Melbourne_Real_Estate.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cdW2mjD1a58G"
      },
      "source": [
        "Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MfKhC7DKHZ0S"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "%matplotlib inline\n",
        "#This is done so that to add plots in our notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RU0olvDobAIP"
      },
      "source": [
        "Importing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YTEIX_aXBaF4"
      },
      "outputs": [],
      "source": [
        "HouseDF = pd.read_csv('melb_data.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": false,
        "id": "pm66bB90fB_d",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Suburb</th>\n",
              "      <th>Address</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Method</th>\n",
              "      <th>SellerG</th>\n",
              "      <th>Date</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>...</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>CouncilArea</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Regionname</th>\n",
              "      <th>Propertycount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>85 Turner St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1480000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>3/12/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.79960</td>\n",
              "      <td>144.99840</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/02/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80790</td>\n",
              "      <td>144.99340</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80930</td>\n",
              "      <td>144.99440</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>40 Federation La</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.79690</td>\n",
              "      <td>144.99690</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>55a Park St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>4/06/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.80720</td>\n",
              "      <td>144.99410</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13575</th>\n",
              "      <td>Wheelers Hill</td>\n",
              "      <td>12 Strada Cr</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1245000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Barry</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>16.7</td>\n",
              "      <td>3150.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>652.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1981.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.90562</td>\n",
              "      <td>145.16761</td>\n",
              "      <td>South-Eastern Metropolitan</td>\n",
              "      <td>7392.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13576</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>77 Merrett Dr</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1031000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Williams</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>333.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>1995.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85927</td>\n",
              "      <td>144.87904</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13577</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>83 Power St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1170000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Raine</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>436.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85274</td>\n",
              "      <td>144.88738</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13578</th>\n",
              "      <td>Williamstown</td>\n",
              "      <td>96 Verdon St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>2500000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Sweeney</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.8</td>\n",
              "      <td>3016.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>866.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.85908</td>\n",
              "      <td>144.89299</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6380.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13579</th>\n",
              "      <td>Yarraville</td>\n",
              "      <td>6 Agnes St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1285000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Village</td>\n",
              "      <td>26/08/2017</td>\n",
              "      <td>6.3</td>\n",
              "      <td>3013.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>362.0</td>\n",
              "      <td>112.0</td>\n",
              "      <td>1920.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-37.81188</td>\n",
              "      <td>144.88449</td>\n",
              "      <td>Western Metropolitan</td>\n",
              "      <td>6543.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13580 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              Suburb           Address  Rooms Type      Price Method  \\\n",
              "0         Abbotsford      85 Turner St      2    h  1480000.0      S   \n",
              "1         Abbotsford   25 Bloomburg St      2    h  1035000.0      S   \n",
              "2         Abbotsford      5 Charles St      3    h  1465000.0     SP   \n",
              "3         Abbotsford  40 Federation La      3    h   850000.0     PI   \n",
              "4         Abbotsford       55a Park St      4    h  1600000.0     VB   \n",
              "...              ...               ...    ...  ...        ...    ...   \n",
              "13575  Wheelers Hill      12 Strada Cr      4    h  1245000.0      S   \n",
              "13576   Williamstown     77 Merrett Dr      3    h  1031000.0     SP   \n",
              "13577   Williamstown       83 Power St      3    h  1170000.0      S   \n",
              "13578   Williamstown      96 Verdon St      4    h  2500000.0     PI   \n",
              "13579     Yarraville        6 Agnes St      4    h  1285000.0     SP   \n",
              "\n",
              "        SellerG        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  \\\n",
              "0        Biggin   3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0   \n",
              "1        Biggin   4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0   \n",
              "2        Biggin   4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0   \n",
              "3        Biggin   4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0   \n",
              "4        Nelson   4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0   \n",
              "...         ...         ...       ...       ...  ...       ...  ...       ...   \n",
              "13575     Barry  26/08/2017      16.7    3150.0  ...       2.0  2.0     652.0   \n",
              "13576  Williams  26/08/2017       6.8    3016.0  ...       2.0  2.0     333.0   \n",
