Rental_Property_Prices_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JIAMDm1IluV"
      },
      "source": [
        "## **Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLkfdPJ-W6Zo",
        "outputId": "a2d4f22f-814a-4c41-c70e-b345e5920b15"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade scikit-learn==1.3.2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "AKFZdExfaxEV"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "pd.set_option('display.max_columns', None)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "rVFFtk_Obj_7",
        "outputId": "3cac36e9-958e-4b7d-d2a1-872910a35202"
      },
      "outputs": [
        {
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              "      <th>id</th>\n",
              "      <th>type</th>\n",
              "      <th>locality</th>\n",
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              "      <th>longitude</th>\n",
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            ],
            "text/plain": [
              "                                     id  type       locality  \\\n",
              "0      ff8081815733a243015733b2876600a6  BHK2            NaN   \n",
              "1      ff8081815ee25e15015ee50004da2acd  BHK2      Bellandur   \n",
              "2      ff80818163f29c9e0163f46770b873e7  BHK3  Thiruvanmiyur   \n",
              "3      ff80818164b68a700164b74b1b247a1d  BHK1      Attiguppe   \n",
              "4      ff80818163b1faf00163b4b9b8a163b1  BHK3      Kodihalli   \n",
              "...                                 ...   ...            ...   \n",
              "20550                                19   NaN            NaN   \n",
              "20551                                20  BHK2            NaN   \n",
              "20552                                21  BHK2            NaN   \n",
              "20553                                22  BHK2            NaN   \n",
              "20554                                23  BHK1            NaN   \n",
              "\n",
              "           activation_date   latitude  longitude lease_type  gym  lift  \\\n",
              "0         21-04-2018 14:44  12.934471  77.634471     FAMILY    1     1   \n",
              "1      2017-09-10 12:48:00  12.929557  77.672280     ANYONE    0     1   \n",
              "2      2018-12-06 22:02:00  12.982870  80.262012     FAMILY    0     1   \n",
              "3         20-07-2018 16:22  12.955991  77.531634     FAMILY    0     0   \n",
              "4         31-05-2018 17:41  12.963903  77.649446     FAMILY    0     0   \n",
              "...                    ...        ...        ...        ...  ...   ...   \n",
              "20550                  NaN        NaN        NaN        NaN    1     1   \n",
              "20551                  NaN        NaN        NaN        NaN    0     0   \n",
              "20552                  NaN        NaN        NaN        NaN    1     1   \n",
              "20553                  NaN        NaN        NaN        NaN    0     1   \n",
              "20554                  NaN        NaN        NaN        NaN    0     0   \n",
              "\n",
              "       swimming_pool  negotiable      furnishing      parking  property_size  \\\n",
              "0                  1           0  SEMI_FURNISHED         BOTH           1250   \n",
              "1                  0           1  SEMI_FURNISHED         BOTH           1400   \n",
              "2                  0           0  SEMI_FURNISHED         BOTH           1350   \n",
              "3                  0           1  SEMI_FURNISHED  TWO_WHEELER            600   \n",
              "4                  0           1  SEMI_FURNISHED         BOTH           1500   \n",
              "...              ...         ...             ...          ...            ...   \n",
              "20550              1           1  SEMI_FURNISHED         BOTH           1650   \n",
              "20551              0           1  SEMI_FURNISHED         BOTH           1000   \n",
              "20552              0           1  SEMI_FURNISHED         BOTH           1000   \n",
              "20553              0           0  SEMI_FURNISHED         BOTH           1000   \n",
              "20554              0           1  SEMI_FURNISHED  TWO_WHEELER            500   \n",
              "\n",
              "       property_age  bathroom facing  cup_board  floor  total_floor  \\\n",
              "0              25.0       2.0      E        2.0    6.0         12.0   \n",
              "1               4.0       2.0     NE        2.0    3.0          4.0   \n",
              "2               6.0       3.0      E        3.0    1.0          5.0   \n",
              "3               3.0       1.0      E        1.0    1.0          2.0   \n",
              "4              15.0       3.0      E        4.0    0.0          0.0   \n",
              "...             ...       ...    ...        ...    ...          ...   \n",
              "20550           NaN       NaN    NaN        NaN    NaN          NaN   \n",
              "20551           NaN       NaN    NaN        NaN    NaN          NaN   \n",
              "20552           NaN       NaN    NaN        NaN    NaN          NaN   \n",
              "20553           NaN       NaN    NaN        NaN    NaN          NaN   \n",
              "20554           NaN       NaN    NaN        NaN    NaN          NaN   \n",
              "\n",
              "                                               amenities water_supply  \\\n",
              "0      {\"LIFT\":true,\"GYM\":true,\"INTERNET\":true,\"AC\":f...    CORP_BORE   \n",
              "1      {\"LIFT\":true,\"GYM\":false,\"INTERNET\":false,\"AC\"...  CORPORATION   \n",
              "2      {\"LIFT\":true,\"GYM\":false,\"INTERNET\":false,\"AC\"...    CORP_BORE   \n",
              "3      {\"LIFT\":false,\"GYM\":false,\"INTERNET\":false,\"AC...  CORPORATION   \n",
              "4      {\"LIFT\":false,\"GYM\":false,\"INTERNET\":false,\"AC...  CORPORATION   \n",
              "...                                                  ...          ...   \n",
              "20550                                                NaN          NaN   \n",
              "20551                                                NaN          NaN   \n",
              "20552                                                NaN          NaN   \n",
              "20553                                                NaN          NaN   \n",
              "20554                                                NaN          NaN   \n",
              "\n",
              "      building_type  balconies     rent  \n",
              "0                AP        2.0  40000.0  \n",
              "1                AP        2.0  22000.0  \n",
              "2                AP        3.0  28000.0  \n",
              "3                IH        0.0   8000.0  \n",
              "4                IH        1.0  45000.0  \n",
              "...             ...        ...      ...  \n",
              "20550           NaN        NaN      NaN  \n",
              "20551           NaN        NaN      NaN  \n",
              "20552           NaN        NaN      NaN  \n",
              "20553           NaN        NaN      NaN  \n",
              "20554           NaN        NaN      NaN  \n",
              "\n",
              "[20555 rows x 25 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_excel('https://raw.githubusercontent.com/gopiashokan/Rental-Property-Prices-Prediction/main/Dataset/House_Rent_Train.xlsx')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZTspgrT0cnKA",
        "outputId": "f57fbc24-702b-4962-c0a4-dfca528e0143"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                 20523\n",
              "type                   9\n",
              "locality            1917\n",
              "activation_date    19566\n",
              "latitude           17733\n",
              "longitude          17726\n",
              "lease_type             4\n",
              "gym                    2\n",
              "lift                   2\n",
              "swimming_pool          2\n",
              "negotiable             2\n",
              "furnishing             3\n",
              "parking                4\n",
              "property_size       1141\n",
              "property_age          44\n",
              "bathroom               8\n",
              "facing                 8\n",
              "cup_board             19\n",
              "floor                 23\n",
              "total_floor           27\n",
              "amenities           4994\n",
              "water_supply           3\n",
              "building_type          4\n",
              "balconies              9\n",
              "rent                 206\n",
              "dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
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          "height": 226
        },
        "id": "IpRDrvCweWHw",
        "outputId": "0e80132f-d94d-4ecc-b56c-6a3d328927e5"
      },
      "outputs": [
        {
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-36db349c-5898-4332-b846-c332eccc9a55 button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-36db349c-5898-4332-b846-c332eccc9a55');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type locality activation_date  latitude  longitude lease_type  gym  \\\n",
              "20550   NaN      NaN             NaN       NaN        NaN        NaN    1   \n",
              "20551  BHK2      NaN             NaN       NaN        NaN        NaN    0   \n",
              "20552  BHK2      NaN             NaN       NaN        NaN        NaN    1   \n",
              "20553  BHK2      NaN             NaN       NaN        NaN        NaN    0   \n",
              "20554  BHK1      NaN             NaN       NaN        NaN        NaN    0   \n",
              "\n",
              "       lift  swimming_pool  negotiable      furnishing      parking  \\\n",
              "20550     1              1           1  SEMI_FURNISHED         BOTH   \n",
              "20551     0              0           1  SEMI_FURNISHED         BOTH   \n",
              "20552     1              0           1  SEMI_FURNISHED         BOTH   \n",
              "20553     1              0           0  SEMI_FURNISHED         BOTH   \n",
              "20554     0              0           1  SEMI_FURNISHED  TWO_WHEELER   \n",
              "\n",
              "       property_size  property_age  bathroom facing  cup_board  floor  \\\n",
              "20550           1650           NaN       NaN    NaN        NaN    NaN   \n",
              "20551           1000           NaN       NaN    NaN        NaN    NaN   \n",
              "20552           1000           NaN       NaN    NaN        NaN    NaN   \n",
              "20553           1000           NaN       NaN    NaN        NaN    NaN   \n",
              "20554            500           NaN       NaN    NaN        NaN    NaN   \n",
              "\n",
              "       total_floor amenities water_supply building_type  balconies  rent  \n",
              "20550          NaN       NaN          NaN           NaN        NaN   NaN  \n",
              "20551          NaN       NaN          NaN           NaN        NaN   NaN  \n",
              "20552          NaN       NaN          NaN           NaN        NaN   NaN  \n",
              "20553          NaN       NaN          NaN           NaN        NaN   NaN  \n",
              "20554          NaN       NaN          NaN           NaN        NaN   NaN  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# id columns have 99.8% unique values, so drop the column\n",
        "\n",
        "df.drop(columns='id', inplace=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pa9Jzclqp7jp",
        "outputId": "19dc6f3f-ed5a-4f03-8782-c745c39d4caf"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "type                27\n",
              "locality           160\n",
              "activation_date     23\n",
              "latitude            23\n",
              "longitude           23\n",
              "lease_type          23\n",
              "gym                  0\n",
              "lift                 0\n",
              "swimming_pool        0\n",
              "negotiable           0\n",
              "furnishing           0\n",
              "parking              0\n",
              "property_size        0\n",
              "property_age        23\n",
              "bathroom            23\n",
              "facing              23\n",
              "cup_board           23\n",
              "floor               23\n",
              "total_floor         23\n",
              "amenities           23\n",
              "water_supply        23\n",
              "building_type       23\n",
              "balconies           23\n",
              "rent                23\n",
              "dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "NePhkXryqFnX",
        "outputId": "6f920b4f-4a7c-49a5-e3d7-835fffdcb5d2"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>type</th>\n",
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              "      <th>total_floor</th>\n",
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              "      <td>{\"LIFT\":true,\"GYM\":false,\"INTERNET\":true,\"AC\":...</td>\n",
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              "      <td>AP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20000.0</td>\n",
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              "    <tr>\n",
              "      <th>20373</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
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              "      <th>20374</th>\n",
              "      <td>BHK2</td>\n",
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              "      <td>21-12-2017 21:18</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>{\"LIFT\":true,\"GYM\":true,\"INTERNET\":true,\"AC\":f...</td>\n",
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              "      <td>Indiranagar</td>\n",
              "      <td>28-06-2018 21:58</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
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              "        const element = document.querySelector('#df-66097170-55ac-46c9-93a8-e964fca06cbd');\n",
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              "\n",
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              "          '<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",
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              "\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "       type               locality      activation_date   latitude  longitude  \\\n",
              "20372  BHK1                 Ulsoor  2017-11-10 17:08:00  12.979762  77.615395   \n",
              "20373  BHK2  Koramangala 4th Block     19-10-2017 20:31  12.931072  77.632361   \n",
              "20374  BHK2     Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2            Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1      Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20372     ANYONE    0     1              0           1  SEMI_FURNISHED   \n",
              "20373     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20372  TWO_WHEELER            850          30.0       2.0      E        1.0   \n",
              "20373         BOTH           1000          10.0       2.0      N        2.0   \n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor                                          amenities  \\\n",
              "20372    1.0          4.0  {\"LIFT\":true,\"GYM\":false,\"INTERNET\":true,\"AC\":...   \n",
              "20373    1.0          2.0  {\"LIFT\":false,\"GYM\":false,\"INTERNET\":true,\"AC\"...   \n",
              "20374    5.0          5.0  {\"LIFT\":true,\"GYM\":true,\"INTERNET\":true,\"AC\":f...   \n",
              "20375    2.0          4.0  {\"LIFT\":true,\"GYM\":false,\"INTERNET\":true,\"AC\":...   \n",
              "20376    1.0          1.0  {\"LIFT\":false,\"GYM\":false,\"INTERNET\":false,\"AC...   \n",
              "\n",
              "      water_supply building_type  balconies     rent  \n",
              "20372    CORP_BORE            AP        1.0  20000.0  \n",
              "20373  CORPORATION            IH        2.0  30000.0  \n",
              "20374     BOREWELL            AP        2.0  16000.0  \n",
              "20375    CORP_BORE            AP        3.0  30000.0  \n",
              "20376  CORPORATION            IH        1.0   8500.0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# drop the null values\n",
