5rY3CDpICIP7k6G8sVM/hyXtW3vqqh4xPZD6lmtZ6RLFe9NKKS108NJRBigaS68Zzr3zgK2zpO0L5F/1N7glKhIbXI+2+GbMU/mLQ4Fzk25Qc0iaRMirPLGjHaw/YP0fb4HMW3cnpR705aYTuRYzWR4UUzufNm2MPM84Dbbt2e20fAAcKOkCxn+mrKHyBm+EZxKXA9/LmAHIlLUtvUS4nrMnWt8K7FRO4+CHS0AJO3UOpxEfK6yhJMHfjHWs8OZTOTUlBCBPZDIb1lP0p8I9fjdC9jpoux6JrCxhvRb9ibydY4l8ly2IvSzZuQy2HEI4hAiXFhatfuRVBHaLNDXpjVZZGZPYnJ4F1E9txrlCkhuIpL2S202Gl7cU/l1Sar8KsFu6Xvbk1hE2sL2mQqR6CbV4P05wze27wQ2V4i8NmkSP7J9SS4bvUj6BtEO7lFCb2yapONtfzazqQ9nHq8v7fxbhZJ8ybniO+lr2FMoYcj2Oe1jSWcS4fkStoYViihajJXIy22adi+dvkrSfk3zgN8TocoxM/CLMTrY4aSFxH/afpW6UY9fgfDsNG7gywiRwNxu7l8TyZeftn116/y3Wy7oLHQZgiDc2sVDAsR7dD6wmqQziBy1t5UwZPs2SSunn7O21elDMa2dHh6VtLZTqyKFXEyRgpKuqq5bvJihz68pUBFo+1Li89sFz7N9X/LGnUd4+q4ltMey0VukpBCi3o2YA8dMKoT5FBFCPprQ41oJmCRpL9vn57DTpjcvMS1a3pzbzgg8B8im07YQbiecIVnpYL5r23p7qbEHfjFm+7KURPgSYtL77UIesjg2Hk2TAq1y8pKcQngndknHexL5STuN+IjHx9NSxeEphE7RZpLmSyfY/lwhF3rREER6TTDk1v4RZRs1XyjpOiKcLKIvW1YdqxT2PILwiE1Kp+YBJ9g+KqetFkcWGreXw4FLUyFJU01ZZDJM+WkH0MpPI4pxSlSJ9lZTHixpM7cEjycgS6X/4RuAL9j+t6Qi3h1F5etbCBHR3xFFRrn4AtHGbAXgEkKI+hpJ6xF6i9kXYwBpI/UmYmG5KkOV3rnt9Obl/gV4fyFbbSX+ScALCXmQ3HZWBt4HPJ8IhwLgMt0SnkVUrc8v/iLm9TE7eAZ+MZaSVj9CfLAEnCDpKNunZDZ1vUIB/WyGLyR63c85WNt2OwT1UUk3ZBx/MqEJo/S9K0qHIJqctD+kr7ZbO9uNQwv2tWvCeasnz1/OCendxMTwYtu/S/bXIsQc32378xltAWW1dnrsXNxUoKZTc0tVU9JtftqrGV5NeRpwPbEImKh8iQjZzAYuV/SPzJYzppAO2i193U0IKatAysYUJ+mUdJ+4BsDRrzKrIUVD+p2IheUMIlS5pu1nZTXUoqu83ERboHkecKbtKwvYOYO4Hl5LhMrfCvy1gB0Ip8c3iIUzwB7p3LZjHtn2QH8RbTqe2jp+KjGp57Zzap+vUwq9pqsJZePmeAvg6ozjX9fxezSVSDr+AlE9N6WwvTctyrkxjH/pKF+XZH4t1wMr9Tm/MiGXUOL/tymRF/QA8AgROryvgJ0DgRVbx9OJdIASr2n2opzLZGsO0VuxOX4KMKeErfH8yvk5JuRGLgPWaZ27tcBzvq7fz/2OM9h6OL2mrRiSmcr+mnpsXrwo5zLZOmRRzmWwc236Pqd17heFXtMNi3Jucb4G3jNGJF6287fup0AypgvGkvtwAKGovQLhvfo7sRvIRamKv5E4jRDruwLYgSgjP6SgvQ/Q6pYwyrnFwt1qBi3lPqFP239NYaMSfIE+mmYF7Oxj+3+aA0ev0n0Y8l7lpLP8NEIFv2Q1ZWe0Qv8jkSv0vxNxzV0q6XxCnqjEPLWhpPvS2Mumn0nHU0d+2GLxAeI1nQicKalYH1tJU4HlgJUkTWfofzcNeGYhs28lBGbbvK3PubHSpBLcIek1RHXoUzLbaPibpD2IkDWEpzbLemJgF2OtSeI3wM8lfY8IRb2ejD371HGH+jTmDcSkMS0d5y4h32bhf5KV5zmpgkv6KlHNmR2Fuv+rgWdquEjlNDJqwvWUPy+A84auR2sWX6yRvLvRNJssSU7bz1QoU6paqpP8tCT78BjhXSxSTdkxTdhrXeL1NM3qX0fGz7Ht7xKaTssTc/ihRG7rScC5ztSVwfbkHOMsoq3jgOPSwv/NwHeBVSW9n3hN/z+juf2I/9mqDM/buo/YXGUjSfm8BVgzpe40PJkhbb2cfCw5Jg4j8rmmEekbJdg72fg8cd+/ikzzxMAq8CuE2EbEmSowJDUeqb4d6m3vn8NOj82uqik7QdJ1tjce6TijnQ2JJNKjGN4Y937gUtv39H3g47dz6ii/tjMKLEp6lP695QRMtZ3dOybpcuBVwFeIBOA7gLd5uAxFDjvHAKsT+UgQN5Q/2j4sp52WvWXoID9N0iwPb4A+4UnXxGucqshTPtSPbGetuu6xOZ2YZ3e1nW0D2bHMTq/t9YmFzC7O2CmhNf5Btk9Y+F+OycazCS3PTzLc43s/EUosJYY+oRnYxVjXSLqG4R3qlyLa7Gw6+iMXy9Y5RDVlUxK9J7Ch7VzVlJ3Ss6AQsCzRtLmIAn96b6YQbYrm5hx7SSBNtncSXqp3E5Vn/9OE+DLamQTsSyz8AC4ETnZKfM9sq+tqyiYJvV3sU8Jr0AmS5gIbNAvYtLCd4/ztg/qFn+7P/T6lSMpBLi+z0wmSXmn7kpG89pm99Z2Sqin3YcH2bCW6CpxG5L39Ix1PB47NYWtgw5QNKS+jX/uH3GWvXXWoh/LVlJ3SZWggsT2hP7c04Up/IeFZzKqTJWkVIj9oVds7SHoesJntr+a0Mw68wfbxhPL0RwEkHUL+XJCDkp0vNicK2YFuqyl3Td8PbJ0rIjDbIV8DZkpqJBnewNBmMSfXEYLG9xCbtRWBv0i6k8gxzNWWqbjSfx+ZiaYTSIlN6MsIRYHX9fmdWVB0drFR9+3tvkfkG19EuTzPhg2ahRjMz2PdaLQHLCoD7xlTNIRumEqoks+z/b7MdjrpUJ9sXQ0cbvtn6XgL4Bjbm43+yAqApKYlx0891NT9xiZvLaOd84iq2g/Z3lDSFKLCMaudrukXRpZ0ffO/nGh20rize8Os/c5VRibNtVumw8ttX1/AxsnAt21fkI63A3YmNBGPt/3S0R7/OOy8rN95dyTrUgpJazpJ4Ix2biIh6QbbL+zI1mzg5U1KS/LUXpZjTh94z1ifndKVabeT286p6ebbTAYlk3JLV1MOOv+2fW+PblCJXclKts+S9AEA2/NSSHZCMkpibtsjPGHs9NBlNSWSNmfBsErWRuvjwA1E/uAUAJXpprGp7X2aA9s/kXSM7X1TaDQLXSy6JE1zdC3oW/lXKGx9DtCbj/tt4EV9/nZMSDrd9p4LO5eBH0p6te0fZx63H8cCV0tqKu/fBHw8x8ADvxjrudCbxp4rZBy/98L+Y/q+qqRVnVfgE1iwmpJwo7+ZjFWiA87Nkt5CVOs9BziYqIrJzYOSnspQb8pN6aYNUymuIm62KxGTUsP95L32urLT5r10p/Z/OrA2sXhpFnwmQn0