              "13577     Raine  26/08/2017       6.8    3016.0  ...       2.0  4.0     436.0   \n",
              "13578   Sweeney  26/08/2017       6.8    3016.0  ...       1.0  5.0     866.0   \n",
              "13579   Village  26/08/2017       6.3    3013.0  ...       1.0  1.0     362.0   \n",
              "\n",
              "       BuildingArea  YearBuilt  CouncilArea Lattitude  Longtitude  \\\n",
              "0               NaN        NaN        Yarra -37.79960   144.99840   \n",
              "1              79.0     1900.0        Yarra -37.80790   144.99340   \n",
              "2             150.0     1900.0        Yarra -37.80930   144.99440   \n",
              "3               NaN        NaN        Yarra -37.79690   144.99690   \n",
              "4             142.0     2014.0        Yarra -37.80720   144.99410   \n",
              "...             ...        ...          ...       ...         ...   \n",
              "13575           NaN     1981.0          NaN -37.90562   145.16761   \n",
              "13576         133.0     1995.0          NaN -37.85927   144.87904   \n",
              "13577           NaN     1997.0          NaN -37.85274   144.88738   \n",
              "13578         157.0     1920.0          NaN -37.85908   144.89299   \n",
              "13579         112.0     1920.0          NaN -37.81188   144.88449   \n",
              "\n",
              "                       Regionname Propertycount  \n",
              "0           Northern Metropolitan        4019.0  \n",
              "1           Northern Metropolitan        4019.0  \n",
              "2           Northern Metropolitan        4019.0  \n",
              "3           Northern Metropolitan        4019.0  \n",
              "4           Northern Metropolitan        4019.0  \n",
              "...                           ...           ...  \n",
              "13575  South-Eastern Metropolitan        7392.0  \n",
              "13576        Western Metropolitan        6380.0  \n",
              "13577        Western Metropolitan        6380.0  \n",
              "13578        Western Metropolitan        6380.0  \n",
              "13579        Western Metropolitan        6543.0  \n",
              "\n",
              "[13580 rows x 21 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
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      "source": [
        "HouseDF"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eH9MlfNnfB_d"
      },
      "source": [
        "### Data Exploration"
      ]
    },
    {
      "cell_type": "code",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Suburb</th>\n",
              "      <th>Address</th>\n",
              "      <th>Rooms</th>\n",
              "      <th>Type</th>\n",
              "      <th>Price</th>\n",
              "      <th>Method</th>\n",
              "      <th>SellerG</th>\n",
              "      <th>Date</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
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              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>CouncilArea</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Regionname</th>\n",
              "      <th>Propertycount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>85 Turner St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1480000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>3/12/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>202.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.7996</td>\n",
              "      <td>144.9984</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
              "      <td>2</td>\n",
              "      <td>h</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/02/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8079</td>\n",
              "      <td>144.9934</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8093</td>\n",
              "      <td>144.9944</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>40 Federation La</td>\n",
              "      <td>3</td>\n",
              "      <td>h</td>\n",
              "      <td>850000.0</td>\n",
              "      <td>PI</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.7969</td>\n",
              "      <td>144.9969</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>55a Park St</td>\n",
              "      <td>4</td>\n",
              "      <td>h</td>\n",
              "      <td>1600000.0</td>\n",
              "      <td>VB</td>\n",
              "      <td>Nelson</td>\n",
              "      <td>4/06/2016</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>142.0</td>\n",
              "      <td>2014.0</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>-37.8072</td>\n",
              "      <td>144.9941</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "      <td>4019.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Suburb           Address  Rooms Type      Price Method SellerG  \\\n",
              "0  Abbotsford      85 Turner St      2    h  1480000.0      S  Biggin   \n",
              "1  Abbotsford   25 Bloomburg St      2    h  1035000.0      S  Biggin   \n",
              "2  Abbotsford      5 Charles St      3    h  1465000.0     SP  Biggin   \n",
              "3  Abbotsford  40 Federation La      3    h   850000.0     PI  Biggin   \n",
              "4  Abbotsford       55a Park St      4    h  1600000.0     VB  Nelson   \n",
              "\n",
              "        Date  Distance  Postcode  ...  Bathroom  Car  Landsize  BuildingArea  \\\n",
              "0  3/12/2016       2.5    3067.0  ...       1.0  1.0     202.0           NaN   \n",
              "1  4/02/2016       2.5    3067.0  ...       1.0  0.0     156.0          79.0   \n",
              "2  4/03/2017       2.5    3067.0  ...       2.0  0.0     134.0         150.0   \n",
              "3  4/03/2017       2.5    3067.0  ...       2.0  1.0      94.0           NaN   \n",
              "4  4/06/2016       2.5    3067.0  ...       1.0  2.0     120.0         142.0   \n",
              "\n",
              "   YearBuilt  CouncilArea Lattitude  Longtitude             Regionname  \\\n",
              "0        NaN        Yarra  -37.7996    144.9984  Northern Metropolitan   \n",
              "1     1900.0        Yarra  -37.8079    144.9934  Northern Metropolitan   \n",
              "2     1900.0        Yarra  -37.8093    144.9944  Northern Metropolitan   \n",
              "3        NaN        Yarra  -37.7969    144.9969  Northern Metropolitan   \n",
              "4     2014.0        Yarra  -37.8072    144.9941  Northern Metropolitan   \n",
              "\n",
              "  Propertycount  \n",
              "0        4019.0  \n",
              "1        4019.0  \n",
              "2        4019.0  \n",
              "3        4019.0  \n",
              "4        4019.0  \n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HouseDF.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "9VhlPGY7Bg5g",
        "outputId": "fc4e9e84-b236-43b8-aed1-808be7354450"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Rooms</th>\n",
              "      <th>Price</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>Lattitude</th>\n",
              "      <th>Longtitude</th>\n",
              "      <th>Propertycount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13580.000000</td>\n",
              "      <td>1.358000e+04</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13518.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>7130.000000</td>\n",
              "      <td>8205.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "      <td>13580.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.937997</td>\n",
              "      <td>1.075684e+06</td>\n",
              "      <td>10.137776</td>\n",
              "      <td>3105.301915</td>\n",
              "      <td>2.914728</td>\n",
              "      <td>1.534242</td>\n",
              "      <td>1.610075</td>\n",
              "      <td>558.416127</td>\n",
              "      <td>151.967650</td>\n",
              "      <td>1964.684217</td>\n",
              "      <td>-37.809203</td>\n",
              "      <td>144.995216</td>\n",
              "      <td>7454.417378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.955748</td>\n",
              "      <td>6.393107e+05</td>\n",
              "      <td>5.868725</td>\n",
              "      <td>90.676964</td>\n",
              "      <td>0.965921</td>\n",
              "      <td>0.691712</td>\n",
              "      <td>0.962634</td>\n",
              "      <td>3990.669241</td>\n",
              "      <td>541.014538</td>\n",
              "      <td>37.273762</td>\n",
              "      <td>0.079260</td>\n",
              "      <td>0.103916</td>\n",
              "      <td>4378.581772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.500000e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3000.000000</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>1196.000000</td>\n",
              "      <td>-38.182550</td>\n",
              "      <td>144.431810</td>\n",
              "      <td>249.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>6.500000e+05</td>\n",
              "      <td>6.100000</td>\n",
              "      <td>3044.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>177.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>1940.000000</td>\n",
              "      <td>-37.856822</td>\n",
              "      <td>144.929600</td>\n",
              "      <td>4380.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>9.030000e+05</td>\n",
              "      <td>9.200000</td>\n",
              "      <td>3084.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>440.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>1970.000000</td>\n",
              "      <td>-37.802355</td>\n",
              "      <td>145.000100</td>\n",
              "      <td>6555.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.330000e+06</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>651.000000</td>\n",
              "      <td>174.000000</td>\n",
              "      <td>1999.000000</td>\n",
              "      <td>-37.756400</td>\n",
              "      <td>145.058305</td>\n",
              "      <td>10331.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>9.000000e+06</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>3977.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>433014.000000</td>\n",
              "      <td>44515.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>-37.408530</td>\n",
              "      <td>145.526350</td>\n",
              "      <td>21650.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Rooms         Price      Distance      Postcode      Bedroom2  \\\n",
              "count  13580.000000  1.358000e+04  13580.000000  13580.000000  13580.000000   \n",
              "mean       2.937997  1.075684e+06     10.137776   3105.301915      2.914728   \n",
              "std        0.955748  6.393107e+05      5.868725     90.676964      0.965921   \n",
              "min        1.000000  8.500000e+04      0.000000   3000.000000      0.000000   \n",
              "25%        2.000000  6.500000e+05      6.100000   3044.000000      2.000000   \n",
              "50%        3.000000  9.030000e+05      9.200000   3084.000000      3.000000   \n",
              "75%        3.000000  1.330000e+06     13.000000   3148.000000      3.000000   \n",
              "max       10.000000  9.000000e+06     48.100000   3977.000000     20.000000   \n",
              "\n",
              "           Bathroom           Car       Landsize  BuildingArea    YearBuilt  \\\n",
              "count  13580.000000  13518.000000   13580.000000   7130.000000  8205.000000   \n",
              "mean       1.534242      1.610075     558.416127    151.967650  1964.684217   \n",
              "std        0.691712      0.962634    3990.669241    541.014538    37.273762   \n",
              "min        0.000000      0.000000       0.000000      0.000000  1196.000000   \n",
              "25%        1.000000      1.000000     177.000000     93.000000  1940.000000   \n",
              "50%        1.000000      2.000000     440.000000    126.000000  1970.000000   \n",
              "75%        2.000000      2.000000     651.000000    174.000000  1999.000000   \n",
              "max        8.000000     10.000000  433014.000000  44515.000000  2018.000000   \n",
              "\n",
              "          Lattitude    Longtitude  Propertycount  \n",
              "count  13580.000000  13580.000000   13580.000000  \n",
              "mean     -37.809203    144.995216    7454.417378  \n",
              "std        0.079260      0.103916    4378.581772  \n",
              "min      -38.182550    144.431810     249.000000  \n",
              "25%      -37.856822    144.929600    4380.000000  \n",
              "50%      -37.802355    145.000100    6555.000000  \n",
              "75%      -37.756400    145.058305   10331.000000  \n",
              "max      -37.408530    145.526350   21650.000000  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HouseDF.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FHyej4hGBkki",
        "outputId": "f6a12141-d6b5-4973-8117-98e99dfd4d0d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13580 entries, 0 to 13579\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Suburb         13580 non-null  object \n",
            " 1   Address        13580 non-null  object \n",
            " 2   Rooms          13580 non-null  int64  \n",
            " 3   Type           13580 non-null  object \n",
            " 4   Price          13580 non-null  float64\n",
            " 5   Method         13580 non-null  object \n",
            " 6   SellerG        13580 non-null  object \n",
            " 7   Date           13580 non-null  object \n",
            " 8   Distance       13580 non-null  float64\n",
            " 9   Postcode       13580 non-null  float64\n",
