Data_Preprocessing.ipynb

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    "colab": {
      "name": "Data_Preprocessing.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "id": "eIjyqpkUR369",
        "outputId": "365c15b7-43b0-406f-e266-d5c86d7dce38"
      },
      "source": [
        "pwd"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ny04MVCjR7K1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTJ3zWpPSFsv"
      },
      "source": [
        "df=pd.read_csv(\"/content/House_Price.csv\",header=0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "N4ZPPEURTAtO",
        "outputId": "bc37ffe8-07c2-466e-9066-89972389cad9"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>airport</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>waterbody</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>bus_ter</th>\n",
              "      <th>parks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.00632</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.35</td>\n",
              "      <td>3.81</td>\n",
              "      <td>4.18</td>\n",
              "      <td>4.01</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>YES</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.1920</td>\n",
              "      <td>River</td>\n",
              "      <td>23</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.049347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.02731</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.99</td>\n",
              "      <td>4.70</td>\n",
              "      <td>5.12</td>\n",
              "      <td>5.06</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.1728</td>\n",
              "      <td>Lake</td>\n",
              "      <td>42</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.046146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.02729</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>5.03</td>\n",
              "      <td>4.86</td>\n",
              "      <td>5.01</td>\n",
              "      <td>4.97</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.394</td>\n",
              "      <td>101.1200</td>\n",
              "      <td>None</td>\n",
              "      <td>38</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.045764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.03237</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.21</td>\n",
              "      <td>5.93</td>\n",
              "      <td>6.16</td>\n",
              "      <td>5.96</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>YES</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.2672</td>\n",
              "      <td>Lake</td>\n",
              "      <td>45</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.047151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.16</td>\n",
              "      <td>5.86</td>\n",
              "      <td>6.37</td>\n",
              "      <td>5.86</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>NO</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.2896</td>\n",
              "      <td>Lake</td>\n",
              "      <td>55</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.039474</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "0   24.0     0.00632       32.31  ...        23      YES  0.049347\n",
              "1   21.6     0.02731       37.07  ...        42      YES  0.046146\n",
              "2   34.7     0.02729       37.07  ...        38      YES  0.045764\n",
              "3   33.4     0.03237       32.18  ...        45      YES  0.047151\n",
              "4   36.2     0.06905       32.18  ...        55      YES  0.039474\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OrqYBQNvTMqz",
        "outputId": "c9652faa-d7b8-4011-f5aa-735e828d2dcb"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 19)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSCD_oSCTh09"
      },
      "source": [
        "#univariate analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "fI9HkGVrTTPN",
        "outputId": "7a56c836-241a-4475-ad33-70a5054ea23d"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>498.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.528854</td>\n",
              "      <td>3.613524</td>\n",
              "      <td>41.136779</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.971996</td>\n",
              "      <td>3.628775</td>\n",
              "      <td>3.960672</td>\n",
              "      <td>3.618972</td>\n",
              "      <td>21.544466</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>7.899767</td>\n",
              "      <td>13.041605</td>\n",
              "      <td>39.181818</td>\n",
              "      <td>0.054454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.182176</td>\n",
              "      <td>8.601545</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.108532</td>\n",
              "      <td>2.108580</td>\n",
              "      <td>2.119797</td>\n",
              "      <td>2.099203</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>1.476683</td>\n",
              "      <td>5.238957</td>\n",
              "      <td>12.513697</td>\n",
              "      <td>0.010632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.006320</td>\n",
              "      <td>30.460000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.130000</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.268000</td>\n",
              "      <td>10.057600</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.033292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.025000</td>\n",
              "      <td>0.082045</td>\n",
              "      <td>35.190000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.270000</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>2.232500</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>6.634500</td>\n",
              "      <td>11.189800</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.046464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.200000</td>\n",
              "      <td>0.256510</td>\n",
              "      <td>39.690000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.385000</td>\n",
              "      <td>3.010000</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>3.070000</td>\n",
              "      <td>20.950000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>7.999000</td>\n",
              "      <td>12.720000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.053507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>3.677082</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.367500</td>\n",
              "      <td>4.992500</td>\n",
              "      <td>5.407500</td>\n",
              "      <td>4.985000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>9.088000</td>\n",
              "      <td>14.170800</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.061397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>88.976200</td>\n",
              "      <td>57.740000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.930000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.940000</td>\n",
              "      <td>27.400000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>10.876000</td>\n",
              "      <td>101.120000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.086711</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price  crime_rate  resid_area  ...  n_hot_rooms    rainfall       parks\n",
              "count  506.000000  506.000000  506.000000  ...   506.000000  506.000000  506.000000\n",
              "mean    22.528854    3.613524   41.136779  ...    13.041605   39.181818    0.054454\n",
              "std      9.182176    8.601545    6.860353  ...     5.238957   12.513697    0.010632\n",
              "min      5.000000    0.006320   30.460000  ...    10.057600    3.000000    0.033292\n",
              "25%     17.025000    0.082045   35.190000  ...    11.189800   28.000000    0.046464\n",
              "50%     21.200000    0.256510   39.690000  ...    12.720000   39.000000    0.053507\n",
              "75%     25.000000    3.677082   48.100000  ...    14.170800   50.000000    0.061397\n",
              "max     50.000000   88.976200   57.740000  ...   101.120000   60.000000    0.086711\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rr2rK-r0UN1K"
      },
      "source": [
        "#2 important things we should use for EDD\n",
        "#count\n",
        "#difference between mean and median\n",
        "#a negatively skewed (also known as left-skewed) distribution is a type of distribution \n",
        "#in which more values are concentrated on the right side (tail) \n",
        "#of the distribution graph while the left tail of the distribution graph is longer.\n",
        "#Now we need to plot scatter plots to understand them completely."