TEkkHEUVFdxKvqQm5bZDZ1B2p2vCb6XhX4M6UdJ8tlzB9Vk8g2vcsTQhhP5g5xPYNQqj0WobCkw1Zw9aKDgLPB1boyRubRn7Jjobn9zyHKRRY9BEySB+U9C9C5qJUOBTbX5M0i6Fm5zvZviXH2EtCmPJ3DHk9msaeRzUhvgzjN73fphILvdnExbABMCtn6DAtvg4kdGG+R8TIDyRKeufYztKwdNCRtBzwIWA74r26ADja9j8z29mYmNDXJwouVgZ2tj2hF80KeYGHbT+mUEZfDzivQBJ1V3YmEwvyE+mmmvKXhJzLwEy+kn4DvNR2yYbaKJp2H8FQOPRKIm/xXqIg5zeZ7Myij5ae7dzyLZ0g6fVEHt+ODMmPQGxwvmk722Y0RQI+yFAhFsQ8+wjw5Yn6PwSQdCzw1VwLsGFjD9B8MAxJLybK4P+Sjt9K5Iv9nsjlyuoClvQd4AjbN6bj9ZOdnTPa+B6RuHo1oQX2NJjf83DCJvAPGu1rL+0G9yOuvVuAj0zkqjmYn3O3FZHkfCWhxv+I7d0nop1ka6btl+QedwRbZwMH275joX88QUib0m0HRbZASX5E0hzbG6RzpfIV30h05rg3Ha9I5CV9t4CtrW1f3nNuCxdoUyTpk10tvCQ9k/Bmt8P+l4/8iMW2807CYz6FyAc+05kkpQZ5MXYd8Crbf5e0NeHWPojQmXpuzkVSsnez7V637ALnxmhjfpJ52s3fQewGs3p0BpWe/KMFyFUp1fW11zVNYn0KTS1r+zMlkmi7spNsfZ6opuyVm8iWZiDpB4SX/snEtTCT4Y3qs1bzdolCrHld4EcMf025FPgbOzOIkPIaDL/xZq2OV0daesnWAtd0wYVfv6KYUrqO/ca8F7gt56Jd0qeJcPUttML+JT9PktYlFmW7ERvFk21fOvqjRmeQc8YmtzwQuxLu0XOAc1RGBmKOpK8AX0/HuxMhy5zMD8/YflTS7XUh9rjYjMjpOxP4ORRr+9T1tdc1krQZcY2/I50rIU/SlR2IxRGEIHCDGcoNycExGcd6ovGH9LU05bokQIQNv0gskkoWw+xJ5Bi/i9DSW43wbpdgUp9zWe/N6XO0ObCyhrewmka5z9SJRLHAHGKufQGRrrGCpAOcqWsCEX5dt1RaQS/JEbJe+rqbuM+/R9J+tt+8uOMO9GJM0pS0At+GEI9sKPG6305UOR5MXHjXEirEOWn6ppFsNL3TiiUsDhhPB7YldjNvIXbxZ9q+ObOdrq+9rjmUaH10ru2bU+XhmHaF42ynk36iTYWepE/bfn/7d2l3P2FlE5ypo8kiMM/2SaWN2L5N0rLAMzp4bbMkfQ5o+rAeSNw/crI0oX05haEWVhDtkEp56v8MvKOZXxU6i0cB7yN0zXItxm4lvNrFF2PJg/5aQrPtE7YbZYZPK4SPF3/sAQ5TfojoQ3g30VJlY9uWtA5wmu0tCtjciNTKgrhAzrGdte9XJQ+pDH434LPAR3O+T+Nx7Q0ykp4EYPuBAmOP2ug6d5gt2ewXKpqfmzQRUaigv4+ooJtfnVcgfHgkcBdwLsPDoblzgF9HEoa2vaYKCUMnW8sDH2Z4p4mP2e7X5mystp5t+7bc445g6ybb6/c7lzPdQNGRZkPgYoZfE9n7Qiv0RM/q995IWmEs+WODsEvvi+2PS7oYeAbwk1bl0iQifycLKYdht/TVtDjpZKddefykRdhriPdrDeC/iYk9G11de10j6Tjbh7Zyn4aR+0Yl6QWE3MNT4lB/BfbK7MnspNE1gKQDgP8E1pLUrqh9MmWkVbrkDGLuey2wP6F7+NcCdho9xcNb50p0LzgSeAnRFgvbN0jKHekgjf0gw3s4luQhSZ+l8KI5cbOikXtbhuSWNAfnrIj+PsMrREuyh+1hvYclXWx7m7Em8g+sZ6wrJD1GtGJ4R1NWLelW2xO5tclAIulrhMzEj4ly7pvG+SlNKCS9yPa16kidXNJVRPeCS9Pxy4nQwOY57aSxize6Vog0T6dPA+VBqLC1/aKe6sNf2H7xeD+3xUHSNbY3bSfSl/JedlWUkGz9hFg0v5fWork3bJ7J1rLE5qMtQ3Ii0UZtuZyebklLAzPS4Vznl7+ZCixHpEm8nKF842nA+c7QVL4uxsaIpDcQejRbAOcTu4Cv2C6yi6osPmnh3LiXu+iZNrCksBS2S3g/GhudtShSR42ue2w+jeHeiQnblLq1eLmA8Db/mWhbtHam8TtpdC3px0TO1v8jwl7/RSTuHwwsZXv/HHZ6bM4mihKupVWU4Hx9Ntu2BmrRDPM3aacRslUiii3e6ozSFoqeuIcCqwJ/Ymgxdh9RSTnmNJeBDVN2hUML5rsp7v964g17WnLPnpuxYqQyRmz3q1qqPA5Szs67iJCrJM0DTrB91KgPXDxulfRh4PR0vAeRi1mCrhpdN/lInyMm9rsIfaRf0qNYPsH4WPL8HUYIHU8j5sJcdNXo+lRCBPp0wov+L0Ip/wLg6Ew2eumkKCHReIzukPQaYtHctx3TWFH0TD6SBfW/ckeNjgW2sz032Z1BVMxnU/u3fbykLwAftF3kOqiesQJImk70rNrV9jbj/XwqlRykZPcdgH2dGgunCseTCFf95zPbm06oq29J3HCvIIot7slpp2VvY0JkFgo1uk52ZhOSGRfZ3kjSK4hclHcs5KETCkmH2j5uvJ/H4yUVjHwY2J5YlDU3SRcq6DiSDooSkq3XEp+j1RhaNB9p+wcFbP2KkAXp9fhl7dLQL3xcMKRcRP8N6mKsUqksIpKuJ1TW7+45vzJRqJBlkkr5GfsD6wA3AqfkzgEZwe6WwHNsn5pe05OaRWdmO426+2xgI0e7pyLh1/FE0h9sr55prM6qXlP+0X8RlfHfpJXSUELmQtGyrxd3lXdcatEs6ee2X5p73D52TiH6krY1Pifb3ruArWOIDjjfcebFUw1TViqVRWWp3oUYRN6YpKUy2jmNCKdcQXjinkvekNcCSDqC6EG4LhGqWoqY3EvIkPwjeV8uB86QdBct1f8BIqeocidVr5K2J0LI3yckaR5ayEPGzBMgv/g9QAkP5qWpcvM7DPf4ZetqkTiAyPNrpCyuIAoFSrAf8f96VNLDZMw3rp6xSqWySPTTx1qU3y2GnXbbrynAzFxjj2LzBmAj4LoOqueWBx4m8u52B1YAzsgdvhlvcnrGWmMWrXqVdAWwf2b5lIXZ3Kvfedtf68j+H22vVmDcfgLNLiSjMeGpnrFKpbKotDtAtBGtqsAMtNt+zZNKda0axiO2Lckwf8FUBA8JRj4m6UfA33KHPLpC0v300ZwjdQgpYHIV4JHW8SPpXBZsb7Xwv8pOu5JxKtG14zqiqKQLilx7Lqy1Keks27tIupH+uoclNlIiNlBr2j5a0mpEl4Yxe2frYqxSqSwStkv1sOtlPNp+nSXpS8CKkvYB9gZOzmlA0qbAp4C/E5V5pwMrAZMk7WX7/Jz2usD2kxf+V1npV/X6vx0/h6zYHiYELWlFhoRSszAOi+ZGV+8IoPFaXkZ0MRiTOGqLQ9L312Yab1E4kchPeyXxGX6AaGM1ZmmQGqasVCoVQNK2wHbEDeoC2xdmHn8W8EEiLPllYAfb10haj+iRWqRKa9Doqup1vEj5lzeV1LjrAkWbopsYkojZE9jQdl+tuIlAk47RIwacpfimesYqlUoFSIuvCyWtBJTI35rS6A5KOsr2NcnurzoKxU5YJLW1sH6fvub/roQMRFdoeHuxyUTBylnj94yysbbt/2gdfzTlZmZlBK/fvcAs4DDbObUJ/y1pcmMvVV0/lmPguhirVCpLLB2HDtuT9sM9v6shitG5lvgfNavW5v8lyvSm7JJjWj/PA26zfft4PZmMPCxpS9s/g/kisL3XfQ6OA24nxHlFdMRZm8i7O4VoX5SLppfxKpI+DuxMdGsYMzVMWalUlli6DB1KepSQsGjydBrZBAFTbeeUB6lMICStwlDe0Uzbd43n88mBpA2JHL8V0ql7iDZFczLb6dc27QbbLyyh35fmhkbM/RLbv8wxbvWMVSqVJZnOQocdFkAMHJLWS+9JX4mTAtpVnSFpF+CzwE+JhfkJkg63/e1xfWJjxPZsohhnWjq+T9KhQNbFGPBQ+h82/6+diWbkUMbjvBwRTjYZix+qZ6xSqSyxtPXRerXScmqnVcaGpC/b3ncQtatSJ4ZtG29YykO6aNA6MkAx7bm1gOOBzYgF0jVEG6Y/AS9qwqSZbH2EaHV4DrFwfgNwtu2PjXnsuhirVCpLKjV0WBlv2iLH6XgSMLt9blAoJTDbFZLmEhWh/0zHywI35Kh8rWHKSqWyxFJDhxOL8VarL8T5ki4AzkzHuwI/HsfnU5Ls3h9JM4CTgFVsry9pA2DHHN6qPvyZEOZtwqDLEB64MVM9Y5VKpVKZEEg6oXU4X63e9s7j9JQWG0nrEAuIKyXtBGyZfvUPoj3Wb8fv2S0+CxOYtZ3VCSTpMuBw4Est7a+bbK+f004a97tEocWFxGvcluiNejuA7YNHfvToVM9YpVKpVCYEXajVd8hxwAcAbH+HaKiNpBek371u/J7a4jMOXRmWsz2zp+BmXiFb56avhp/mGrguxiqVSqUyUXkQWHO8n8RisortG3tP2r5R0hrdP50Jy92S1mZIiHVn4I4ShmyfJmlpYEY6Ndf2v0d7zKJSF2OVSqVSmRD0qNVPAp7HxFWrX3GU3xXpFzmgHEhoBK4n6U/A74hm3tmR9HKivdPvibDrapLeavvyMY9dc8YqlUqlMhGQ9LLW4YRWq5d0JiEaenLP+XcSUhe7js8zm5hIWp5YoD8EvNn2GQVsXAu8xfbcdDyDEId+0ZjHrouxSqVSqUw0mh6inqA3saS6fy7wCNHuCWATYGngjbb/Ml7PbSKQxGQPBJ4JfA+4KB0fBsyx/foCNufY3mBh5xZr7Al6HVcqlUplCWG0HqJA7h6inSLpFUBT+Xez7UvG8/lMFCR9j2ixdDVRVfs0InR4iO3sDcmTzVOBR4Gvp1O7A5Nt7z3msetirFKpVCpPZLrsIVqZGLTFciVNJpL2V28EWQvZXIbwvjUyJFcAJ9r+11jHrgn8lUqlUnmi01kP0cqEYX4Vo+1HJd1eeCE2meiMsB7wudzj18VYpVKpVJ7oPNb6+eGe39XwzpLJhpLuSz8LWDYdi+hXOi2nsbTgmytpddt/yDk21MVYpVKpVJ74bNi60S7bcxOeOn5PqzJejFMrs+nAzZJmEhp3zXPZcawD18VYpVKpVJ7Q1B6ilScIHy41cF2MVSqVSqVSqYyApKnA/sA6wI3AV21nbblUqykrlUqlUqlURkDSt4iCgSuAHQix4UOy2qiLsUqlUqlUKpX+9MhoTAFm2t44p41JOQerVCqVSqVSGTDaMhpZw5MN1TNWqVQqlUqlMgKSHmWoelJEI/eHyCijURdjlUqlUqlUKuNIDVNWKpVKpVKpjCN1MVapVCqVSqUyjtTFWKVSqVQqlco4UhdjlUqlUqlUKuNIXYxVKpVKpVKpjCP/B/oUTtN4trgTAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(data.isna(), cbar=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ngUwXZKK_Dv"
      },
      "outputs": [],
      "source": [
        "data.drop(\"YearBuilt\", axis = 1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J4uxV6A4LPSB"
      },
      "outputs": [],
      "source": [
        "data.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KstNX1w7LWOC",
        "outputId": "aea2bc88-d49b-4abd-b941-ba17d8aa131e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fecac6c2a10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.heatmap(data.isna(), cbar=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hMB1yVxSR6wY"
      },
      "source": [
        "# Handling non numeric values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8ae8p0IGR-m-",
        "outputId": "7dc41bcd-32c5-4a8d-b91c-795ca9b23ee2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Suburb            object\n",
              "Address           object\n",
              "Rooms              int64\n",
              "Type              object\n",
              "Price            float64\n",
              "Method            object\n",
              "SellerG           object\n",
              "Date              object\n",
              "Distance         float64\n",
              "Postcode         float64\n",
              "Bedroom2         float64\n",
              "Bathroom         float64\n",
              "Car              float64\n",
              "Landsize         float64\n",
              "BuildingArea     float64\n",
              "CouncilArea       object\n",