            " 10  Bedroom2       13580 non-null  float64\n",
            " 11  Bathroom       13580 non-null  float64\n",
            " 12  Car            13518 non-null  float64\n",
            " 13  Landsize       13580 non-null  float64\n",
            " 14  BuildingArea   7130 non-null   float64\n",
            " 15  YearBuilt      8205 non-null   float64\n",
            " 16  CouncilArea    12211 non-null  object \n",
            " 17  Lattitude      13580 non-null  float64\n",
            " 18  Longtitude     13580 non-null  float64\n",
            " 19  Regionname     13580 non-null  object \n",
            " 20  Propertycount  13580 non-null  float64\n",
            "dtypes: float64(12), int64(1), object(8)\n",
            "memory usage: 2.2+ MB\n"
          ]
        }
      ],
      "source": [
        "HouseDF.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-jciY_qhBnPd",
        "outputId": "16403c66-c2ec-42ac-bbe8-daf1e27646c2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Suburb', 'Address', 'Rooms', 'Type', 'Price', 'Method', 'SellerG',\n",
              "       'Date', 'Distance', 'Postcode', 'Bedroom2', 'Bathroom', 'Car',\n",
              "       'Landsize', 'BuildingArea', 'YearBuilt', 'CouncilArea', 'Lattitude',\n",
              "       'Longtitude', 'Regionname', 'Propertycount'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HouseDF.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mcnBMzaDfB_e"
      },
      "source": [
        "### Preparing the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMtxSzfWSCln",
        "outputId": "e3cb5179-bfdc-482a-ffca-6e50f8608104",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Suburb              0\n",
              "Address             0\n",
              "Rooms               0\n",
              "Type                0\n",
              "Price               0\n",
              "Method              0\n",
              "SellerG             0\n",
              "Date                0\n",
              "Distance            0\n",
              "Postcode            0\n",
              "Bedroom2            0\n",
              "Bathroom            0\n",
              "Car                62\n",
              "Landsize            0\n",
              "BuildingArea     6450\n",
              "YearBuilt        5375\n",
              "CouncilArea      1369\n",
              "Lattitude           0\n",
              "Longtitude          0\n",
              "Regionname          0\n",
              "Propertycount       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HouseDF.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BQM9ifNgSCiY"
      },
      "outputs": [],
      "source": [
        "# drop missing value\n",
        "HouseDF.dropna(inplace = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09jVicyNSCfi",
        "outputId": "f1d5204c-721d-4239-e187-39a3e8a3cdb5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Suburb           0\n",
              "Address          0\n",
              "Rooms            0\n",
              "Type             0\n",
              "Price            0\n",
              "Method           0\n",
              "SellerG          0\n",
              "Date             0\n",
              "Distance         0\n",
              "Postcode         0\n",
              "Bedroom2         0\n",
              "Bathroom         0\n",
              "Car              0\n",
              "Landsize         0\n",
              "BuildingArea     0\n",
              "YearBuilt        0\n",
              "CouncilArea      0\n",
              "Lattitude        0\n",
              "Longtitude       0\n",
              "Regionname       0\n",
              "Propertycount    0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HouseDF.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IP01TGhKCNfW"
      },
      "source": [
        "One hot encoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ckgxYCPBucM",
        "outputId": "c8a3c90d-a449-4659-994f-5bf3791b86c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "total object data type features: 8\n",
            "Features \n",
            " ['Suburb', 'Address', 'Type', 'Method', 'SellerG', 'Date', 'CouncilArea', 'Regionname']\n"
          ]
        }
      ],
      "source": [
        "df_encod = HouseDF.copy()\n",
        "\n",
        "object_features=df_encod.select_dtypes(include=\"object\").columns.tolist()\n",
        "\n",
        "print(\"total object data type features:\",len(object_features))\n",
        "\n",
        "print(\"Features \\n\",object_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "RFVNDQy8DCR4",
        "outputId": "060947b8-85e2-4fe2-e150-c7e1c58d71ce"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Suburb</th>\n",
              "      <th>Address</th>\n",
              "      <th>Type</th>\n",
              "      <th>Method</th>\n",
              "      <th>SellerG</th>\n",
              "      <th>Date</th>\n",
              "      <th>CouncilArea</th>\n",
              "      <th>Regionname</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>25 Bloomburg St</td>\n",
              "      <td>h</td>\n",
              "      <td>S</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/02/2016</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Abbotsford</td>\n",
              "      <td>5 Charles St</td>\n",
              "      <td>h</td>\n",
              "      <td>SP</td>\n",
              "      <td>Biggin</td>\n",
              "      <td>4/03/2017</td>\n",
              "      <td>Yarra</td>\n",
              "      <td>Northern Metropolitan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Suburb          Address Type Method SellerG       Date CouncilArea  \\\n",
              "1  Abbotsford  25 Bloomburg St    h      S  Biggin  4/02/2016       Yarra   \n",
              "2  Abbotsford     5 Charles St    h     SP  Biggin  4/03/2017       Yarra   \n",
              "\n",
              "              Regionname  \n",
              "1  Northern Metropolitan  \n",
              "2  Northern Metropolitan  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_encod[object_features].head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QwtVgVG_EdZo",
        "outputId": "2493f0de-5745-4010-b7d9-f1060a523d6c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "shape of DF before encoding :  (6196, 21)\n",
            "shape of DF after encoding :  (6196, 6710)\n"
          ]
        }
      ],
      "source": [
        "print(\"shape of DF before encoding : \",df_encod.shape)\n",
        "df_encod=pd.get_dummies(df_encod,columns=object_features,prefix=object_features,drop_first=True)\n",
        "print(\"shape of DF after encoding : \",df_encod.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "Sl2Sr2qtE_YB",
        "outputId": "d7454e14-6778-4c72-85e2-01492202a054"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Rooms</th>\n",
              "      <th>Price</th>\n",
              "      <th>Distance</th>\n",