        "\n",
        "df = df.dropna().reset_index(drop=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xHKDMHVq1YM",
        "outputId": "5fc16e7e-40bd-4871-fa60-2edd1a8b4e8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "type               0\n",
              "locality           0\n",
              "activation_date    0\n",
              "latitude           0\n",
              "longitude          0\n",
              "lease_type         0\n",
              "gym                0\n",
              "lift               0\n",
              "swimming_pool      0\n",
              "negotiable         0\n",
              "furnishing         0\n",
              "parking            0\n",
              "property_size      0\n",
              "property_age       0\n",
              "bathroom           0\n",
              "facing             0\n",
              "cup_board          0\n",
              "floor              0\n",
              "total_floor        0\n",
              "amenities          0\n",
              "water_supply       0\n",
              "building_type      0\n",
              "balconies          0\n",
              "rent               0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "aENogpHDfzOn",
        "outputId": "97855b9f-651d-4c95-f6f3-48953e15173c"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'{\"LIFT\":true,\"GYM\":false,\"INTERNET\":false,\"AC\":false,\"CLUB\":false,\"INTERCOM\":false,\"POOL\":false,\"CPA\":false,\"FS\":false,\"SERVANT\":false,\"SECURITY\":true,\"SC\":true,\"GP\":false,\"PARK\":true,\"RWH\":true,\"STP\":true,\"HK\":false,\"PB\":true,\"VP\":true}'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[0,'amenities']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "Un2qT4hSfOxk",
        "outputId": "150ffe4f-9e9e-47ce-cf4d-f46657e8d0cd"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'{\"LIFT\":true,\"GYM\":false,\"INTERNET\":false,\"AC\":false,\"CLUB\":false,\"INTERCOM\":false,\"POOL\":false,\"CPA\":true,\"FS\":false,\"SERVANT\":false,\"SECURITY\":false,\"SC\":true,\"GP\":false,\"PARK\":true,\"RWH\":false,\"STP\":false,\"HK\":false,\"PB\":true,\"VP\":true}'"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[1,'amenities']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UzHQWlnvgGXI"
      },
      "outputs": [],
      "source": [
        "# convert string dictionary into dictionary\n",
        "\n",
        "def amenities_encoding(amenities):\n",
        "\n",
        "    # load the string dictionary to json [convert true=True and false=False - boolean format]\n",
        "    a = json.loads(amenities)\n",
        "\n",
        "    # value = True means convert to 1, else convert to 0 ----> [so True convert to 1 and False convert to 0]\n",
        "    b = {key:1 if value else 0 for key,value in a.items()}\n",
        "\n",
        "    # remove the existing column in df\n",
        "    for i in ['LIFT','GYM','POOL']:\n",
        "        b.pop(i, None)\n",
        "\n",
        "    return b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "kcLzMunljY1j",
        "outputId": "0c434323-8e6a-42f6-8684-e06a155afc87"
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      "outputs": [
        {
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c15b5963-f3cc-4501-9411-be5ed5884626');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-45334226-c07e-4ed3-9dc6-fba2ad2681f2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45334226-c07e-4ed3-9dc6-fba2ad2681f2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-45334226-c07e-4ed3-9dc6-fba2ad2681f2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type            locality      activation_date   latitude  longitude  \\\n",
              "20374  BHK2  Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2         Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1   Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor                                          amenities  \\\n",
              "20374    5.0          5.0  {'INTERNET': 1, 'AC': 0, 'CLUB': 1, 'INTERCOM'...   \n",
              "20375    2.0          4.0  {'INTERNET': 1, 'AC': 0, 'CLUB': 0, 'INTERCOM'...   \n",
              "20376    1.0          1.0  {'INTERNET': 0, 'AC': 0, 'CLUB': 0, 'INTERCOM'...   \n",
              "\n",
              "      water_supply building_type  balconies     rent  \n",
              "20374     BOREWELL            AP        2.0  16000.0  \n",
              "20375    CORP_BORE            AP        3.0  30000.0  \n",
              "20376  CORPORATION            IH        1.0   8500.0  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# string dictionary convert to dictionary\n",
        "\n",
        "df['amenities'] = df['amenities'].apply(lambda x: amenities_encoding(x))\n",
        "df.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Z_HcGIr_uCxq",
        "outputId": "e021f831-7b97-421e-9475-347ac3bf0c7e"
      },
      "outputs": [
        {
          "data": {
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              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d5135f47-a7d7-4296-ad9a-0b8f83ce36be button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20372         1   0   0.0         0  0.0   1      0.0         1   1  1.0   \n",
              "20373         1   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "20374         1   0   1.0         0  1.0   0      0.0         1   0  0.0   \n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20372     1  0.0  0.0   1   0  1.0  \n",
              "20373     1  1.0  0.0   0   0  0.0  \n",
              "20374     1  0.0  0.0   0   1  0.0  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# make new dataframe for amenities dictionary\n",
        "amenities_df = pd.DataFrame(df['amenities'].tolist(), index=df.index)\n",
        "\n",
        "# column names convert to lower case\n",
        "amenities_df.columns = amenities_df.columns.str.lower()\n",
        "amenities_df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pUo5nDsw6D1z",
        "outputId": "c5ffe387-90c9-4bc3-965b-8a5dc0c22c08"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>type</th>\n",
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              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>AP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>20373</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>19-10-2017 20:31</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>0</td>\n",
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              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>IH</td>\n",
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              "      <td>30000.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>20374</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>21-12-2017 21:18</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>FAMILY</td>\n",
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              "      <td>SEMI_FURNISHED</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>16000.0</td>\n",
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              "      <th>20375</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>28-06-2018 21:58</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    animation:\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "    20% {\n",
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              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type               locality      activation_date   latitude  longitude  \\\n",
              "20372  BHK1                 Ulsoor  2017-11-10 17:08:00  12.979762  77.615395   \n",
              "20373  BHK2  Koramangala 4th Block     19-10-2017 20:31  12.931072  77.632361   \n",
              "20374  BHK2     Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2            Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1      Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20372     ANYONE    0     1              0           1  SEMI_FURNISHED   \n",
              "20373     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20372  TWO_WHEELER            850          30.0       2.0      E        1.0   \n",
              "20373         BOTH           1000          10.0       2.0      N        2.0   \n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor water_supply building_type  balconies     rent  \\\n",
              "20372    1.0          4.0    CORP_BORE            AP        1.0  20000.0   \n",
              "20373    1.0          2.0  CORPORATION            IH        2.0  30000.0   \n",
              "20374    5.0          5.0     BOREWELL            AP        2.0  16000.0   \n",
              "20375    2.0          4.0    CORP_BORE            AP        3.0  30000.0   \n",
              "20376    1.0          1.0  CORPORATION            IH        1.0   8500.0   \n",
              "\n",
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20372         1   0   0.0         0  0.0   1      0.0         1   1  1.0   \n",
              "20373         1   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "20374         1   0   1.0         0  1.0   0      0.0         1   0  0.0   \n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20372     1  0.0  0.0   1   0  1.0  \n",
              "20373     1  1.0  0.0   0   0  0.0  \n",
              "20374     1  0.0  0.0   0   1  0.0  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# concatinate amenities dataframe with df\n",
        "\n",
        "df = pd.concat([df, amenities_df], axis=1)\n",
        "df.drop(columns='amenities', inplace=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TiYjf_vF_5QU",
        "outputId": "5f360796-9bea-4090-ae31-b0ea1470dc5a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "type                  0\n",
              "locality              0\n",
              "activation_date       0\n",
              "latitude              0\n",
              "longitude             0\n",
              "lease_type            0\n",
              "gym                   0\n",
              "lift                  0\n",
              "swimming_pool         0\n",
              "negotiable            0\n",
              "furnishing            0\n",
              "parking               0\n",
              "property_size         0\n",
              "property_age          0\n",
              "bathroom              0\n",
              "facing                0\n",
              "cup_board             0\n",
              "floor                 0\n",
              "total_floor           0\n",
              "water_supply          0\n",
              "building_type         0\n",
              "balconies             0\n",
              "rent                  0\n",
              "internet              0\n",
              "ac                    0\n",
              "club               1055\n",
              "intercom              0\n",
              "cpa                1055\n",
              "fs                    0\n",
              "servant            1055\n",
              "security              0\n",
              "sc                    0\n",
              "gp                 1055\n",
              "park                  0\n",
              "rwh                1055\n",
              "stp                1055\n",
              "hk                    0\n",
              "pb                    0\n",
              "vp                 1055\n",
              "dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "rHtlDEDzBOE-",
        "outputId": "225ebba3-285f-4f19-ba16-b737a47c3e84"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>locality</th>\n",
              "      <th>activation_date</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
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              "      <th>total_floor</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>building_type</th>\n",
              "      <th>balconies</th>\n",
              "      <th>rent</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20372</th>\n",
              "      <td>BHK1</td>\n",
              "      <td>Ulsoor</td>\n",
              "      <td>2017-11-10 17:08:00</td>\n",
              "      <td>12.979762</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>AP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20373</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>19-10-2017 20:31</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>IH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>20374</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>21-12-2017 21:18</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>16000.0</td>\n",
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              "      <td>BHK2</td>\n",
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              "      <td>28-06-2018 21:58</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
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              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5bbf3b45-223d-4b08-bd3b-96d822eee426\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5bbf3b45-223d-4b08-bd3b-96d822eee426')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5bbf3b45-223d-4b08-bd3b-96d822eee426 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type               locality      activation_date   latitude  longitude  \\\n",
              "20372  BHK1                 Ulsoor  2017-11-10 17:08:00  12.979762  77.615395   \n",
              "20373  BHK2  Koramangala 4th Block     19-10-2017 20:31  12.931072  77.632361   \n",
              "20374  BHK2     Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2            Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1      Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20372     ANYONE    0     1              0           1  SEMI_FURNISHED   \n",
              "20373     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20372  TWO_WHEELER            850          30.0       2.0      E        1.0   \n",
              "20373         BOTH           1000          10.0       2.0      N        2.0   \n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor water_supply building_type  balconies     rent  \\\n",
              "20372    1.0          4.0    CORP_BORE            AP        1.0  20000.0   \n",
              "20373    1.0          2.0  CORPORATION            IH        2.0  30000.0   \n",
              "20374    5.0          5.0     BOREWELL            AP        2.0  16000.0   \n",
              "20375    2.0          4.0    CORP_BORE            AP        3.0  30000.0   \n",
              "20376    1.0          1.0  CORPORATION            IH        1.0   8500.0   \n",
              "\n",
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20372         1   0   0.0         0  0.0   1      0.0         1   1  1.0   \n",
              "20373         1   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "20374         1   0   1.0         0  1.0   0      0.0         1   0  0.0   \n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20372     1  0.0  0.0   1   0  1.0  \n",
              "20373     1  1.0  0.0   0   0  0.0  \n",
              "20374     1  0.0  0.0   0   1  0.0  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Handling null values ---> replace with 0\n",
        "\n",
        "df.fillna(0, inplace=True)\n",
        "df.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b296cJ0nBncA",
        "outputId": "aca155c7-8fcb-4b47-f6cc-b40971499667"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "type               0\n",
              "locality           0\n",
              "activation_date    0\n",
              "latitude           0\n",
              "longitude          0\n",
              "lease_type         0\n",
              "gym                0\n",
              "lift               0\n",
              "swimming_pool      0\n",
              "negotiable         0\n",
              "furnishing         0\n",
              "parking            0\n",
              "property_size      0\n",
              "property_age       0\n",
              "bathroom           0\n",
              "facing             0\n",
              "cup_board          0\n",
              "floor              0\n",
              "total_floor        0\n",
              "water_supply       0\n",
              "building_type      0\n",
              "balconies          0\n",
              "rent               0\n",
              "internet           0\n",
              "ac                 0\n",
              "club               0\n",
              "intercom           0\n",
              "cpa                0\n",
              "fs                 0\n",
              "servant            0\n",