              "Lattitude        float64\n",
              "Longtitude       float64\n",
              "Regionname        object\n",
              "Propertycount    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZC_xs3acSABQ"
      },
      "outputs": [],
      "source": [
        "data.drop([\"Suburb\", \"Address\", \"Method\", \"SellerG\", \"Date\", \"CouncilArea\", \"Type\"], axis = 1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "data": {
            "text/plain": [
              "Rooms              int64\n",
              "Price            float64\n",
              "Distance         float64\n",
              "Postcode         float64\n",
              "Bedroom2         float64\n",
              "Bathroom         float64\n",
              "Car              float64\n",
              "Landsize         float64\n",
              "BuildingArea     float64\n",
              "Lattitude        float64\n",
              "Longtitude       float64\n",
              "Regionname        object\n",
              "Propertycount    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "53MXvB5SShkD"
      },
      "outputs": [],
      "source": [
        "one_hot = pd.get_dummies(data['Regionname'])"
      ]
    },
    {
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          "metadata": {},
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      "source": [
        "one_hot"
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    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "data.drop(\"Regionname\", axis = 1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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              "       Rooms      Price  Distance  Postcode  Bedroom2  Bathroom  Car  \\\n",
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              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.79690</td>\n",
              "      <td>144.99690</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.00000</td>\n",
              "      <td>-37.80720</td>\n",
              "      <td>144.99410</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12208</th>\n",
              "      <td>3</td>\n",
              "      <td>1285000.0</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.86335</td>\n",
              "      <td>144.89487</td>\n",
              "      <td>6380.0</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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12209</th>\n",
              "      <td>2</td>\n",
              "      <td>560000.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3181.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.60000</td>\n",
              "      <td>-37.85581</td>\n",
              "      <td>144.99025</td>\n",
              "      <td>4380.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12210</th>\n",
              "      <td>3</td>\n",
              "      <td>525300.0</td>\n",
              "      <td>25.5</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.61387</td>\n",
              "      <td>145.03850</td>\n",
              "      <td>2940.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
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              "    <tr>\n",
              "      <th>12211</th>\n",
              "      <td>2</td>\n",
              "      <td>750000.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.81649</td>\n",
              "      <td>144.86731</td>\n",
              "      <td>6543.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>12212</th>\n",