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "CIxuXNI9VvxA",
        "outputId": "30990d76-c9d0-4729-992e-73667ec6e13a"
      },
      "source": [
        "sns.jointplot(x='n_hot_rooms',y='price',data=df)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fefdced5090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "iGP5MLzaWSDH",
        "outputId": "a15d16d1-6967-47c8-e34a-8a5f8ac20416"
      },
      "source": [
        "sns.jointplot(x='rainfall',y='price',data=df)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fefd3903f50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5C7ftkFdWqoP"
      },
      "source": [
        "For categorical variables we need to plot a bar plot or count plot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "crhAbaiXWZkg",
        "outputId": "4e126201-f37c-40ea-ec50-e6f8db0e938b"
      },
      "source": [
        "sns.countplot(x='airport',data=df)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefd33328d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZPxIxMqoW-5M"
      },
      "source": [
        "Nothing unusual about this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "YBsgprsJW5QU",
        "outputId": "d8170e60-9832-4d7c-d957-3650fb7af847"
      },
      "source": [
        "sns.countplot(x='bus_ter',data=df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fefd32f3150>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PnNXWHgjcs4N1a4HXV9ULAJKsAe6sql9pD965NMnn29pjgadU1f/ukd9GGpNHCtL8bqmqS1v7n4DnPMDaS4F3JflT4KCq2jrmz3g+cEb7rqTLgEOBo9rcVw0ETYKhIM1v+y8FK2Ar9/2bOaBPVJ0N/CHwSOb+2n/ymD8jwKuq6pi2HVlV244UfrD7pUu7z1CQ5vcLSZ7V2i8DvgzcBDyjjfXnISR5YntWwluBy4EdhcJdwOj1hs8Bf9Se4UySX2xPc5MmxlCQ5vdN4KwkNwAHA+cBbwLOTTID3Duy9jVJrktyDfB/wL/vYJ/XAPcmuTrJa4H3MXch+WvtFtj34nU+TZhfnS1J6jxSkCR1HqpKe1iSpwIf2m74x1X1zEnUI+0KTx9JkjpPH0mSOkNBktQZCpKkzlCQJHX/DyG4uUGglo+iAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzCscWhYXPR2"
      },
      "source": [
        "This is unusual as it is taking only one value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYos5Bh0XN26"
      },
      "source": [
        "#observations:\n",
        "#missing values in n_hos_beds\n",
        "#skewness or outliers in crime rate\n",
        "#outliers in n_hot_rooms and rainfall\n",
        "#bus terminal has only Y\n",
        "#Note :when data has outliers than there is big difference between mean and median.\n",
        "#Also standard deviation in such cases is higher"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KDgQv8E2e4fa",
        "outputId": "308e5b08-4e4e-411b-c981-a1279ea1b11c"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 19 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   price        506 non-null    float64\n",
            " 1   crime_rate   506 non-null    float64\n",
            " 2   resid_area   506 non-null    float64\n",
            " 3   air_qual     506 non-null    float64\n",
            " 4   room_num     506 non-null    float64\n",
            " 5   age          506 non-null    float64\n",
            " 6   dist1        506 non-null    float64\n",
            " 7   dist2        506 non-null    float64\n",
            " 8   dist3        506 non-null    float64\n",
            " 9   dist4        506 non-null    float64\n",
            " 10  teachers     506 non-null    float64\n",
            " 11  poor_prop    506 non-null    float64\n",
            " 12  airport      506 non-null    object \n",
            " 13  n_hos_beds   498 non-null    float64\n",
            " 14  n_hot_rooms  506 non-null    float64\n",
            " 15  waterbody    506 non-null    object \n",
            " 16  rainfall     506 non-null    int64  \n",
            " 17  bus_ter      506 non-null    object \n",
            " 18  parks        506 non-null    float64\n",
            "dtypes: float64(15), int64(1), object(3)\n",
            "memory usage: 75.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ohaZvu6e5oU",
        "outputId": "172337bf-0368-4ddc-94fc-1ff407edab14"
      },
      "source": [
        "np.percentile(df.n_hot_rooms,[99])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15.39952])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XQiywxcJfdRA",
        "outputId": "a6b10045-5f9e-43bd-b6bb-1ed8ea3f1ee0"
      },
      "source": [
        "np.percentile(df.n_hot_rooms,[99])[0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.399519999999999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOkQqG_Jfzr_"
      },
      "source": [
        "uv=np.percentile(df.n_hot_rooms,[99])[0]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "WvlqUGCKf2i-",
        "outputId": "da60557d-ca67-46f7-f0ce-d12b1ad3a34c"
      },
      "source": [
        "df[(df.n_hot_rooms>uv)]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>airport</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>waterbody</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>bus_ter</th>\n",
              "      <th>parks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.02729</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>5.03</td>\n",
              "      <td>4.86</td>\n",
              "      <td>5.01</td>\n",