              "security           0\n",
              "sc                 0\n",
              "gp                 0\n",
              "park               0\n",
              "rwh                0\n",
              "stp                0\n",
              "hk                 0\n",
              "pb                 0\n",
              "vp                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "v7Ws-NzFBuVt",
        "outputId": "49e457e8-d202-487d-9878-0ea1ffcc68a0"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-84fa1868-8fe9-4d5e-ac96-865cfe8616c5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>locality</th>\n",
              "      <th>activation_date</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>lease_type</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>furnishing</th>\n",
              "      <th>parking</th>\n",
              "      <th>property_size</th>\n",
              "      <th>property_age</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>facing</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>building_type</th>\n",
              "      <th>balconies</th>\n",
              "      <th>rent</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20372</th>\n",
              "      <td>BHK1</td>\n",
              "      <td>Ulsoor</td>\n",
              "      <td>2017-11-10 17:08:00</td>\n",
              "      <td>12.979762</td>\n",
              "      <td>77.615395</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>AP</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20373</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>19-10-2017 20:31</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>N</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>IH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20374</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>21-12-2017 21:18</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>BOREWELL</td>\n",
              "      <td>AP</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20375</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>28-06-2018 21:58</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>E</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>AP</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20376</th>\n",
              "      <td>BHK1</td>\n",
              "      <td>Malleshwaram West</td>\n",
              "      <td>2018-08-07 22:31:00</td>\n",
              "      <td>12.998645</td>\n",
              "      <td>77.574336</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>500</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>IH</td>\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type               locality      activation_date   latitude  longitude  \\\n",
              "20372  BHK1                 Ulsoor  2017-11-10 17:08:00  12.979762  77.615395   \n",
              "20373  BHK2  Koramangala 4th Block     19-10-2017 20:31  12.931072  77.632361   \n",
              "20374  BHK2     Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2            Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1      Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20372     ANYONE    0     1              0           1  SEMI_FURNISHED   \n",
              "20373     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20372  TWO_WHEELER            850          30.0       2.0      E        1.0   \n",
              "20373         BOTH           1000          10.0       2.0      N        2.0   \n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor water_supply building_type  balconies     rent  \\\n",
              "20372    1.0          4.0    CORP_BORE            AP        1.0  20000.0   \n",
              "20373    1.0          2.0  CORPORATION            IH        2.0  30000.0   \n",
              "20374    5.0          5.0     BOREWELL            AP        2.0  16000.0   \n",
              "20375    2.0          4.0    CORP_BORE            AP        3.0  30000.0   \n",
              "20376    1.0          1.0  CORPORATION            IH        1.0   8500.0   \n",
              "\n",
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20372         1   0   0.0         0  0.0   1      0.0         1   1  1.0   \n",
              "20373         1   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "20374         1   0   1.0         0  1.0   0      0.0         1   0  0.0   \n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20372     1  0.0  0.0   1   0  1.0  \n",
              "20373     1  1.0  0.0   0   0  0.0  \n",
              "20374     1  0.0  0.0   0   1  0.0  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1 = df\n",
        "df1.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bF-Oy0ADibz",
        "outputId": "5d54fa23-d004-461f-eb44-348f1ed33541"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "type                   9\n",
              "locality            1917\n",
              "activation_date    19429\n",
              "latitude           17625\n",
              "longitude          17617\n",
              "lease_type             4\n",
              "gym                    2\n",
              "lift                   2\n",
              "swimming_pool          2\n",
              "negotiable             2\n",
              "furnishing             3\n",
              "parking                4\n",
              "property_size       1136\n",
              "property_age          44\n",
              "bathroom               8\n",
              "facing                 8\n",
              "cup_board             19\n",
              "floor                 23\n",
              "total_floor           27\n",
              "water_supply           3\n",
              "building_type          4\n",
              "balconies              9\n",
              "rent                 206\n",
              "internet               2\n",
              "ac                     2\n",
              "club                   2\n",
              "intercom               2\n",
              "cpa                    2\n",
              "fs                     2\n",
              "servant                2\n",
              "security               2\n",
              "sc                     2\n",
              "gp                     2\n",
              "park                   2\n",
              "rwh                    2\n",
              "stp                    2\n",
              "hk                     2\n",
              "pb                     2\n",
              "vp                     2\n",
              "dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "K70775tw2aAv",
        "outputId": "ec19164b-56ef-4a90-b6f6-3bed48e464da"
      },
      "outputs": [
        {
          "data": {
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              "      <th>type</th>\n",
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              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>W</td>\n",
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              "      <td>16000.0</td>\n",
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              "      <td>BHK2</td>\n",
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              "      <td>12.965502</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
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              "      <th>20376</th>\n",
              "      <td>BHK1</td>\n",
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            "text/plain": [
              "       type            locality      activation_date   latitude  longitude  \\\n",
              "20374  BHK2  Kumaraswamy Layout     21-12-2017 21:18  12.901800  77.566597   \n",
              "20375  BHK2         Indiranagar     28-06-2018 21:58  12.965502  77.647694   \n",
              "20376  BHK1   Malleshwaram West  2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20374     FAMILY    1     1              0           1  SEMI_FURNISHED   \n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20374         BOTH           1000           0.0       2.0      W        2.0   \n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor water_supply building_type  balconies     rent  \\\n",
              "20374    5.0          5.0     BOREWELL            AP        2.0  16000.0   \n",
              "20375    2.0          4.0    CORP_BORE            AP        3.0  30000.0   \n",
              "20376    1.0          1.0  CORPORATION            IH        1.0   8500.0   \n",
              "\n",
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20374         1   0   1.0         0  1.0   0      0.0         1   0  0.0   \n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20374     1  0.0  0.0   0   1  0.0  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
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        },
        "id": "5ql0aoHc64BC",
        "outputId": "dc4207cb-269e-4ddc-a9b8-2923861e5f95"
      },
      "outputs": [
        {
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f49a73eb-da32-4bf4-89f6-5388fb9c0075');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e97d2f47-9098-431e-95c5-2a7291ec39e6')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e97d2f47-9098-431e-95c5-2a7291ec39e6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type           locality     activation_date   latitude  longitude  \\\n",
              "20375  BHK2        Indiranagar 2018-06-28 21:58:00  12.965502  77.647694   \n",
              "20376  BHK1  Malleshwaram West 2018-08-07 22:31:00  12.998645  77.574336   \n",
              "\n",
              "      lease_type  gym  lift  swimming_pool  negotiable      furnishing  \\\n",
              "20375     FAMILY    0     1              0           0  SEMI_FURNISHED   \n",
              "20376     ANYONE    0     0              0           1  SEMI_FURNISHED   \n",
              "\n",
              "           parking  property_size  property_age  bathroom facing  cup_board  \\\n",
              "20375         BOTH           1000           5.0       2.0      E        2.0   \n",
              "20376  TWO_WHEELER            500           3.0       1.0      E        1.0   \n",
              "\n",
              "       floor  total_floor water_supply building_type  balconies     rent  \\\n",
              "20375    2.0          4.0    CORP_BORE            AP        3.0  30000.0   \n",
              "20376    1.0          1.0  CORPORATION            IH        1.0   8500.0   \n",
              "\n",
              "       internet  ac  club  intercom  cpa  fs  servant  security  sc   gp  \\\n",
              "20375         1   0   0.0         0  1.0   1      0.0         1   1  0.0   \n",
              "20376         0   0   0.0         0  0.0   0      0.0         0   1  0.0   \n",
              "\n",
              "       park  rwh  stp  hk  pb   vp  \n",
              "20375     1  0.0  0.0   1   1  1.0  \n",
              "20376     1  0.0  0.0   0   0  0.0  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# data type convertion\n",
        "df1['activation_date'] = pd.to_datetime(df1['activation_date'])\n",
        "df1.tail(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "HscG0rw18iEX",
        "outputId": "6724ddf6-c6f9-424c-8d39-0b58e2496d68"
      },
      "outputs": [
        {
          "data": {
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              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>5.0</td>\n",
              "      <td>BOREWELL</td>\n",
              "      <td>AP</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
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              "      <th>20375</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>6</td>\n",
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              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-734e167c-f025-4f10-addc-f13c5da4c918 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type            locality   latitude  longitude lease_type  gym  lift  \\\n",
              "20374  BHK2  Kumaraswamy Layout  12.901800  77.566597     FAMILY    1     1   \n",
              "20375  BHK2         Indiranagar  12.965502  77.647694     FAMILY    0     1   \n",
              "20376  BHK1   Malleshwaram West  12.998645  77.574336     ANYONE    0     0   \n",
              "\n",
              "       swimming_pool  negotiable      furnishing      parking  property_size  \\\n",
              "20374              0           1  SEMI_FURNISHED         BOTH           1000   \n",
              "20375              0           0  SEMI_FURNISHED         BOTH           1000   \n",
              "20376              0           1  SEMI_FURNISHED  TWO_WHEELER            500   \n",
              "\n",
              "       property_age  bathroom facing  cup_board  floor  total_floor  \\\n",
              "20374           0.0       2.0      W        2.0    5.0          5.0   \n",
              "20375           5.0       2.0      E        2.0    2.0          4.0   \n",
              "20376           3.0       1.0      E        1.0    1.0          1.0   \n",
              "\n",
              "      water_supply building_type  balconies     rent  internet  ac  club  \\\n",
              "20374     BOREWELL            AP        2.0  16000.0         1   0   1.0   \n",
              "20375    CORP_BORE            AP        3.0  30000.0         1   0   0.0   \n",
              "20376  CORPORATION            IH        1.0   8500.0         0   0   0.0   \n",
              "\n",
              "       intercom  cpa  fs  servant  security  sc   gp  park  rwh  stp  hk  pb  \\\n",
              "20374         0  1.0   0      0.0         1   0  0.0     1  0.0  0.0   0   1   \n",
              "20375         0  1.0   1      0.0         1   1  0.0     1  0.0  0.0   1   1   \n",
              "20376         0  0.0   0      0.0         0   1  0.0     1  0.0  0.0   0   0   \n",
              "\n",
              "        vp  activation_day  activation_month  activation_year  \n",
              "20374  0.0              21                12             2017  \n",
              "20375  1.0              28                 6             2018  \n",
              "20376  0.0               7                 8             2018  "
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# date column split into day, month, year columns\n",
        "\n",
        "df1['activation_day'] = df1['activation_date'].dt.day\n",
        "df1['activation_month'] = df1['activation_date'].dt.month\n",
        "df1['activation_year'] = df1['activation_date'].dt.year\n",
        "df1.drop(columns='activation_date', inplace=True)\n",
        "df1.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "ItjKpguSR79i",
        "outputId": "ce0cce9c-fd8c-4e20-81ac-b583f7da6231"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>type</th>\n",
              "      <th>locality</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
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              "      <th>property_age</th>\n",
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              "      <th>facing</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>building_type</th>\n",
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              "      <th>20374</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
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              "      <td>77.566597</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>BOREWELL</td>\n",
              "      <td>AP</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20375</th>\n",
              "      <td>BHK2</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
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              "      <td>1000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>AP</td>\n",
              "      <td>3.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>2018</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20376</th>\n",
              "      <td>BHK1</td>\n",
              "      <td>Malleshwaram West</td>\n",
              "      <td>12.998645</td>\n",
              "      <td>77.574336</td>\n",
              "      <td>ANYONE</td>\n",
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              "      <td>8500.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7aeb1b4d-adba-4e87-97e2-c8df4dc7d707')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-7aeb1b4d-adba-4e87-97e2-c8df4dc7d707 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
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              "          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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