              "      <td>6</td>\n",
              "      <td>2450000.0</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>388.50000</td>\n",
              "      <td>-37.81038</td>\n",
              "      <td>144.89389</td>\n",
              "      <td>6543.0</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>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>12211 rows × 20 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff25bad0-4006-49bb-ab79-f01c02e9fb86')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ],
      "source": [
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TAtKXyEITGNL",
        "outputId": "b4901122-a136-49f3-84be-f489a8715c15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rooms                           int64\n",
              "Price                         float64\n",
              "Distance                      float64\n",
              "Postcode                      float64\n",
              "Bedroom2                      float64\n",
              "Bathroom                      float64\n",
              "Car                           float64\n",
              "Landsize                      float64\n",
              "BuildingArea                  float64\n",
              "Lattitude                     float64\n",
              "Longtitude                    float64\n",
              "Propertycount                 float64\n",
              "Eastern Metropolitan            uint8\n",
              "Eastern Victoria                uint8\n",
              "Northern Metropolitan           uint8\n",
              "Northern Victoria               uint8\n",
              "South-Eastern Metropolitan      uint8\n",
              "Southern Metropolitan           uint8\n",
              "Western Metropolitan            uint8\n",
              "Western Victoria                uint8\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ],
      "source": [
        "data.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jjv4hYfmM0md"
      },
      "source": [
        "# Building your own model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-HyAyyl3PLQE"
      },
      "outputs": [],
      "source": [
        "x = data.drop(\"Price\", axis=1)\n",
        "y = data[\"Price\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "OIU6HvuiYpQd",
        "outputId": "b7593039-9e14-4476-bef0-580572cf1478"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Rooms  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
              "0          2       2.5    3067.0       2.0       1.0  1.0     202.0   \n",
              "1          2       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
              "2          3       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
              "3          3       2.5    3067.0       3.0       2.0  1.0      94.0   \n",
              "4          4       2.5    3067.0       3.0       1.0  2.0     120.0   \n",
              "...      ...       ...       ...       ...       ...  ...       ...   \n",
              "12208      3       6.8    3016.0       3.0       1.0  0.0     296.0   \n",
              "12209      2       4.6    3181.0       2.0       1.0  1.0       0.0   \n",
              "12210      3      25.5    3750.0       3.0       2.0  2.0     400.0   \n",
              "12211      2       6.3    3013.0       2.0       1.0  2.0     269.0   \n",
              "12212      6       6.3    3013.0       6.0       3.0  2.0    1087.0   \n",
              "\n",
              "       BuildingArea  Lattitude  Longtitude  Propertycount  \\\n",