              "      <td>4.97</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.394</td>\n",
              "      <td>101.12</td>\n",
              "      <td>None</td>\n",
              "      <td>38</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.045764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>50.0</td>\n",
              "      <td>2.01019</td>\n",
              "      <td>49.58</td>\n",
              "      <td>0.6050</td>\n",
              "      <td>7.929</td>\n",
              "      <td>96.2</td>\n",
              "      <td>2.11</td>\n",
              "      <td>1.91</td>\n",
              "      <td>2.31</td>\n",
              "      <td>1.86</td>\n",
              "      <td>25.3</td>\n",
              "      <td>3.70</td>\n",
              "      <td>YES</td>\n",
              "      <td>8.300</td>\n",
              "      <td>15.40</td>\n",
              "      <td>River</td>\n",
              "      <td>37</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.052105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.02009</td>\n",
              "      <td>32.68</td>\n",
              "      <td>0.4161</td>\n",
              "      <td>8.034</td>\n",
              "      <td>31.9</td>\n",
              "      <td>5.41</td>\n",
              "      <td>4.80</td>\n",
              "      <td>5.28</td>\n",
              "      <td>4.99</td>\n",
              "      <td>25.3</td>\n",
              "      <td>2.88</td>\n",
              "      <td>YES</td>\n",
              "      <td>8.900</td>\n",
              "      <td>15.40</td>\n",
              "      <td>River</td>\n",
              "      <td>51</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.044178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.57834</td>\n",
              "      <td>33.97</td>\n",
              "      <td>0.5750</td>\n",
              "      <td>8.297</td>\n",
              "      <td>67.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.13</td>\n",
              "      <td>2.43</td>\n",
              "      <td>2.52</td>\n",
              "      <td>27.0</td>\n",
              "      <td>7.44</td>\n",
              "      <td>YES</td>\n",
              "      <td>8.000</td>\n",
              "      <td>15.40</td>\n",
              "      <td>None</td>\n",
              "      <td>42</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.063235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>50.0</td>\n",
              "      <td>5.66998</td>\n",
              "      <td>48.10</td>\n",
              "      <td>0.6310</td>\n",
              "      <td>6.683</td>\n",
              "      <td>96.8</td>\n",
              "      <td>1.55</td>\n",
              "      <td>1.28</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.94</td>\n",
              "      <td>19.8</td>\n",
              "      <td>3.73</td>\n",
              "      <td>YES</td>\n",
              "      <td>6.700</td>\n",
              "      <td>15.40</td>\n",
              "      <td>River</td>\n",
              "      <td>58</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.067477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>13.4</td>\n",
              "      <td>7.05042</td>\n",
              "      <td>48.10</td>\n",
              "      <td>0.6140</td>\n",
              "      <td>6.103</td>\n",
              "      <td>85.1</td>\n",
              "      <td>2.08</td>\n",
              "      <td>1.80</td>\n",
              "      <td>2.34</td>\n",
              "      <td>1.87</td>\n",
              "      <td>19.8</td>\n",
              "      <td>23.29</td>\n",
              "      <td>NO</td>\n",
              "      <td>8.268</td>\n",
              "      <td>81.12</td>\n",
              "      <td>Lake</td>\n",
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              "      <td>YES</td>\n",
              "      <td>0.063344</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "2     34.7     0.02729       37.07  ...        38      YES  0.045764\n",
              "166   50.0     2.01019       49.58  ...        37      YES  0.052105\n",
              "204   50.0     0.02009       32.68  ...        51      YES  0.044178\n",
              "267   50.0     0.57834       33.97  ...        42      YES  0.063235\n",
              "369   50.0     5.66998       48.10  ...        58      YES  0.067477\n",
              "423   13.4     7.05042       48.10  ...        29      YES  0.063344\n",
              "\n",
              "[6 rows x 19 columns]"
            ]
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          "metadata": {
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          "execution_count": 19
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        "outputId": "1881613c-0d7a-41fb-aa2a-e14b2f79d6fd"
      },
      "source": [
        "df.n_hot_rooms[(df.n_hot_rooms>3*uv)]=3*uv"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
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        }
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      "source": [
        "df[(df.n_hot_rooms>uv)]"
      ],
      "execution_count": 22,