              "      <th>Postcode</th>\n",
              "      <th>Bedroom2</th>\n",
              "      <th>Bathroom</th>\n",
              "      <th>Car</th>\n",
              "      <th>Landsize</th>\n",
              "      <th>BuildingArea</th>\n",
              "      <th>YearBuilt</th>\n",
              "      <th>...</th>\n",
              "      <th>CouncilArea_Wyndham</th>\n",
              "      <th>CouncilArea_Yarra</th>\n",
              "      <th>CouncilArea_Yarra Ranges</th>\n",
              "      <th>Regionname_Eastern Victoria</th>\n",
              "      <th>Regionname_Northern Metropolitan</th>\n",
              "      <th>Regionname_Northern Victoria</th>\n",
              "      <th>Regionname_South-Eastern Metropolitan</th>\n",
              "      <th>Regionname_Southern Metropolitan</th>\n",
              "      <th>Regionname_Western Metropolitan</th>\n",
              "      <th>Regionname_Western Victoria</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1035000.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>156.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1465000.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3067.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>134.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>1900.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2 rows × 6710 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Rooms      Price  Distance  Postcode  Bedroom2  Bathroom  Car  Landsize  \\\n",
              "1      2  1035000.0       2.5    3067.0       2.0       1.0  0.0     156.0   \n",
              "2      3  1465000.0       2.5    3067.0       3.0       2.0  0.0     134.0   \n",
              "\n",
              "   BuildingArea  YearBuilt  ...  CouncilArea_Wyndham  CouncilArea_Yarra  \\\n",
              "1          79.0     1900.0  ...                    0                  1   \n",
              "2         150.0     1900.0  ...                    0                  1   \n",
              "\n",
              "   CouncilArea_Yarra Ranges  Regionname_Eastern Victoria  \\\n",
              "1                         0                            0   \n",
              "2                         0                            0   \n",
              "\n",
              "   Regionname_Northern Metropolitan  Regionname_Northern Victoria  \\\n",
              "1                                 1                             0   \n",
              "2                                 1                             0   \n",
              "\n",
              "   Regionname_South-Eastern Metropolitan  Regionname_Southern Metropolitan  \\\n",
              "1                                      0                                 0   \n",
              "2                                      0                                 0   \n",
              "\n",
              "   Regionname_Western Metropolitan  Regionname_Western Victoria  \n",
              "1                                0                            0  \n",
              "2                                0                            0  \n",
              "\n",
              "[2 rows x 6710 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_encod.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DLKIiTiXKzTn",
        "outputId": "7452f4b4-3da8-4d70-b0ad-471338ea4e47"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Sony\\AppData\\Local\\Temp\\ipykernel_12100\\88291756.py:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df_encod['Price'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from scipy import stats\n",
        "sns.distplot(df_encod['Price'])\n",
        "fig = plt.figure()\n",
        "prob = stats.probplot(df_encod[\"Price\"], plot=plt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dUY0GPO3NJQj",
        "outputId": "7b9dbf2b-fa7b-4e8d-a8d1-d673a69a70a9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\Sony\\AppData\\Local\\Temp\\ipykernel_12100\\4285129832.py:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  dist_price = sns.distplot(df_encod[\"LogPrice\"])\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Since the probability Plot looks like a log distribution , so now we can use np.log to transform it\n",
        "df_encod[\"LogPrice\"] = np.log(df_encod[\"Price\"])\n",
        "dist_price = sns.distplot(df_encod[\"LogPrice\"])\n",
        "fig = plt.figure()\n",
        "prob_log = stats.probplot(df_encod[\"LogPrice\"], plot=plt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YciEZTXBO7oi"
      },
      "outputs": [],
      "source": [
        "corr = HouseDF.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "EVpmKlTHPYiG",
        "outputId": "a737c626-8c7e-435b-9a60-e3fc73e6ba5b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.heatmap(corr, annot=True, cmap='coolwarm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CAKW0AzRPb_V"
      },
      "outputs": [],
      "source": [
        "X = df_encod.drop(['Price','LogPrice'],axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eju81LwNQDYB"
      },
      "outputs": [],
      "source": [
        "y = df_encod['Price']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zC_00XFRQHsN"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=101)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BS9GoeVWfB_f"
      },
      "source": [
        "### Standarisation of data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gtkhQy7tRD_K"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdV3DX9NRGSq",
        "outputId": "fd6df4f1-089a-4aac-9915-a41c3179efe5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "StandardScaler()\n"
          ]
        }
      ],
      "source": [
        "print(scaler.fit(X_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hmtU97yKRIFY"
      },
      "outputs": [],
      "source": [
        "X_train=scaler.transform(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6FVEVkrwRJ1M"
      },
      "outputs": [],
      "source": [
        "X_test = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xr24za9AfB_g"
      },
      "source": [
        "### Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "mY47TCuHRLkd",
        "outputId": "b3fa8649-ec6a-42ac-8860-715de526a454"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "lr = LinearRegression()\n",
        "\n",
        "lr.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dteKsPixRRSR"
      },
      "outputs": [],
      "source": [
        "lr_pred = lr.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0D56IwoVOHn",
        "outputId": "32fcc63a-276e-4326-9a51-cf369ca133e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1075584.1046542842\n"
          ]
        }
      ],
      "source": [
        "print(lr.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CLv-w7UdVPlC"
      },
      "outputs": [],
      "source": [
        "coeff_df = pd.DataFrame(lr.coef_,X.columns,columns=['Coefficient'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "SgXxZO_eVRFn",