              "0         151.96765  -37.79960   144.99840         4019.0   \n",
              "1          79.00000  -37.80790   144.99340         4019.0   \n",
              "2         150.00000  -37.80930   144.99440         4019.0   \n",
              "3         151.96765  -37.79690   144.99690         4019.0   \n",
              "4         142.00000  -37.80720   144.99410         4019.0   \n",
              "...             ...        ...         ...            ...   \n",
              "12208     151.96765  -37.86335   144.89487         6380.0   \n",
              "12209      61.60000  -37.85581   144.99025         4380.0   \n",
              "12210     151.96765  -37.61387   145.03850         2940.0   \n",
              "12211     151.96765  -37.81649   144.86731         6543.0   \n",
              "12212     388.50000  -37.81038   144.89389         6543.0   \n",
              "\n",
              "       Eastern Metropolitan  Eastern Victoria  Northern Metropolitan  \\\n",
              "0                         0                 0                      1   \n",
              "1                         0                 0                      1   \n",
              "2                         0                 0                      1   \n",
              "3                         0                 0                      1   \n",
              "4                         0                 0                      1   \n",
              "...                     ...               ...                    ...   \n",
              "12208                     0                 0                      0   \n",
              "12209                     0                 0                      0   \n",
              "12210                     0                 0                      1   \n",
              "12211                     0                 0                      0   \n",
              "12212                     0                 0                      0   \n",
              "\n",
              "       Northern Victoria  South-Eastern Metropolitan  Southern Metropolitan  \\\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",
              "12208                  0                           0                      0   \n",
              "12209                  0                           0                      1   \n",
              "12210                  0                           0                      0   \n",
              "12211                  0                           0                      0   \n",
              "12212                  0                           0                      0   \n",
              "\n",
              "       Western Metropolitan  Western Victoria  \n",
              "0                         0                 0  \n",
              "1                         0                 0  \n",
              "2                         0                 0  \n",
              "3                         0                 0  \n",
              "4                         0                 0  \n",
              "...                     ...               ...  \n",
              "12208                     1                 0  \n",
              "12209                     0                 0  \n",
              "12210                     0                 0  \n",
              "12211                     1                 0  \n",
              "12212                     1                 0  \n",
              "\n",
              "[12211 rows x 19 columns]"