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              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-386562ad-b9fb-4798-8b5f-355b6d71352c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type            locality   latitude  longitude lease_type  gym  lift  \\\n",
              "20374  BHK2  Kumaraswamy Layout  12.901800  77.566597     FAMILY    1     1   \n",
              "20375  BHK2         Indiranagar  12.965502  77.647694     FAMILY    0     1   \n",
              "20376  BHK1   Malleshwaram West  12.998645  77.574336     ANYONE    0     0   \n",
              "\n",
              "       swimming_pool  negotiable      furnishing      parking  property_size  \\\n",
              "20374              0           1  SEMI_FURNISHED         BOTH           1000   \n",
              "20375              0           0  SEMI_FURNISHED         BOTH           1000   \n",
              "20376              0           1  SEMI_FURNISHED  TWO_WHEELER            500   \n",
              "\n",
              "       property_age  bathroom facing  cup_board  floor  total_floor  \\\n",
              "20374           0.0       2.0      W        2.0    5.0          5.0   \n",
              "20375           5.0       2.0      E        2.0    2.0          4.0   \n",
              "20376           3.0       1.0      E        1.0    1.0          1.0   \n",
              "\n",
              "      water_supply building_type  balconies     rent  internet  ac  club  \\\n",
              "20374     BOREWELL            AP        2.0  16000.0         1   0   1.0   \n",
              "20375    CORP_BORE            AP        3.0  30000.0         1   0   0.0   \n",
              "20376  CORPORATION            IH        1.0   8500.0         0   0   0.0   \n",
              "\n",
              "       intercom  cpa  fs  servant  security  sc   gp  park  rwh  stp  hk  pb  \\\n",
              "20374         0  1.0   0      0.0         1   0  0.0     1  0.0  0.0   0   1   \n",
              "20375         0  1.0   1      0.0         1   1  0.0     1  0.0  0.0   1   1   \n",
              "20376         0  0.0   0      0.0         0   1  0.0     1  0.0  0.0   0   0   \n",
              "\n",
              "        vp  activation_day  activation_month  activation_year  no_of_amenities  \n",
              "20374  0.0              21                12             2017              8.0  \n",
              "20375  1.0              28                 6             2018             10.0  \n",
              "20376  0.0               7                 8             2018              2.0  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# calculate the count of amenities\n",
        "\n",
        "df1['no_of_amenities'] = df1['gym'] + df1['lift'] + df1['swimming_pool'] + df1['internet'] + df1['ac'] + df1['club'] + df1['intercom'] + df1['cpa'] + df1['fs'] + df1['servant'] + df1['security'] + df1['sc'] + df1['gp'] + df1['park'] + df1['rwh'] + df1['stp'] + df1['hk'] + df1['pb'] + df1['vp']\n",
        "df1.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "4FkjY5s1-qJZ",
        "outputId": "f75fd689-b773-411b-fb7e-640dae7fce72"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>property_size</th>\n",
              "      <th>property_age</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>balconies</th>\n",
              "      <th>rent</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "      <th>activation_day</th>\n",
              "      <th>activation_month</th>\n",
              "      <th>activation_year</th>\n",
              "      <th>no_of_amenities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20377.000000</td>\n",
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              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
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              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "      <td>20377.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.945750</td>\n",
              "      <td>77.637238</td>\n",
              "      <td>0.226235</td>\n",
              "      <td>0.361142</td>\n",
              "      <td>0.174461</td>\n",
              "      <td>0.711488</td>\n",
              "      <td>1061.437209</td>\n",
              "      <td>5.777053</td>\n",
              "      <td>1.857732</td>\n",
              "      <td>4.567503</td>\n",
              "      <td>1.869510</td>\n",
              "      <td>3.798106</td>\n",
              "      <td>1.147863</td>\n",
              "      <td>19048.502969</td>\n",
              "      <td>0.661825</td>\n",
              "      <td>0.042548</td>\n",
              "      <td>0.185601</td>\n",
              "      <td>0.192865</td>\n",
              "      <td>0.291701</td>\n",
              "      <td>0.234676</td>\n",
              "      <td>0.037150</td>\n",
              "      <td>0.472984</td>\n",
              "      <td>0.471512</td>\n",
              "      <td>0.099917</td>\n",
              "      <td>0.506012</td>\n",
              "      <td>0.246503</td>\n",
              "      <td>0.170535</td>\n",
              "      <td>0.198557</td>\n",
              "      <td>0.364921</td>\n",
              "      <td>0.238553</td>\n",
              "      <td>15.344555</td>\n",
              "      <td>6.325072</td>\n",
              "      <td>2017.637336</td>\n",
              "      <td>5.177700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.029587</td>\n",
              "      <td>0.110758</td>\n",
              "      <td>0.418404</td>\n",
              "      <td>0.480344</td>\n",
              "      <td>0.379515</td>\n",
              "      <td>0.453081</td>\n",
              "      <td>591.857798</td>\n",
              "      <td>6.362450</td>\n",
              "      <td>0.710933</td>\n",
              "      <td>336.427053</td>\n",
              "      <td>2.181633</td>\n",
              "      <td>3.183849</td>\n",
              "      <td>0.993290</td>\n",
              "      <td>8433.444157</td>\n",
              "      <td>0.473100</td>\n",
              "      <td>0.201841</td>\n",
              "      <td>0.388794</td>\n",
              "      <td>0.394557</td>\n",
              "      <td>0.454557</td>\n",
              "      <td>0.423807</td>\n",
              "      <td>0.189133</td>\n",
              "      <td>0.499282</td>\n",
              "      <td>0.499200</td>\n",
              "      <td>0.299896</td>\n",
              "      <td>0.499976</td>\n",
              "      <td>0.430986</td>\n",
              "      <td>0.376112</td>\n",
              "      <td>0.398924</td>\n",
              "      <td>0.481420</td>\n",
              "      <td>0.426209</td>\n",
              "      <td>8.864386</td>\n",
              "      <td>3.251640</td>\n",
              "      <td>0.480781</td>\n",
              "      <td>4.570412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.900004</td>\n",
              "      <td>77.500072</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <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>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>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>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.918232</td>\n",
              "      <td>77.571912</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12500.000000</td>\n",
              "      <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>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>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>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.943792</td>\n",
              "      <td>77.633367</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1044.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>17500.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>15.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.970922</td>\n",
              "      <td>77.695692</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>24000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12.999999</td>\n",
              "      <td>80.266346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>48026.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>19.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            "text/plain": [
              "           latitude     longitude           gym          lift  swimming_pool  \\\n",
              "count  20377.000000  20377.000000  20377.000000  20377.000000   20377.000000   \n",
              "mean      12.945750     77.637238      0.226235      0.361142       0.174461   \n",
              "std        0.029587      0.110758      0.418404      0.480344       0.379515   \n",
              "min       12.900004     77.500072      0.000000      0.000000       0.000000   \n",
              "25%       12.918232     77.571912      0.000000      0.000000       0.000000   \n",
              "50%       12.943792     77.633367      0.000000      0.000000       0.000000   \n",
              "75%       12.970922     77.695692      0.000000      1.000000       0.000000   \n",
              "max       12.999999     80.266346      1.000000      1.000000       1.000000   \n",
              "\n",
              "         negotiable  property_size  property_age      bathroom     cup_board  \\\n",
              "count  20377.000000   20377.000000  20377.000000  20377.000000  20377.000000   \n",
              "mean       0.711488    1061.437209      5.777053      1.857732      4.567503   \n",
              "std        0.453081     591.857798      6.362450      0.710933    336.427053   \n",
              "min        0.000000       1.000000     -1.000000      1.000000      0.000000   \n",
              "25%        0.000000     700.000000      1.000000      1.000000      1.000000   \n",
              "50%        1.000000    1044.000000      5.000000      2.000000      2.000000   \n",
              "75%        1.000000    1280.000000     10.000000      2.000000      3.000000   \n",
              "max        1.000000   50000.000000    400.000000     21.000000  48026.000000   \n",
              "\n",
              "              floor   total_floor     balconies          rent      internet  \\\n",
              "count  20377.000000  20377.000000  20377.000000  20377.000000  20377.000000   \n",
              "mean       1.869510      3.798106      1.147863  19048.502969      0.661825   \n",
              "std        2.181633      3.183849      0.993290   8433.444157      0.473100   \n",
              "min        0.000000      0.000000      0.000000   8000.000000      0.000000   \n",
              "25%        1.000000      2.000000      0.000000  12500.000000      0.000000   \n",
              "50%        1.000000      3.000000      1.000000  17500.000000      1.000000   \n",
              "75%        2.000000      4.000000      2.000000  24000.000000      1.000000   \n",
              "max       25.000000     26.000000     13.000000  50000.000000      1.000000   \n",
              "\n",
              "                 ac          club      intercom           cpa            fs  \\\n",
              "count  20377.000000  20377.000000  20377.000000  20377.000000  20377.000000   \n",
              "mean       0.042548      0.185601      0.192865      0.291701      0.234676   \n",
              "std        0.201841      0.388794      0.394557      0.454557      0.423807   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "            servant      security            sc            gp          park  \\\n",
              "count  20377.000000  20377.000000  20377.000000  20377.000000  20377.000000   \n",
              "mean       0.037150      0.472984      0.471512      0.099917      0.506012   \n",
              "std        0.189133      0.499282      0.499200      0.299896      0.499976   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
              "75%        0.000000      1.000000      1.000000      0.000000      1.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "                rwh           stp            hk            pb            vp  \\\n",
              "count  20377.000000  20377.000000  20377.000000  20377.000000  20377.000000   \n",
              "mean       0.246503      0.170535      0.198557      0.364921      0.238553   \n",
              "std        0.430986      0.376112      0.398924      0.481420      0.426209   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "       activation_day  activation_month  activation_year  no_of_amenities  \n",
              "count    20377.000000      20377.000000     20377.000000     20377.000000  \n",
              "mean        15.344555          6.325072      2017.637336         5.177700  \n",
              "std          8.864386          3.251640         0.480781         4.570412  \n",
              "min          1.000000          1.000000      2017.000000         0.000000  \n",
              "25%          7.000000          4.000000      2017.000000         2.000000  \n",
              "50%         15.000000          6.000000      2018.000000         3.000000  \n",
              "75%         23.000000          9.000000      2018.000000         8.000000  \n",
              "max         31.000000         12.000000      2018.000000        19.000000  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "WbfK1JqUEDqP",
        "outputId": "20f08088-6ada-4847-d677-c535a08818a7"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       type            locality   latitude  longitude lease_type  gym  lift  \\\n",
              "20341  BHK2  Kumaraswamy Layout  12.901800  77.566597     FAMILY    1     1   \n",
              "20342  BHK2         Indiranagar  12.965502  77.647694     FAMILY    0     1   \n",
              "20343  BHK1   Malleshwaram West  12.998645  77.574336     ANYONE    0     0   \n",
              "\n",
              "       swimming_pool  negotiable      furnishing      parking  property_size  \\\n",
              "20341              0           1  SEMI_FURNISHED         BOTH           1000   \n",
              "20342              0           0  SEMI_FURNISHED         BOTH           1000   \n",
              "20343              0           1  SEMI_FURNISHED  TWO_WHEELER            500   \n",
              "\n",
              "       property_age  bathroom facing  cup_board  floor  total_floor  \\\n",
              "20341           0.0       2.0      W        2.0    5.0          5.0   \n",
              "20342           5.0       2.0      E        2.0    2.0          4.0   \n",
              "20343           3.0       1.0      E        1.0    1.0          1.0   \n",
              "\n",
              "      water_supply building_type  balconies     rent  internet  ac  club  \\\n",
              "20341     BOREWELL            AP        2.0  16000.0         1   0   1.0   \n",
              "20342    CORP_BORE            AP        3.0  30000.0         1   0   0.0   \n",
              "20343  CORPORATION            IH        1.0   8500.0         0   0   0.0   \n",
              "\n",
              "       intercom  cpa  fs  servant  security  sc   gp  park  rwh  stp  hk  pb  \\\n",
              "20341         0  1.0   0      0.0         1   0  0.0     1  0.0  0.0   0   1   \n",
              "20342         0  1.0   1      0.0         1   1  0.0     1  0.0  0.0   1   1   \n",
              "20343         0  0.0   0      0.0         0   1  0.0     1  0.0  0.0   0   0   \n",
              "\n",
              "        vp  activation_day  activation_month  activation_year  no_of_amenities  \n",
              "20341  0.0              21                12             2017              8.0  \n",
              "20342  1.0              28                 6             2018             10.0  \n",
              "20343  0.0               7                 8             2018              2.0  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# remove the negative value in property_age column\n",
        "\n",
        "df1 = df1[df1['property_age']>=0].reset_index(drop=True)\n",
        "df1.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "1bCPoynpIKpH",
        "outputId": "bfb58258-01c6-4ecf-e1db-7e93663ce608"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>property_size</th>\n",
              "      <th>property_age</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>balconies</th>\n",
              "      <th>rent</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "      <th>activation_day</th>\n",
              "      <th>activation_month</th>\n",
              "      <th>activation_year</th>\n",
              "      <th>no_of_amenities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.945743</td>\n",