        "outputId": "b00bde1a-591c-4aeb-dbd0-25dee9abd3b6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Rooms</th>\n",
              "      <td>62045.781831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Distance</th>\n",
              "      <td>-41434.535726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Postcode</th>\n",
              "      <td>6792.237575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bedroom2</th>\n",
              "      <td>62548.472296</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bathroom</th>\n",
              "      <td>75028.334604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regionname_Northern Victoria</th>\n",
              "      <td>-4090.265980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regionname_South-Eastern Metropolitan</th>\n",
              "      <td>-6661.998359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regionname_Southern Metropolitan</th>\n",
              "      <td>37951.388463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regionname_Western Metropolitan</th>\n",
              "      <td>-18630.687061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Regionname_Western Victoria</th>\n",
              "      <td>-6997.582531</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6709 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Coefficient\n",
              "Rooms                                  62045.781831\n",
              "Distance                              -41434.535726\n",
              "Postcode                                6792.237575\n",
              "Bedroom2                               62548.472296\n",
              "Bathroom                               75028.334604\n",
              "...                                             ...\n",
              "Regionname_Northern Victoria           -4090.265980\n",
              "Regionname_South-Eastern Metropolitan  -6661.998359\n",
              "Regionname_Southern Metropolitan       37951.388463\n",
              "Regionname_Western Metropolitan       -18630.687061\n",
              "Regionname_Western Victoria            -6997.582531\n",
              "\n",
              "[6709 rows x 1 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "coeff_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "TBLFSurGVUME",
        "outputId": "94a20b8f-166a-473a-f216-6c5bcda2cec2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x1cf13e1e890>"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.displot((y_test-lr_pred),bins=50)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EEVPY-SnfB_g"
      },
      "outputs": [],
      "source": [
        "training_score = lr.score(X_train, y_train)\n",
        "testing_score = lr.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aa72xJ1WfB_k",
        "outputId": "319bccdc-2d6c-4987-c348-7c9c265334d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: Linear Regression\n",
            "Training Score: 1.0\n",
            "Testing Score: 0.6266073009427093\n"
          ]
        }
      ],
      "source": [
        "print(f\"Model: Linear Regression\")\n",
        "print(f\"Training Score: {training_score}\")\n",
        "print(f\"Testing Score: {testing_score}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CeKhuow1VoKv",
        "outputId": "3a89e62b-c732-4c14-aef4-b6833e78ea52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE: 246552.61869038877\n",
            "MSE: 157640082079.1016\n",
            "R2_score 0.6266073009427093\n"
          ]
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "lr_mae = metrics.mean_absolute_error(y_test, lr_pred)\n",
        "lr_mse = metrics.mean_squared_error(y_test, lr_pred)\n",
        "lr_rmse = np.sqrt(metrics.mean_squared_error(y_test, lr_pred))\n",
        "print('MAE:', metrics.mean_absolute_error(y_test, lr_pred))\n",
        "print('MSE:', metrics.mean_squared_error(y_test, lr_pred))\n",
        "#print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, lr_pred)))\n",
        "print('R2_score',metrics.r2_score(y_test,lr_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpzH8VtsfB_k"
      },
      "source": [
        "### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "32bUGl6UVuAm"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lOAzf_7eVwvN"
      },
      "outputs": [],
      "source": [
        "dtree = DecisionTreeRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "M8WGvGCAXDG9",
        "outputId": "cec196df-a4d2-4491-9ed0-b1276f3f6ec9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "DecisionTreeRegressor()"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dtree.fit(X_train , y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMZW-o4nfB_k"
      },
      "outputs": [],
      "source": [
        "training_score = dtree.score(X_train, y_train)\n",
        "testing_score = dtree.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AjZRxlfEfB_k",
        "outputId": "2cd4c54b-8b72-4911-c59f-da19c9812a9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: Decision Tree\n",
            "Training Score: 1.0\n",
            "Testing Score: 0.5555600583900973\n"
          ]
        }
      ],
      "source": [
        "print(f\"Model: Decision Tree\")\n",
        "print(f\"Training Score: {training_score}\")\n",
        "print(f\"Testing Score: {testing_score}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CVDEOHAIXOhW"
      },
      "outputs": [],
      "source": [
        "training_data_prediction = dtree.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kzuVy_FaZDoq"
      },
      "outputs": [],
      "source": [
        "dtree_pred = dtree.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "a3YbZxs-ZFhm",
        "outputId": "229b78de-9acd-423d-b93f-ca15d0edc06d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.scatter(y_train, training_data_prediction)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Price vs Preicted Price\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnLGNv6LZHfe",
        "outputId": "8b24524c-ffa1-4708-e8f9-3c7ee416c177"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "R squared error :  1.0\n",
            "Mean Absolute Error :  0.0\n"
          ]
        }
      ],
      "source": [
        "# R squared error\n",
        "score_1 = metrics.r2_score(y_train, training_data_prediction)\n",
        "\n",
        "# Mean Absolute Error\n",
        "score_2 = metrics.mean_absolute_error(y_train, training_data_prediction)\n",
        "\n",
        "print(\"R squared error : \", score_1)\n",
        "print('Mean Absolute Error : ', score_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G8_mOYZ0ZMmA"
      },
      "outputs": [],
      "source": [
        "dtree_mae = metrics.mean_absolute_error(y_test,dtree_pred)\n",
        "dtree_mse = metrics.mean_squared_error(y_test,dtree_pred)\n",
        "dtree_r2 = metrics.r2_score(y_test,dtree_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tx6-VU03ZQ8h",
        "outputId": "0b6fb73d-fc50-48f8-e33c-e8258ec20d05"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE of decision tree model: 237260.40056474385\n",