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              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.79690</td>\n",
              "      <td>144.99690</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.00000</td>\n",
              "      <td>-37.80720</td>\n",
              "      <td>144.99410</td>\n",
              "      <td>4019.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12208</th>\n",
              "      <td>3</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.86335</td>\n",
              "      <td>144.89487</td>\n",
              "      <td>6380.0</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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12209</th>\n",
              "      <td>2</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3181.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61.60000</td>\n",
              "      <td>-37.85581</td>\n",
              "      <td>144.99025</td>\n",
              "      <td>4380.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12210</th>\n",
              "      <td>3</td>\n",
              "      <td>25.5</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.61387</td>\n",
              "      <td>145.03850</td>\n",
              "      <td>2940.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12211</th>\n",
              "      <td>2</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>269.0</td>\n",
              "      <td>151.96765</td>\n",
              "      <td>-37.81649</td>\n",
              "      <td>144.86731</td>\n",
              "      <td>6543.0</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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12212</th>\n",
              "      <td>6</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1087.0</td>\n",
              "      <td>388.50000</td>\n",
              "      <td>-37.81038</td>\n",
              "      <td>144.89389</td>\n",
              "      <td>6543.0</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>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12211 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-08d43592-d4cf-4ca0-8ca6-9dca075a3a5e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-08d43592-d4cf-4ca0-8ca6-9dca075a3a5e 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-08d43592-d4cf-4ca0-8ca6-9dca075a3a5e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "-DL5A0jsYrv9",
        "outputId": "f1a9efb2-6223-4219-9168-e8723e69f9b7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1480000.0\n",
              "1        1035000.0\n",
              "2        1465000.0\n",
              "3         850000.0\n",
              "4        1600000.0\n",
              "           ...    \n",
              "12208    1285000.0\n",
              "12209     560000.0\n",
              "12210     525300.0\n",
              "12211     750000.0\n",
              "12212    2450000.0\n",
              "Name: Price, Length: 12211, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c748VbL4Le3a"
      },
      "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=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HDoIjiCvNt_n"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression # Package for Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MOBRTzuDNyWc"
      },
      "outputs": [],
      "source": [
        "model = LinearRegression()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eFJbq5nwN0bU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "61f9da23-e811-4e1c-da1b-dce7010c10c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ],