              "      <td>77.637263</td>\n",
              "      <td>0.226406</td>\n",
              "      <td>0.361040</td>\n",
              "      <td>0.174548</td>\n",
              "      <td>0.711266</td>\n",
              "      <td>1061.762338</td>\n",
              "      <td>5.788046</td>\n",
              "      <td>1.858140</td>\n",
              "      <td>4.572061</td>\n",
              "      <td>1.870035</td>\n",
              "      <td>3.797582</td>\n",
              "      <td>1.147857</td>\n",
              "      <td>19048.188409</td>\n",
              "      <td>0.661817</td>\n",
              "      <td>0.042568</td>\n",
              "      <td>0.185706</td>\n",
              "      <td>0.192932</td>\n",
              "      <td>0.291831</td>\n",
              "      <td>0.234664</td>\n",
              "      <td>0.037161</td>\n",
              "      <td>0.472965</td>\n",
              "      <td>0.471687</td>\n",
              "      <td>0.099784</td>\n",
              "      <td>0.505948</td>\n",
              "      <td>0.246412</td>\n",
              "      <td>0.170615</td>\n",
              "      <td>0.198781</td>\n",
              "      <td>0.365022</td>\n",
              "      <td>0.238547</td>\n",
              "      <td>15.342017</td>\n",
              "      <td>6.325796</td>\n",
              "      <td>2017.636748</td>\n",
              "      <td>5.178431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.029582</td>\n",
              "      <td>0.110805</td>\n",
              "      <td>0.418515</td>\n",
              "      <td>0.480314</td>\n",
              "      <td>0.379589</td>\n",
              "      <td>0.453185</td>\n",
              "      <td>592.100777</td>\n",
              "      <td>6.361746</td>\n",
              "      <td>0.711027</td>\n",
              "      <td>336.699793</td>\n",
              "      <td>2.182685</td>\n",
              "      <td>3.184757</td>\n",
              "      <td>0.993298</td>\n",
              "      <td>8431.271930</td>\n",
              "      <td>0.473103</td>\n",
              "      <td>0.201886</td>\n",
              "      <td>0.388879</td>\n",
              "      <td>0.394609</td>\n",
              "      <td>0.454616</td>\n",
              "      <td>0.423799</td>\n",
              "      <td>0.189160</td>\n",
              "      <td>0.499281</td>\n",
              "      <td>0.499210</td>\n",
              "      <td>0.299719</td>\n",
              "      <td>0.499977</td>\n",
              "      <td>0.430932</td>\n",
              "      <td>0.376182</td>\n",
              "      <td>0.399093</td>\n",
              "      <td>0.481448</td>\n",
              "      <td>0.426206</td>\n",
              "      <td>8.862771</td>\n",
              "      <td>3.253369</td>\n",
              "      <td>0.480948</td>\n",
              "      <td>4.571908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.900004</td>\n",
              "      <td>77.500072</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <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>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>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>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.918239</td>\n",
              "      <td>77.571956</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12500.000000</td>\n",
              "      <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>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>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>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.943787</td>\n",
              "      <td>77.633368</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1045.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>17500.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>15.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.970899</td>\n",
              "      <td>77.695702</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>24000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>12.999999</td>\n",
              "      <td>80.266346</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>48026.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>19.000000</td>\n",
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              "  </tbody>\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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            ],
            "text/plain": [
              "           latitude     longitude           gym          lift  swimming_pool  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000   20344.000000   \n",
              "mean      12.945743     77.637263      0.226406      0.361040       0.174548   \n",
              "std        0.029582      0.110805      0.418515      0.480314       0.379589   \n",
              "min       12.900004     77.500072      0.000000      0.000000       0.000000   \n",
              "25%       12.918239     77.571956      0.000000      0.000000       0.000000   \n",
              "50%       12.943787     77.633368      0.000000      0.000000       0.000000   \n",
              "75%       12.970899     77.695702      0.000000      1.000000       0.000000   \n",
              "max       12.999999     80.266346      1.000000      1.000000       1.000000   \n",
              "\n",
              "         negotiable  property_size  property_age      bathroom     cup_board  \\\n",
              "count  20344.000000   20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.711266    1061.762338      5.788046      1.858140      4.572061   \n",
              "std        0.453185     592.100777      6.361746      0.711027    336.699793   \n",
              "min        0.000000       1.000000      0.000000      1.000000      0.000000   \n",
              "25%        0.000000     700.000000      1.000000      1.000000      1.000000   \n",
              "50%        1.000000    1045.000000      5.000000      2.000000      2.000000   \n",
              "75%        1.000000    1280.000000     10.000000      2.000000      3.000000   \n",
              "max        1.000000   50000.000000    400.000000     21.000000  48026.000000   \n",
              "\n",
              "              floor   total_floor     balconies          rent      internet  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       1.870035      3.797582      1.147857  19048.188409      0.661817   \n",
              "std        2.182685      3.184757      0.993298   8431.271930      0.473103   \n",
              "min        0.000000      0.000000      0.000000   8000.000000      0.000000   \n",
              "25%        1.000000      2.000000      0.000000  12500.000000      0.000000   \n",
              "50%        1.000000      3.000000      1.000000  17500.000000      1.000000   \n",
              "75%        2.000000      4.000000      2.000000  24000.000000      1.000000   \n",
              "max       25.000000     26.000000     13.000000  50000.000000      1.000000   \n",
              "\n",
              "                 ac          club      intercom           cpa            fs  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.042568      0.185706      0.192932      0.291831      0.234664   \n",
              "std        0.201886      0.388879      0.394609      0.454616      0.423799   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "            servant      security            sc            gp          park  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.037161      0.472965      0.471687      0.099784      0.505948   \n",
              "std        0.189160      0.499281      0.499210      0.299719      0.499977   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
              "75%        0.000000      1.000000      1.000000      0.000000      1.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "                rwh           stp            hk            pb            vp  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.246412      0.170615      0.198781      0.365022      0.238547   \n",
              "std        0.430932      0.376182      0.399093      0.481448      0.426206   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "       activation_day  activation_month  activation_year  no_of_amenities  \n",
              "count    20344.000000      20344.000000     20344.000000     20344.000000  \n",
              "mean        15.342017          6.325796      2017.636748         5.178431  \n",
              "std          8.862771          3.253369         0.480948         4.571908  \n",
              "min          1.000000          1.000000      2017.000000         0.000000  \n",
              "25%          7.000000          4.000000      2017.000000         2.000000  \n",
              "50%         15.000000          6.000000      2018.000000         3.000000  \n",
              "75%         23.000000          9.000000      2018.000000         8.000000  \n",
              "max         31.000000         12.000000      2018.000000        19.000000  "
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "kWCENwI--seL",
        "outputId": "6bf4b521-2c5b-4d02-b2a1-ed06e6b8dfa6"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>activation_day</th>\n",
              "      <th>activation_month</th>\n",
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              "      <th>building_type</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>balconies</th>\n",
              "      <th>parking</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "      <th>no_of_amenities</th>\n",
              "      <th>rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20339</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "      <td>Ulsoor</td>\n",
              "      <td>12.979762</td>\n",
              "      <td>77.615395</td>\n",
              "      <td>BHK1</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20340</th>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>IH</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20341</th>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>W</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>BOREWELL</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20342</th>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>2018</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20343</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>Malleshwaram West</td>\n",
              "      <td>12.998645</td>\n",
              "      <td>77.574336</td>\n",
              "      <td>BHK1</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>500</td>\n",
              "      <td>3.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>IH</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fd0b20d-121e-4ee3-b19e-1f139643c7b2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9fd0b20d-121e-4ee3-b19e-1f139643c7b2 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-9fd0b20d-121e-4ee3-b19e-1f139643c7b2');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b5f4b14e-7331-48d0-bcea-e6d8d6503275\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b5f4b14e-7331-48d0-bcea-e6d8d6503275')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b5f4b14e-7331-48d0-bcea-e6d8d6503275 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       activation_day  activation_month  activation_year  \\\n",
              "20339              10                11             2017   \n",
              "20340              19                10             2017   \n",
              "20341              21                12             2017   \n",
              "20342              28                 6             2018   \n",
              "20343               7                 8             2018   \n",
              "\n",
              "                    locality   latitude  longitude  type lease_type  \\\n",
              "20339                 Ulsoor  12.979762  77.615395  BHK1     ANYONE   \n",
              "20340  Koramangala 4th Block  12.931072  77.632361  BHK2     ANYONE   \n",
              "20341     Kumaraswamy Layout  12.901800  77.566597  BHK2     FAMILY   \n",
              "20342            Indiranagar  12.965502  77.647694  BHK2     FAMILY   \n",
              "20343      Malleshwaram West  12.998645  77.574336  BHK1     ANYONE   \n",
              "\n",
              "       property_size  property_age      furnishing facing  floor  total_floor  \\\n",
              "20339            850          30.0  SEMI_FURNISHED      E    1.0          4.0   \n",
              "20340           1000          10.0  SEMI_FURNISHED      N    1.0          2.0   \n",
              "20341           1000           0.0  SEMI_FURNISHED      W    5.0          5.0   \n",
              "20342           1000           5.0  SEMI_FURNISHED      E    2.0          4.0   \n",
              "20343            500           3.0  SEMI_FURNISHED      E    1.0          1.0   \n",
              "\n",
              "      building_type water_supply  negotiable  cup_board  balconies  \\\n",
              "20339            AP    CORP_BORE           1        1.0        1.0   \n",
              "20340            IH  CORPORATION           1        2.0        2.0   \n",
              "20341            AP     BOREWELL           1        2.0        2.0   \n",
              "20342            AP    CORP_BORE           0        2.0        3.0   \n",
              "20343            IH  CORPORATION           1        1.0        1.0   \n",
              "\n",
              "           parking  bathroom  gym  lift  swimming_pool  internet  ac  club  \\\n",
              "20339  TWO_WHEELER       2.0    0     1              0         1   0   0.0   \n",
              "20340         BOTH       2.0    0     0              0         1   0   0.0   \n",
              "20341         BOTH       2.0    1     1              0         1   0   1.0   \n",
              "20342         BOTH       2.0    0     1              0         1   0   0.0   \n",
              "20343  TWO_WHEELER       1.0    0     0              0         0   0   0.0   \n",
              "\n",
              "       intercom  cpa  fs  servant  security  sc   gp  park  rwh  stp  hk  pb  \\\n",
              "20339         0  0.0   1      0.0         1   1  1.0     1  0.0  0.0   1   0   \n",
              "20340         0  0.0   0      0.0         0   1  0.0     1  1.0  0.0   0   0   \n",
              "20341         0  1.0   0      0.0         1   0  0.0     1  0.0  0.0   0   1   \n",
              "20342         0  1.0   1      0.0         1   1  0.0     1  0.0  0.0   1   1   \n",
              "20343         0  0.0   0      0.0         0   1  0.0     1  0.0  0.0   0   0   \n",
              "\n",
              "        vp  no_of_amenities     rent  \n",
              "20339  1.0              9.0  20000.0  \n",
              "20340  0.0              4.0  30000.0  \n",
              "20341  0.0              8.0  16000.0  \n",
              "20342  1.0             10.0  30000.0  \n",
              "20343  0.0              2.0   8500.0  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# rearrange the columns order\n",
        "\n",
        "df1 = df1[['activation_day', 'activation_month', 'activation_year', 'locality', 'latitude', 'longitude', 'type', 'lease_type',\n",
        "           'property_size', 'property_age', 'furnishing', 'facing', 'floor', 'total_floor', 'building_type', 'water_supply',\n",
        "           'negotiable', 'cup_board', 'balconies', 'parking', 'bathroom', 'gym', 'lift', 'swimming_pool', 'internet', 'ac',\n",
        "           'club', 'intercom', 'cpa', 'fs', 'servant', 'security', 'sc', 'gp', 'park', 'rwh', 'stp', 'hk', 'pb', 'vp', 'no_of_amenities', 'rent']]\n",
        "\n",
        "df1.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8sXoSJHcEA0",
        "outputId": "6b644f7f-e63e-4069-bc69-404aca14b5c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['BHK2', 'BHK3', 'BHK1', 'RK1', 'BHK4', 'BHK4PLUS', '1BHK1', 'bhk2',\n",
              "       'bhk3'], dtype=object)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1['type'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "6MrRNREQKqR3",
        "outputId": "5af763b0-9657-4651-cbd8-9fe5443fe7a1"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activation_day</th>\n",
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              "      <th>cup_board</th>\n",
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              "      <th>cpa</th>\n",
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              "      <th>sc</th>\n",
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              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