            "MSE of decision tree model 187635026211.01453\n",
            "R2 score of decision tree model 0.5555600583900973\n"
          ]
        }
      ],
      "source": [
        "print(\"MAE of decision tree model:\" ,dtree_mae)\n",
        "print(\"MSE of decision tree model\",dtree_mse)\n",
        "print(\"R2 score of decision tree model\" , dtree_r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDsw6S9ZfB_l"
      },
      "source": [
        "### KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j5V1hKUPZUyx"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K0giyuPTZXAy"
      },
      "outputs": [],
      "source": [
        "knn = KNeighborsRegressor(n_neighbors=20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "eCCDfR1uZY2E",
        "outputId": "52e26032-7fb1-4f81-b90c-090e0a069683"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor(n_neighbors=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=20)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "KNeighborsRegressor(n_neighbors=20)"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4S-D_bJafB_l"
      },
      "outputs": [],
      "source": [
        "training_score = knn.score(X_train, y_train)\n",
        "testing_score = knn.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riY3BOQ_fB_l",
        "outputId": "f5fee63a-9b6c-42f5-b563-d17529ae5e6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: KNN\n",
            "Training Score: 0.06417601572783826\n",
            "Testing Score: -0.08767588364669687\n"
          ]
        }
      ],
      "source": [
        "print(f\"Model: KNN\")\n",
        "print(f\"Training Score: {training_score}\")\n",
        "print(f\"Testing Score: {testing_score}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oVUszJN1ZaYi"
      },
      "outputs": [],
      "source": [
        "knn_pred = knn.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9hnyxJNOZdla"
      },
      "outputs": [],
      "source": [
        "knn_mae = metrics.mean_absolute_error(y_test,knn_pred)\n",
        "knn_mse = metrics.mean_squared_error(y_test,knn_pred)\n",
        "knn_r2 = metrics.r2_score(y_test,knn_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgCpZR6RZgU6",
        "outputId": "edc74fc1-2ec5-44cd-9281-1ec7bd98e746"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE of KNN tree model: 551787.8553448971\n",
            "MSE of KNN tree model 459198361420.60425\n",
            "R2 score of KNN tree model -0.08767588364669687\n"
          ]
        }
      ],
      "source": [
        "print(\"MAE of KNN tree model:\" ,knn_mae)\n",
        "print(\"MSE of KNN tree model\",knn_mse)\n",
        "print(\"R2 score of KNN tree model\" , knn_r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0waVDu0XfB_m"
      },
      "source": [
        "### SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRHwSq1uZhwp"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "P4HjQfjDZldR",
        "outputId": "a365a0fc-b506-4983-d2e5-12303ae1e5d8"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVR()"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_reg = SVR(kernel='rbf')\n",
        "svm_reg.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DdURNpo0ZoLK",
        "outputId": "ac3c0ff8-7157-4938-b638-f399b34363e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "-0.07562023218237668"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_reg.score(X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fZmlm0GufB_m"
      },
      "outputs": [],
      "source": [
        "training_score = svm_reg.score(X_train, y_train)\n",
        "testing_score = svm_reg.score(X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XiYCoE0zfB_m",
        "outputId": "eac511b8-fca3-4605-e492-408fef3e9bf7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: SVM\n",
            "Training Score: -0.08001615983546428\n",
            "Testing Score: -0.07562023218237668\n"
          ]
        }
      ],
      "source": [
        "print(f\"Model: SVM\")\n",
        "print(f\"Training Score: {training_score}\")\n",
        "print(f\"Testing Score: {testing_score}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OxTxe6xxZ1GQ"
      },
      "outputs": [],
      "source": [
        "svm_reg_pred = svm_reg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "71ysSsMgaCAn"
      },
      "outputs": [],
      "source": [
        "svm_mae = metrics.mean_absolute_error(y_test,svm_reg_pred)\n",
        "svm_mse = metrics.mean_squared_error(y_test,svm_reg_pred)\n",
        "svm_r2 = metrics.r2_score(y_test,svm_reg_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anBmTpnJaiyt",
        "outputId": "a93ba642-cbd2-47ab-d9b6-761b4ded1544",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MAE of SVM model: 444162.57558421657\n",
            "MSE of SVM model: 454108669278.389\n",
            "R2 Score of SVM model :  -0.07562023218237668\n"
          ]
        }
      ],
      "source": [
        "print(\"MAE of SVM model:\",svm_mae)\n",
        "print(\"MSE of SVM model:\",svm_mse)\n",
        "print(\"R2 Score of SVM model : \",svm_r2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56PFtwIbr5tB"
      },
      "source": [
        "### Predicting Prices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IlbQVhp_fB_m",
        "outputId": "4ede0b32-6aa5-48ca-cd74-ebae719d7334"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Prediction</th>\n",
              "      <th>Actual</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8979</th>\n",
              "      <td>8.616668e+05</td>\n",
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              "    <tr>\n",
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              "      <td>1100000.0</td>\n",
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              "    <tr>\n",
              "      <th>7867</th>\n",
              "      <td>7.479998e+05</td>\n",
              "      <td>885000.0</td>\n",
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              "    <tr>\n",
              "      <th>4620</th>\n",
              "      <td>7.404563e+05</td>\n",
              "      <td>741000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5639</th>\n",
              "      <td>7.647054e+05</td>\n",
              "      <td>310000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8399</th>\n",
              "      <td>6.586276e+05</td>\n",
              "      <td>482000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4566</th>\n",
              "      <td>9.010332e+05</td>\n",
              "      <td>1260500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5498</th>\n",
              "      <td>9.940345e+05</td>\n",
              "      <td>965000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7985</th>\n",
              "      <td>5.139132e+05</td>\n",
              "      <td>630000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2479 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Prediction     Actual\n",