      "source": [
        "model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fsz5L4G3N04J"
      },
      "outputs": [],
      "source": [
        "y_pred = model.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Mnfp23kN5F9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65bb2aa9-08f1-4a92-832d-667e21c91ca4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1026258.54883724,  577927.4295018 ,  614617.11495852, ...,\n",
              "       1574602.8505891 , 1152301.09406713,  653407.02303377])"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFOtyOObWVae",
        "outputId": "06488424-6290-4bba-9d28-dd7857e6c44b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5697      480000.0\n",
              "6518     1000000.0\n",
              "8059      365000.0\n",
              "6060      645400.0\n",
              "850       702000.0\n",
              "           ...    \n",
              "10555     500000.0\n",
              "5977      555000.0\n",
              "8736     1823000.0\n",
              "1334     1815000.0\n",
              "6618      710000.0\n",
              "Name: Price, Length: 3664, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PallVIi8N69N"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, y_pred) ** 0.5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Y-9iLWBVB4k",
        "outputId": "a6477e51-4fd8-403d-b7ad-63dd8298ffbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "459803.0595627626"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's try different Model\n"
      ],
      "metadata": {
        "id": "JdEKQNdEW4Y4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ],
      "metadata": {
        "id": "JHyJ4cLbXVlu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "YCsjWoQMYl2X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree_model = DecisionTreeRegressor(random_state=1)"
      ],
      "metadata": {
        "id": "zfoeL35TXiXf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fIDVgkiSXmj6",
        "outputId": "c0cc9980-1ad1-4095-ece1-d411e80027d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeRegressor(random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = tree_model.predict(X_test)"
      ],
      "metadata": {
        "id": "RaOVTRjuXrOm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXF9gjfPXvLS",
        "outputId": "47ae9da3-f553-421e-a455-d4bf90f69b87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 614000., 1200000., 1230000., ...,  650000.,  715500.,  888000.])"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uOSFO95wXxSI",
        "outputId": "376bb055-b6d6-4ab2-9362-0e460a5eae1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10114     460000.0\n",
              "2402      800000.0\n",
              "7252      830000.0\n",
              "8286      741000.0\n",
              "3548     1900000.0\n",
              "           ...    \n",
              "6471      460000.0\n",
              "2299      765000.0\n",
              "12089     696000.0\n",
              "6455      800000.0\n",
              "10087     749000.0\n",
              "Name: Price, Length: 3664, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_test, y_pred) ** 0.5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwtfSO41XzWR",
        "outputId": "658bd4ab-bf15-4c6e-990a-2c2a3c284ce5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "443600.67551085417"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vXsj56zGX5NR"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}