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              "      <th>vp</th>\n",
              "      <th>no_of_amenities</th>\n",
              "      <th>rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20339</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "      <td>Ulsoor</td>\n",
              "      <td>12.979762</td>\n",
              "      <td>77.615395</td>\n",
              "      <td>BHK1</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>20000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20340</th>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>ANYONE</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>IH</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>1</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>2.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20341</th>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>W</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>BOREWELL</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20342</th>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>2018</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>BHK2</td>\n",
              "      <td>FAMILY</td>\n",
              "      <td>1000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>CORP_BORE</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>BOTH</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20343</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>Malleshwaram West</td>\n",
              "      <td>12.998645</td>\n",
              "      <td>77.574336</td>\n",
              "      <td>BHK1</td>\n",
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              "      <td>500</td>\n",
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              "      <td>SEMI_FURNISHED</td>\n",
              "      <td>E</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>IH</td>\n",
              "      <td>CORPORATION</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>TWO_WHEELER</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       activation_day  activation_month  activation_year  \\\n",
              "20339              10                11             2017   \n",
              "20340              19                10             2017   \n",
              "20341              21                12             2017   \n",
              "20342              28                 6             2018   \n",
              "20343               7                 8             2018   \n",
              "\n",
              "                    locality   latitude  longitude  type lease_type  \\\n",
              "20339                 Ulsoor  12.979762  77.615395  BHK1     ANYONE   \n",
              "20340  Koramangala 4th Block  12.931072  77.632361  BHK2     ANYONE   \n",
              "20341     Kumaraswamy Layout  12.901800  77.566597  BHK2     FAMILY   \n",
              "20342            Indiranagar  12.965502  77.647694  BHK2     FAMILY   \n",
              "20343      Malleshwaram West  12.998645  77.574336  BHK1     ANYONE   \n",
              "\n",
              "       property_size  property_age      furnishing facing  floor  total_floor  \\\n",
              "20339            850          30.0  SEMI_FURNISHED      E    1.0          4.0   \n",
              "20340           1000          10.0  SEMI_FURNISHED      N    1.0          2.0   \n",
              "20341           1000           0.0  SEMI_FURNISHED      W    5.0          5.0   \n",
              "20342           1000           5.0  SEMI_FURNISHED      E    2.0          4.0   \n",
              "20343            500           3.0  SEMI_FURNISHED      E    1.0          1.0   \n",
              "\n",
              "      building_type water_supply  negotiable  cup_board  balconies  \\\n",
              "20339            AP    CORP_BORE           1        1.0        1.0   \n",
              "20340            IH  CORPORATION           1        2.0        2.0   \n",
              "20341            AP     BOREWELL           1        2.0        2.0   \n",
              "20342            AP    CORP_BORE           0        2.0        3.0   \n",
              "20343            IH  CORPORATION           1        1.0        1.0   \n",
              "\n",
              "           parking  bathroom  gym  lift  swimming_pool  internet  ac  club  \\\n",
              "20339  TWO_WHEELER       2.0    0     1              0         1   0   0.0   \n",
              "20340         BOTH       2.0    0     0              0         1   0   0.0   \n",
              "20341         BOTH       2.0    1     1              0         1   0   1.0   \n",
              "20342         BOTH       2.0    0     1              0         1   0   0.0   \n",
              "20343  TWO_WHEELER       1.0    0     0              0         0   0   0.0   \n",
              "\n",
              "       intercom  cpa  fs  servant  security  sc   gp  park  rwh  stp  hk  pb  \\\n",
              "20339         0  0.0   1      0.0         1   1  1.0     1  0.0  0.0   1   0   \n",
              "20340         0  0.0   0      0.0         0   1  0.0     1  1.0  0.0   0   0   \n",
              "20341         0  1.0   0      0.0         1   0  0.0     1  0.0  0.0   0   1   \n",
              "20342         0  1.0   1      0.0         1   1  0.0     1  0.0  0.0   1   1   \n",
              "20343         0  0.0   0      0.0         0   1  0.0     1  0.0  0.0   0   0   \n",
              "\n",
              "        vp  no_of_amenities     rent  \n",
              "20339  1.0              9.0  20000.0  \n",
              "20340  0.0              4.0  30000.0  \n",
              "20341  0.0              8.0  16000.0  \n",
              "20342  1.0             10.0  30000.0  \n",
              "20343  0.0              2.0   8500.0  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Handle the wrong value names (duplicate)\n",
        "\n",
        "type_dict = {'1BHK1':'BHK1', 'bhk2':'BHK2', 'bhk3':'BHK3'}\n",
        "\n",
        "df1['type'] = df1['type'].apply(lambda x: type_dict[x] if x in ['1BHK1', 'bhk2', 'bhk3'] else x)\n",
        "df1.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MNQMFp8LprL",
        "outputId": "6c789e88-ca18-4cb6-c588-d2f6d25e0c80"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['BHK2', 'BHK3', 'BHK1', 'RK1', 'BHK4', 'BHK4PLUS'], dtype=object)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1['type'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IoxOkLtxcSv2",
        "outputId": "c59727ba-1661-492e-b801-39171bcc760e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['NE', 'E', 'S', 'N', 'SE', 'W', 'NW', 'SW'], dtype=object)"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1['facing'].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "c7i0Vg2ggEgA"
      },
      "outputs": [],
      "source": [
        "# save to csv file at before encoding for EDA\n",
        "\n",
        "# df1.to_csv('df_before_encode.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "tgHAwRl1D2kr"
      },
      "outputs": [],
      "source": [
        "# encoding the categorical features into numerical features\n",
        "\n",
        "df1['type'] = df1['type'].map({'RK1':0, 'BHK1':1, 'BHK2':2, 'BHK3':3, 'BHK4':4, 'BHK4PLUS':5})\n",
        "df1['facing'] = df1['facing'].map({'N':1, 'E':2, 'W':3, 'S':4, 'NE':5, 'NW':6, 'SE':7, 'SW':8})\n",
        "df1['lease_type'] = df1['lease_type'].map({'BACHELOR':1, 'FAMILY':2, 'COMPANY':3, 'ANYONE':4})\n",
        "df1['furnishing'] = df1['furnishing'].map({'NOT_FURNISHED':0, 'SEMI_FURNISHED':1, 'FULLY_FURNISHED':2})\n",
        "df1['parking'] = df1['parking'].map({'NONE':0, 'TWO_WHEELER':1, 'FOUR_WHEELER':2, 'BOTH':3})\n",
        "df1['water_supply'] = df1['water_supply'].map({'CORPORATION':1, 'CORP_BORE':2, 'BOREWELL':3,})\n",
        "df1['building_type'] = df1['building_type'].map({'AP':1, 'IH':2, 'IF':3, 'GC':4})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "CJZfwiphO4RE",
        "outputId": "4082cc9d-11a7-4fdb-dd7a-2ce73b30cd14"
      },
      "outputs": [
        {
          "data": {
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              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b151233a-ab4b-4781-8444-f761a2d9bcc1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       activation_day  activation_month  activation_year            locality  \\\n",
              "20341              21                12             2017  Kumaraswamy Layout   \n",
              "20342              28                 6             2018         Indiranagar   \n",
              "20343               7                 8             2018   Malleshwaram West   \n",
              "\n",
              "        latitude  longitude  type  lease_type  property_size  property_age  \\\n",
              "20341  12.901800  77.566597     2           2           1000           0.0   \n",
              "20342  12.965502  77.647694     2           2           1000           5.0   \n",
              "20343  12.998645  77.574336     1           4            500           3.0   \n",
              "\n",
              "       furnishing  facing  floor  total_floor  building_type  water_supply  \\\n",
              "20341           1       3    5.0          5.0              1             3   \n",
              "20342           1       2    2.0          4.0              1             2   \n",
              "20343           1       2    1.0          1.0              2             1   \n",
              "\n",
              "       negotiable  cup_board  balconies  parking  bathroom  gym  lift  \\\n",
              "20341           1        2.0        2.0        3       2.0    1     1   \n",
              "20342           0        2.0        3.0        3       2.0    0     1   \n",
              "20343           1        1.0        1.0        1       1.0    0     0   \n",
              "\n",
              "       swimming_pool  internet  ac  club  intercom  cpa  fs  servant  \\\n",
              "20341              0         1   0   1.0         0  1.0   0      0.0   \n",
              "20342              0         1   0   0.0         0  1.0   1      0.0   \n",
              "20343              0         0   0   0.0         0  0.0   0      0.0   \n",
              "\n",
              "       security  sc   gp  park  rwh  stp  hk  pb   vp  no_of_amenities  \\\n",
              "20341         1   0  0.0     1  0.0  0.0   0   1  0.0              8.0   \n",
              "20342         1   1  0.0     1  0.0  0.0   1   1  1.0             10.0   \n",
              "20343         0   1  0.0     1  0.0  0.0   0   0  0.0              2.0   \n",
              "\n",
              "          rent  \n",
              "20341  16000.0  \n",
              "20342  30000.0  \n",
              "20343   8500.0  "
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.tail(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "QFM5sBbCoDtu",
        "outputId": "1d3e45af-9ca5-4be2-e118-0ad571e0cd06"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-bf14e182-eaf1-4a0f-bf87-9258c77f1424\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activation_day</th>\n",
              "      <th>activation_month</th>\n",
              "      <th>activation_year</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>type</th>\n",
              "      <th>lease_type</th>\n",
              "      <th>property_size</th>\n",
              "      <th>property_age</th>\n",
              "      <th>furnishing</th>\n",
              "      <th>facing</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>building_type</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>balconies</th>\n",
              "      <th>parking</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "      <th>no_of_amenities</th>\n",
              "      <th>rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "      <td>20344.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>15.342017</td>\n",
              "      <td>6.325796</td>\n",
              "      <td>2017.636748</td>\n",
              "      <td>12.945743</td>\n",
              "      <td>77.637263</td>\n",
              "      <td>2.011994</td>\n",
              "      <td>2.918748</td>\n",
              "      <td>1061.762338</td>\n",
              "      <td>5.788046</td>\n",
              "      <td>0.987318</td>\n",
              "      <td>2.171254</td>\n",
              "      <td>1.870035</td>\n",
              "      <td>3.797582</td>\n",
              "      <td>2.021481</td>\n",
              "      <td>1.821716</td>\n",
              "      <td>0.711266</td>\n",
              "      <td>4.572061</td>\n",
              "      <td>1.147857</td>\n",
              "      <td>2.072110</td>\n",
              "      <td>1.858140</td>\n",
              "      <td>0.226406</td>\n",
              "      <td>0.361040</td>\n",
              "      <td>0.174548</td>\n",
              "      <td>0.661817</td>\n",
              "      <td>0.042568</td>\n",
              "      <td>0.185706</td>\n",
              "      <td>0.192932</td>\n",
              "      <td>0.291831</td>\n",
              "      <td>0.234664</td>\n",
              "      <td>0.037161</td>\n",
              "      <td>0.472965</td>\n",
              "      <td>0.471687</td>\n",
              "      <td>0.099784</td>\n",
              "      <td>0.505948</td>\n",
              "      <td>0.246412</td>\n",
              "      <td>0.170615</td>\n",
              "      <td>0.198781</td>\n",
              "      <td>0.365022</td>\n",
              "      <td>0.238547</td>\n",
              "      <td>5.178431</td>\n",
              "      <td>19048.188409</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.862771</td>\n",
              "      <td>3.253369</td>\n",
              "      <td>0.480948</td>\n",
              "      <td>0.029582</td>\n",
              "      <td>0.110805</td>\n",
              "      <td>0.746193</td>\n",
              "      <td>1.037580</td>\n",
              "      <td>592.100777</td>\n",
              "      <td>6.361746</td>\n",
              "      <td>0.356451</td>\n",
              "      <td>1.200480</td>\n",
              "      <td>2.182685</td>\n",
              "      <td>3.184757</td>\n",
              "      <td>0.941112</td>\n",
              "      <td>0.668913</td>\n",
              "      <td>0.453185</td>\n",
              "      <td>336.699793</td>\n",
              "      <td>0.993298</td>\n",
              "      <td>1.037781</td>\n",
              "      <td>0.711027</td>\n",
              "      <td>0.418515</td>\n",
              "      <td>0.480314</td>\n",
              "      <td>0.379589</td>\n",
              "      <td>0.473103</td>\n",
              "      <td>0.201886</td>\n",
              "      <td>0.388879</td>\n",
              "      <td>0.394609</td>\n",
              "      <td>0.454616</td>\n",
              "      <td>0.423799</td>\n",
              "      <td>0.189160</td>\n",
              "      <td>0.499281</td>\n",
              "      <td>0.499210</td>\n",
              "      <td>0.299719</td>\n",
              "      <td>0.499977</td>\n",
              "      <td>0.430932</td>\n",
              "      <td>0.376182</td>\n",
              "      <td>0.399093</td>\n",
              "      <td>0.481448</td>\n",
              "      <td>0.426206</td>\n",
              "      <td>4.571908</td>\n",
              "      <td>8431.271930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>12.900004</td>\n",
              "      <td>77.500072</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>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>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>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>8000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>12.918239</td>\n",
              "      <td>77.571956</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>700.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>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>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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>12500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>12.943787</td>\n",
              "      <td>77.633368</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1045.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <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>3.000000</td>\n",
              "      <td>17500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>12.970899</td>\n",
              "      <td>77.695702</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1280.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>24000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>12.999999</td>\n",