              "8979   8.616668e+05   905000.0\n",
              "1498   1.253201e+06  1100000.0\n",
              "10134  1.607717e+06  1655000.0\n",
              "7867   7.479998e+05   885000.0\n",
              "4620   7.404563e+05   741000.0\n",
              "...             ...        ...\n",
              "5639   7.647054e+05   310000.0\n",
              "8399   6.586276e+05   482000.0\n",
              "4566   9.010332e+05  1260500.0\n",
              "5498   9.940345e+05   965000.0\n",
              "7985   5.139132e+05   630000.0\n",
              "\n",
              "[2479 rows x 2 columns]"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = lr.predict(X_test)\n",
        "pd.DataFrame({\"Prediction\": y_pred, \"Actual\": y_test})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_4SmMO9zfB_m",
        "outputId": "2624e167-fa04-4425-90db-61b33cf85aaf"
      },
      "outputs": [
        {
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              "      <td>1085000.0</td>\n",
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              "      <td>1655000.0</td>\n",
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              "      <th>4620</th>\n",
              "      <td>716000.0</td>\n",
              "      <td>741000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5639</th>\n",
              "      <td>400000.0</td>\n",
              "      <td>310000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8399</th>\n",
              "      <td>430000.0</td>\n",
              "      <td>482000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4566</th>\n",
              "      <td>1100000.0</td>\n",
              "      <td>1260500.0</td>\n",
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              "      <th>5498</th>\n",
              "      <td>801000.0</td>\n",
              "      <td>965000.0</td>\n",
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              "    <tr>\n",
              "      <th>7985</th>\n",
              "      <td>565000.0</td>\n",
              "      <td>630000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2479 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Prediction     Actual\n",
              "8979     998000.0   905000.0\n",
              "1498    1085000.0  1100000.0\n",
              "10134   2538000.0  1655000.0\n",
              "7867     690500.0   885000.0\n",
              "4620     716000.0   741000.0\n",
              "...           ...        ...\n",
              "5639     400000.0   310000.0\n",
              "8399     430000.0   482000.0\n",
              "4566    1100000.0  1260500.0\n",
              "5498     801000.0   965000.0\n",
              "7985     565000.0   630000.0\n",
              "\n",
              "[2479 rows x 2 columns]"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = dtree.predict(X_test)\n",
        "pd.DataFrame({\"Prediction\": y_pred, \"Actual\": y_test})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MkXeqiB6fB_m",
        "outputId": "0c4ac2c1-be33-4fe4-a563-1ac959dc9a83"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
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              "      <th>Prediction</th>\n",
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              "      <td>905000.0</td>\n",
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              "      <td>1655000.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>5639</th>\n",
              "      <td>1452200.0</td>\n",
              "      <td>310000.0</td>\n",
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              "    <tr>\n",
              "      <th>8399</th>\n",
              "      <td>1359200.0</td>\n",
              "      <td>482000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4566</th>\n",
              "      <td>1464200.0</td>\n",
              "      <td>1260500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5498</th>\n",
              "      <td>1208000.0</td>\n",
              "      <td>965000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7985</th>\n",
              "      <td>1452200.0</td>\n",
              "      <td>630000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2479 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Prediction     Actual\n",
              "8979    1452200.0   905000.0\n",
              "1498    1464200.0  1100000.0\n",
              "10134   1559700.0  1655000.0\n",
              "7867    1107500.0   885000.0\n",
              "4620    1300450.0   741000.0\n",
              "...           ...        ...\n",
              "5639    1452200.0   310000.0\n",
              "8399    1359200.0   482000.0\n",
              "4566    1464200.0  1260500.0\n",
              "5498    1208000.0   965000.0\n",
              "7985    1452200.0   630000.0\n",
              "\n",
              "[2479 rows x 2 columns]"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = knn.predict(X_test)\n",
        "pd.DataFrame({\"Prediction\": y_pred, \"Actual\": y_test})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tKeYP9kIfB_n",
        "outputId": "f251d06a-823b-4127-ae7a-d3b033409fb6"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Prediction</th>\n",
              "      <th>Actual</th>\n",
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              "  <tbody>\n",
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              "      <th>...</th>\n",
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              "      <td>880015.426511</td>\n",
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              "      <th>8399</th>\n",
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              "      <th>7985</th>\n",
              "      <td>880014.936747</td>\n",
              "      <td>630000.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2479 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Prediction     Actual\n",
              "8979   880015.957738   905000.0\n",
              "1498   880017.920186  1100000.0\n",
              "10134  880020.253152  1655000.0\n",
              "7867   880015.367319   885000.0\n",
              "4620   880015.233609   741000.0\n",
              "...              ...        ...\n",
              "5639   880015.426511   310000.0\n",
              "8399   880015.326518   482000.0\n",
              "4566   880016.597560  1260500.0\n",
              "5498   880016.072772   965000.0\n",
              "7985   880014.936747   630000.0\n",
              "\n",
              "[2479 rows x 2 columns]"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_pred = svm_reg.predict(X_test)\n",
        "pd.DataFrame({\"Prediction\": y_pred, \"Actual\": y_test})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZJl4E3KMfB_n"
      },
      "outputs": [],
      "source": []
    }
  ],
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    "colab": {
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