              "      <td>80.266346</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>48026.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>50000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf14e182-eaf1-4a0f-bf87-9258c77f1424')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bf14e182-eaf1-4a0f-bf87-9258c77f1424 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-bf14e182-eaf1-4a0f-bf87-9258c77f1424');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-272507ac-a63d-490f-b5e5-b6d5337f2caf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-272507ac-a63d-490f-b5e5-b6d5337f2caf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-272507ac-a63d-490f-b5e5-b6d5337f2caf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       activation_day  activation_month  activation_year      latitude  \\\n",
              "count    20344.000000      20344.000000     20344.000000  20344.000000   \n",
              "mean        15.342017          6.325796      2017.636748     12.945743   \n",
              "std          8.862771          3.253369         0.480948      0.029582   \n",
              "min          1.000000          1.000000      2017.000000     12.900004   \n",
              "25%          7.000000          4.000000      2017.000000     12.918239   \n",
              "50%         15.000000          6.000000      2018.000000     12.943787   \n",
              "75%         23.000000          9.000000      2018.000000     12.970899   \n",
              "max         31.000000         12.000000      2018.000000     12.999999   \n",
              "\n",
              "          longitude          type    lease_type  property_size  property_age  \\\n",
              "count  20344.000000  20344.000000  20344.000000   20344.000000  20344.000000   \n",
              "mean      77.637263      2.011994      2.918748    1061.762338      5.788046   \n",
              "std        0.110805      0.746193      1.037580     592.100777      6.361746   \n",
              "min       77.500072      0.000000      1.000000       1.000000      0.000000   \n",
              "25%       77.571956      2.000000      2.000000     700.000000      1.000000   \n",
              "50%       77.633368      2.000000      2.000000    1045.000000      5.000000   \n",
              "75%       77.695702      2.000000      4.000000    1280.000000     10.000000   \n",
              "max       80.266346      5.000000      4.000000   50000.000000    400.000000   \n",
              "\n",
              "         furnishing        facing         floor   total_floor  building_type  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000   20344.000000   \n",
              "mean       0.987318      2.171254      1.870035      3.797582       2.021481   \n",
              "std        0.356451      1.200480      2.182685      3.184757       0.941112   \n",
              "min        0.000000      1.000000      0.000000      0.000000       1.000000   \n",
              "25%        1.000000      1.000000      1.000000      2.000000       1.000000   \n",
              "50%        1.000000      2.000000      1.000000      3.000000       2.000000   \n",
              "75%        1.000000      2.000000      2.000000      4.000000       3.000000   \n",
              "max        2.000000      8.000000     25.000000     26.000000       4.000000   \n",
              "\n",
              "       water_supply    negotiable     cup_board     balconies       parking  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       1.821716      0.711266      4.572061      1.147857      2.072110   \n",
              "std        0.668913      0.453185    336.699793      0.993298      1.037781   \n",
              "min        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        1.000000      0.000000      1.000000      0.000000      1.000000   \n",
              "50%        2.000000      1.000000      2.000000      1.000000      3.000000   \n",
              "75%        2.000000      1.000000      3.000000      2.000000      3.000000   \n",
              "max        3.000000      1.000000  48026.000000     13.000000      3.000000   \n",
              "\n",
              "           bathroom           gym          lift  swimming_pool      internet  \\\n",
              "count  20344.000000  20344.000000  20344.000000   20344.000000  20344.000000   \n",
              "mean       1.858140      0.226406      0.361040       0.174548      0.661817   \n",
              "std        0.711027      0.418515      0.480314       0.379589      0.473103   \n",
              "min        1.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "25%        1.000000      0.000000      0.000000       0.000000      0.000000   \n",
              "50%        2.000000      0.000000      0.000000       0.000000      1.000000   \n",
              "75%        2.000000      0.000000      1.000000       0.000000      1.000000   \n",
              "max       21.000000      1.000000      1.000000       1.000000      1.000000   \n",
              "\n",
              "                 ac          club      intercom           cpa            fs  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.042568      0.185706      0.192932      0.291831      0.234664   \n",
              "std        0.201886      0.388879      0.394609      0.454616      0.423799   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "            servant      security            sc            gp          park  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.037161      0.472965      0.471687      0.099784      0.505948   \n",
              "std        0.189160      0.499281      0.499210      0.299719      0.499977   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      1.000000   \n",
              "75%        0.000000      1.000000      1.000000      0.000000      1.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "                rwh           stp            hk            pb            vp  \\\n",
              "count  20344.000000  20344.000000  20344.000000  20344.000000  20344.000000   \n",
              "mean       0.246412      0.170615      0.198781      0.365022      0.238547   \n",
              "std        0.430932      0.376182      0.399093      0.481448      0.426206   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "50%        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "75%        0.000000      0.000000      0.000000      1.000000      0.000000   \n",
              "max        1.000000      1.000000      1.000000      1.000000      1.000000   \n",
              "\n",
              "       no_of_amenities          rent  \n",
              "count     20344.000000  20344.000000  \n",
              "mean          5.178431  19048.188409  \n",
              "std           4.571908   8431.271930  \n",
              "min           0.000000   8000.000000  \n",
              "25%           2.000000  12500.000000  \n",
              "50%           3.000000  17500.000000  \n",
              "75%           8.000000  24000.000000  \n",
              "max          19.000000  50000.000000  "
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ohllVFd4Q8q",
        "outputId": "7e7d42de-6dd1-46ea-f0e4-9e8ed32c305c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "activation_day         31\n",
              "activation_month       12\n",
              "activation_year         2\n",
              "locality             1915\n",
              "latitude            17594\n",
              "longitude           17587\n",
              "type                    6\n",
              "lease_type              4\n",
              "property_size        1136\n",
              "property_age           43\n",
              "furnishing              3\n",
              "facing                  8\n",
              "floor                  23\n",
              "total_floor            27\n",
              "building_type           4\n",
              "water_supply            3\n",
              "negotiable              2\n",
              "cup_board              19\n",
              "balconies               9\n",
              "parking                 4\n",
              "bathroom                8\n",
              "gym                     2\n",
              "lift                    2\n",
              "swimming_pool           2\n",
              "internet                2\n",
              "ac                      2\n",
              "club                    2\n",
              "intercom                2\n",
              "cpa                     2\n",
              "fs                      2\n",
              "servant                 2\n",
              "security                2\n",
              "sc                      2\n",
              "gp                      2\n",
              "park                    2\n",
              "rwh                     2\n",
              "stp                     2\n",
              "hk                      2\n",
              "pb                      2\n",
              "vp                      2\n",
              "no_of_amenities        20\n",
              "rent                  206\n",
              "dtype: int64"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gl_vbsi5IRui",
        "outputId": "1af8d2e7-d57c-4d51-b1ee-b295f54a2ab3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "activation_day        int64\n",
              "activation_month      int64\n",
              "activation_year       int64\n",
              "locality             object\n",
              "latitude            float64\n",
              "longitude           float64\n",
              "type                  int64\n",
              "lease_type            int64\n",
              "property_size         int64\n",
              "property_age        float64\n",
              "furnishing            int64\n",
              "facing                int64\n",
              "floor               float64\n",
              "total_floor         float64\n",
              "building_type         int64\n",
              "water_supply          int64\n",
              "negotiable            int64\n",
              "cup_board           float64\n",
              "balconies           float64\n",
              "parking               int64\n",
              "bathroom            float64\n",
              "gym                   int64\n",
              "lift                  int64\n",
              "swimming_pool         int64\n",
              "internet              int64\n",
              "ac                    int64\n",
              "club                float64\n",
              "intercom              int64\n",
              "cpa                 float64\n",
              "fs                    int64\n",
              "servant             float64\n",
              "security              int64\n",
              "sc                    int64\n",
              "gp                  float64\n",
              "park                  int64\n",
              "rwh                 float64\n",
              "stp                 float64\n",
              "hk                    int64\n",
              "pb                    int64\n",
              "vp                  float64\n",
              "no_of_amenities     float64\n",
              "rent                float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4fSxZtTIbXJ",
        "outputId": "f02f48e5-80f9-4219-ab2f-40ceef2e3904"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "activation_day      0\n",
              "activation_month    0\n",
              "activation_year     0\n",
              "locality            0\n",
              "latitude            0\n",
              "longitude           0\n",
              "type                0\n",
              "lease_type          0\n",
              "property_size       0\n",
              "property_age        0\n",
              "furnishing          0\n",
              "facing              0\n",
              "floor               0\n",
              "total_floor         0\n",
              "building_type       0\n",
              "water_supply        0\n",
              "negotiable          0\n",
              "cup_board           0\n",
              "balconies           0\n",
              "parking             0\n",
              "bathroom            0\n",
              "gym                 0\n",
              "lift                0\n",
              "swimming_pool       0\n",
              "internet            0\n",
              "ac                  0\n",
              "club                0\n",
              "intercom            0\n",
              "cpa                 0\n",
              "fs                  0\n",
              "servant             0\n",
              "security            0\n",
              "sc                  0\n",
              "gp                  0\n",
              "park                0\n",
              "rwh                 0\n",
              "stp                 0\n",
              "hk                  0\n",
              "pb                  0\n",
              "vp                  0\n",
              "no_of_amenities     0\n",
              "rent                0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zE4_3Ln2LtUy",
        "outputId": "caa3e03d-18b5-4fba-ac39-bf766c277605"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['activation_day', 'activation_month', 'activation_year', 'locality',\n",
              "       'latitude', 'longitude', 'type', 'lease_type', 'property_size',\n",
              "       'property_age', 'furnishing', 'facing', 'floor', 'total_floor',\n",
              "       'building_type', 'water_supply', 'negotiable', 'cup_board', 'balconies',\n",
              "       'parking', 'bathroom', 'gym', 'lift', 'swimming_pool', 'internet', 'ac',\n",
              "       'club', 'intercom', 'cpa', 'fs', 'servant', 'security', 'sc', 'gp',\n",
              "       'park', 'rwh', 'stp', 'hk', 'pb', 'vp', 'no_of_amenities', 'rent'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "kc1qKls5LUQ9",
        "outputId": "43289ab3-66b8-424f-87b3-fb4d72c57a75"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Calculate the correlation matrix\n",
        "df1_corr = df1[['latitude', 'longitude', 'type', 'lease_type', 'property_size', 'property_age', 'furnishing', 'facing', 'floor', 'total_floor',\n",
        "                'building_type', 'water_supply', 'negotiable', 'cup_board', 'balconies', 'parking', 'bathroom', 'no_of_amenities', 'rent']].corr()\n",
        "\n",
        "# Create a heatmap\n",
        "plt.figure(figsize=(15, 5))  # Adjust the figure size if needed\n",
        "sns.heatmap(df1_corr, annot=True, fmt=\".2f\")\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1AxPZ0KvJDKe"
      },
      "outputs": [],
      "source": [
        "# df1.to_csv('df_final.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "riWDcM_TIrgm"
      },
      "source": [
        "## **Model Assessment**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DqeIOnQAVScR"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import ExtraTreesRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from xgboost import XGBRegressor\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "utoTRIS4KJow"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_csv('https://raw.githubusercontent.com/gopiashokan/Rental-Property-Prices-Prediction/main/Dataset/df_final.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "yTBcNtkZXHIn",
        "outputId": "2825d638-ebdd-4fa2-fa26-44134b8555d6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-4c05f737-3a4b-4386-a02d-745264db8786\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>activation_day</th>\n",
              "      <th>activation_month</th>\n",
              "      <th>activation_year</th>\n",
              "      <th>locality</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>type</th>\n",
              "      <th>lease_type</th>\n",
              "      <th>property_size</th>\n",
              "      <th>property_age</th>\n",
              "      <th>furnishing</th>\n",
              "      <th>facing</th>\n",
              "      <th>floor</th>\n",
              "      <th>total_floor</th>\n",
              "      <th>building_type</th>\n",
              "      <th>water_supply</th>\n",
              "      <th>negotiable</th>\n",
              "      <th>cup_board</th>\n",
              "      <th>balconies</th>\n",
              "      <th>parking</th>\n",
              "      <th>bathroom</th>\n",
              "      <th>gym</th>\n",
              "      <th>lift</th>\n",
              "      <th>swimming_pool</th>\n",
              "      <th>internet</th>\n",
              "      <th>ac</th>\n",
              "      <th>club</th>\n",
              "      <th>intercom</th>\n",
              "      <th>cpa</th>\n",
              "      <th>fs</th>\n",
              "      <th>servant</th>\n",
              "      <th>security</th>\n",
              "      <th>sc</th>\n",
              "      <th>gp</th>\n",
              "      <th>park</th>\n",
              "      <th>rwh</th>\n",
              "      <th>stp</th>\n",
              "      <th>hk</th>\n",
              "      <th>pb</th>\n",
              "      <th>vp</th>\n",
              "      <th>no_of_amenities</th>\n",
              "      <th>rent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20339</th>\n",
              "      <td>10</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "      <td>Ulsoor</td>\n",
              "      <td>12.979762</td>\n",
              "      <td>77.615395</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>850</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>9.0</td>\n",
              "      <td>20000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20340</th>\n",
              "      <td>19</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "      <td>Koramangala 4th Block</td>\n",
              "      <td>12.931072</td>\n",
              "      <td>77.632361</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1000</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20341</th>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "      <td>Kumaraswamy Layout</td>\n",
              "      <td>12.901800</td>\n",
              "      <td>77.566597</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>16000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20342</th>\n",
              "      <td>28</td>\n",
              "      <td>6</td>\n",
              "      <td>2018</td>\n",
              "      <td>Indiranagar</td>\n",
              "      <td>12.965502</td>\n",
              "      <td>77.647694</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>30000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20343</th>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>2018</td>\n",
              "      <td>Malleshwaram West</td>\n",
              "      <td>12.998645</td>\n",
              "      <td>77.574336</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>500</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4c05f737-3a4b-4386-a02d-745264db8786 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-4c05f737-3a4b-4386-a02d-745264db8786');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7ba69ae7-c4fb-419e-bab4-ba4c14080b6c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7ba69ae7-c4fb-419e-bab4-ba4c14080b6c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7ba69ae7-c4fb-419e-bab4-ba4c14080b6c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       activation_day  activation_month  activation_year  \\\n",
              "20339              10                11             2017   \n",
              "20340              19                10             2017   \n",
              "20341              21                12             2017   \n",
              "20342              28                 6             2018   \n",
              "20343               7                 8             2018   \n",
              "\n",
              "                    locality   latitude  longitude  type  lease_type  \\\n",
              "20339                 Ulsoor  12.979762  77.615395     1           4   \n",
              "20340  Koramangala 4th Block  12.931072  77.632361     2           4   \n",
              "20341     Kumaraswamy Layout  12.901800  77.566597     2           2   \n",
              "20342            Indiranagar  12.965502  77.647694     2           2   \n",
              "20343      Malleshwaram West  12.998645  77.574336     1           4   \n",
              "\n",
              "       property_size  property_age  furnishing  facing  floor  total_floor  \\\n",
              "20339            850          30.0           1       2    1.0          4.0   \n",
              "20340           1000          10.0           1       1    1.0          2.0   \n",
              "20341           1000           0.0           1       3    5.0          5.0   \n",
              "20342           1000           5.0           1       2    2.0          4.0   \n",
              "20343            500           3.0           1       2    1.0          1.0   \n",
              "\n",
              "       building_type  water_supply  negotiable  cup_board  balconies  parking  \\\n",
              "20339              1             2           1        1.0        1.0        1   \n",
              "20340              2             1           1        2.0        2.0        3   \n",
              "20341              1             3           1        2.0        2.0        3   \n",
              "20342              1             2           0        2.0        3.0        3   \n",
              "20343              2             1           1        1.0        1.0        1   \n",
              "\n",
              "       bathroom  gym  lift  swimming_pool  internet  ac  club  intercom  cpa  \\\n",
              "20339       2.0    0     1              0         1   0   0.0         0  0.0   \n",
              "20340       2.0    0     0              0         1   0   0.0         0  0.0   \n",
              "20341       2.0    1     1              0         1   0   1.0         0  1.0   \n",
              "20342       2.0    0     1              0         1   0   0.0         0  1.0   \n",
              "20343       1.0    0     0              0         0   0   0.0         0  0.0   \n",
              "\n",
              "       fs  servant  security  sc   gp  park  rwh  stp  hk  pb   vp  \\\n",
              "20339   1      0.0         1   1  1.0     1  0.0  0.0   1   0  1.0   \n",
              "20340   0      0.0         0   1  0.0     1  1.0  0.0   0   0  0.0   \n",
              "20341   0      0.0         1   0  0.0     1  0.0  0.0   0   1  0.0   \n",
              "20342   1      0.0         1   1  0.0     1  0.0  0.0   1   1  1.0   \n",
              "20343   0      0.0         0   1  0.0     1  0.0  0.0   0   0  0.0   \n",
              "\n",
              "       no_of_amenities     rent  \n",
              "20339              9.0  20000.0  \n",
              "20340              4.0  30000.0  \n",
              "20341              8.0  16000.0  \n",
              "20342             10.0  30000.0  \n",
              "20343              2.0   8500.0  "
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kya6CJw8lvwB",
        "outputId": "fe4f80af-25d0-4385-a5f7-d50a16ab06e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['activation_day', 'activation_month', 'activation_year', 'locality',\n",
              "       'latitude', 'longitude', 'type', 'lease_type', 'property_size',\n",
              "       'property_age', 'furnishing', 'facing', 'floor', 'total_floor',\n",
              "       'building_type', 'water_supply', 'negotiable', 'cup_board', 'balconies',\n",
              "       'parking', 'bathroom', 'gym', 'lift', 'swimming_pool', 'internet', 'ac',\n",
              "       'club', 'intercom', 'cpa', 'fs', 'servant', 'security', 'sc', 'gp',\n",
              "       'park', 'rwh', 'stp', 'hk', 'pb', 'vp', 'no_of_amenities', 'rent'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df1.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "7lxzC53-I1T4",
        "outputId": "bd1abc3a-2630-4204-e709-3fa13b286845"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.3.2'"
            ]
          },
          "execution_count": 49,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import sklearn\n",
        "sklearn.__version__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7bQdnX9bYcd-"
      },
      "outputs": [],
      "source": [
        "'''\n",
        "prevent the Colab runtime from disconnecting & automatically click the connect button in Google Colab at regular intervals (every minute)\n",
        "\n",
        "copy below javascript --> in colab, right click and select Inspect --> paste in console\n",
        "\n",
        "\n",
        "function ConnectButton(){\n",
        "     console.log(\"Connect pushed\");\n",
        "     document.querySelector(\"#top-toolbar > colab-connect-button\").shadowRoot.querySelector(\"#connect\").click()\n",
        "}\n",
        "setInterval(ConnectButton,60000);\n",
        "'''"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UI6Cj_NaWgeB"
      },
      "outputs": [],
      "source": [
        "# check the train and test accuracy using R2 (R-squared ---> coefficient of determination) to predict selling price\n",
        "\n",
        "def machine_learning_regression(df, algorithm):\n",
        "\n",
        "    x = df1.drop(columns=['locality', 'rent'], axis=1)\n",
        "    y = df1['rent']\n",
        "    x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n",
        "\n",
        "    model = algorithm().fit(x_train, y_train)\n",
        "    y_pred_train = model.predict(x_train)\n",
        "    y_pred_test = model.predict(x_test)\n",
        "    r2_train = r2_score(y_train, y_pred_train)\n",
        "    r2_test = r2_score(y_test, y_pred_test)\n",
        "\n",
        "    # algo = str(algorithm).split(\"'\")[1].split(\".\")[-1]\n",
        "    accuracy_metrics = {'algorithm': algorithm.__name__,\n",
        "                        'R2_train' : r2_train,\n",
        "                        'R2_test'  : r2_test}\n",
        "\n",
        "    return accuracy_metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-EjLqDMcXhbS",
        "outputId": "3a0d52e6-023d-4dc4-f203-bb99deac1ceb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'algorithm': 'DecisionTreeRegressor', 'R2_train': 1.0, 'R2_test': 0.6119988857675056}\n",
            "{'algorithm': 'ExtraTreesRegressor', 'R2_train': 1.0, 'R2_test': 0.7739047514638809}\n",
            "{'algorithm': 'RandomForestRegressor', 'R2_train': 0.9727709802381985, 'R2_test': 0.8058462726189007}\n",
            "{'algorithm': 'AdaBoostRegressor', 'R2_train': 0.5303647320589631, 'R2_test': 0.5122885380778592}\n",
            "{'algorithm': 'GradientBoostingRegressor', 'R2_train': 0.8124807266319778, 'R2_test': 0.7907707121330785}\n",
            "{'algorithm': 'XGBRegressor', 'R2_train': 0.9338734998820475, 'R2_test': 0.8157097298212435}\n"
          ]
        }
      ],
      "source": [
        "print(machine_learning_regression(df1, DecisionTreeRegressor))\n",
        "print(machine_learning_regression(df1, ExtraTreesRegressor))\n",
        "print(machine_learning_regression(df1, RandomForestRegressor))\n",
        "print(machine_learning_regression(df1, AdaBoostRegressor))\n",
        "print(machine_learning_regression(df1, GradientBoostingRegressor))\n",
        "print(machine_learning_regression(df1, XGBRegressor))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        },
        "id": "tlxddGdxbrhN",
        "outputId": "440ca598-cac3-47cc-bcb1-6d7063913c3c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [2, 5, 10, 20],\n",
              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;, None],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 5, 10]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,\n",
              "             param_grid={&#x27;max_depth&#x27;: [2, 5, 10, 20],\n",
              "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;, None],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 5, 10]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestRegressor(), n_jobs=-1,\n",
              "             param_grid={'max_depth': [2, 5, 10, 20],\n",
              "                         'max_features': ['sqrt', 'log2', None],\n",
              "                         'min_samples_leaf': [1, 2, 4],\n",
              "                         'min_samples_split': [2, 5, 10]})"
            ]
          },
          "execution_count": 52,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = df1.drop(columns=['locality', 'rent'], axis=1)\n",
        "y = df1['rent']\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n",
        "\n",
        "param_grid_r = {'max_depth'      : [2, 5, 10, 20],\n",
        "              'min_samples_split': [2, 5, 10],\n",
        "              'min_samples_leaf' : [1, 2, 4],\n",
        "              'max_features'     : ['sqrt', 'log2', None]}\n",
        "\n",
        "grid_search_r = GridSearchCV(estimator=RandomForestRegressor(), param_grid=param_grid_r, cv=5, n_jobs=-1)\n",
        "grid_search_r.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MPamxmCpcL6s",
        "outputId": "b3e314f4-33e3-4012-fa75-771bce243bea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'max_depth': 20,\n",
              "  'max_features': None,\n",
              "  'min_samples_leaf': 1,\n",
              "  'min_samples_split': 10},\n",
              " 0.8025521909826029)"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# best parameters for hypertuning the random forest algorithm for better accuracy in unseen data\n",
        "grid_search_r.best_params_,grid_search_r.best_score_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPyfxigAg7nP",
        "outputId": "91066c88-95c3-44cb-b4ed-84bb2996a8e6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0.9398248514990458, 0.8029100515725566)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = df1.drop(columns=['locality', 'rent'], axis=1)\n",
        "y = df1['rent']\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n",
        "\n",
        "model = RandomForestRegressor(max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=10).fit(x_train, y_train)\n",
        "y_pred_train = model.predict(x_train)\n",
        "y_pred_test = model.predict(x_test)\n",
        "\n",
        "r2_train = r2_score(y_train, y_pred_train)\n",
        "r2_test = r2_score(y_test, y_pred_test)\n",
        "r2_train, r2_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbDf4hvUhM8P",
        "outputId": "4da2d98d-4a9b-4448-e127-b51e9ae19593"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'R2': 0.8050123485491882,\n",
              " 'Mean Absolute Error': 2648.4062092299278,\n",
              " 'Mean Squared Error': 14027182.199315216,\n",
              " 'Root Mean Squared Error': 3745.2879995155536}"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x = df1.drop(columns=['locality', 'rent'], axis=1)\n",
        "y = df1['rent']\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.2,random_state=42)\n",
        "\n",
        "model = RandomForestRegressor(max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=5).fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = np.sqrt(mse)\n",
        "r2 = r2_score(y_test, y_pred)\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "\n",
        "metrics = {'R2': r2,\n",
        "           'Mean Absolute Error': mae,\n",
        "           'Mean Squared Error': mse,\n",
        "           'Root Mean Squared Error': rmse}\n",
        "\n",
        "metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8-js8Qh33cK3",
        "outputId": "75b4f020-3ae8-40d7-84ce-61b2f1fe153b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "24078.025941253196"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "user_data = np.array([[10,9,2017,12.93,77.67,2,4,1400,4,1,5,3,4,1,1,1,2,2,3,2,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,8]])\n",
        "y_pred = model.predict(user_data)\n",
        "y_pred[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fUrkSj_66sJC"
      },
      "outputs": [],
      "source": [
        "# save the regression model by using pickle\n",
        "\n",
        "with open('regression_model.pkl', 'wb') as f:\n",
        "    pickle.dump(model, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tWJ9qc06xk7",
        "outputId": "def95bec-fbcd-45e9-f68d-e401112212f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "24078.025941253196"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# load the pickle model\n",
        "\n",
        "with open('/content/regression_model.pkl', 'rb') as f:\n",
        "    model = pickle.load(f)\n",
        "\n",
        "user_data = np.array([[10,9,2017,12.93,77.67,2,4,1400,4,1,5,3,4,1,1,1,2,2,3,2,0,1,0,0,0,0,0,0,0,0,1,1,0,1,1,1,0,1,1,8]])\n",
        "y_pred = model.predict(user_data)\n",
        "y_pred[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "age4w8WM7KAH"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "6JIAMDm1IluV",
        "riWDcM_TIrgm"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}