      "outputs": [
        {
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          "data": {
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              "      <td>4.03</td>\n",
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              "      <td>2.11</td>\n",
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              "      <td>2.31</td>\n",
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              "      <td>25.3</td>\n",
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              "      <td>0.4161</td>\n",
              "      <td>8.034</td>\n",
              "      <td>31.9</td>\n",
              "      <td>5.41</td>\n",
              "      <td>4.80</td>\n",
              "      <td>5.28</td>\n",
              "      <td>4.99</td>\n",
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              "      <td>8.900</td>\n",
              "      <td>15.40000</td>\n",
              "      <td>River</td>\n",
              "      <td>51</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.044178</td>\n",
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              "    <tr>\n",
              "      <th>267</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.57834</td>\n",
              "      <td>33.97</td>\n",
              "      <td>0.5750</td>\n",
              "      <td>8.297</td>\n",
              "      <td>67.0</td>\n",
              "      <td>2.60</td>\n",
              "      <td>2.13</td>\n",
              "      <td>2.43</td>\n",
              "      <td>2.52</td>\n",
              "      <td>27.0</td>\n",
              "      <td>7.44</td>\n",
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              "      <td>15.40000</td>\n",
              "      <td>None</td>\n",
              "      <td>42</td>\n",
              "      <td>YES</td>\n",
              "      <td>0.063235</td>\n",
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              "      <td>1.55</td>\n",
              "      <td>1.28</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.94</td>\n",
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              "      <td>YES</td>\n",
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              "      <td>1.87</td>\n",
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              "     price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "2     34.7     0.02729       37.07  ...        38      YES  0.045764\n",
              "166   50.0     2.01019       49.58  ...        37      YES  0.052105\n",
              "204   50.0     0.02009       32.68  ...        51      YES  0.044178\n",
              "267   50.0     0.57834       33.97  ...        42      YES  0.063235\n",
              "369   50.0     5.66998       48.10  ...        58      YES  0.067477\n",
              "423   13.4     7.05042       48.10  ...        29      YES  0.063344\n",
              "\n",
              "[6 rows x 19 columns]"
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        "id": "UuXTU6FJhCSO",
        "outputId": "bd1d1085-997d-4766-b52f-6ece1077721b"
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      "source": [
        "np.percentile(df.rainfall,[1])[0]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20.0"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 23
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      "metadata": {
        "id": "qjyxU5PVhfV7"
      },
      "source": [
        "lv=np.percentile(df.rainfall,[1])[0]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
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        "id": "YaM9sySHhhhf",
        "outputId": "dbbc38fd-13d6-480a-8ea2-c8c6354c5c79"
      },
      "source": [
        "df[(df.rainfall<lv)]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "213   28.1     0.14052       40.59  ...         3      YES  0.044019\n",
              "\n",
              "[1 rows x 19 columns]"
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      },
      "source": [
        "df.rainfall[df.rainfall<0.3*lv]=0.3*lv"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
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      "source": [
        "df[(df.rainfall<lv)]"
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      "execution_count": 27,
      "outputs": [
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          "data": {
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              "     price  crime_rate  resid_area  ...  rainfall  bus_ter     parks\n",
              "213   28.1     0.14052       40.59  ...         6      YES  0.044019\n",
              "\n",
              "[1 rows x 19 columns]"
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        "sns.jointplot(x='crime_rate',y='price',data=df)"
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      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fefce944fd0>"
            ]
          },
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          "execution_count": 31
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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "cLxF0fVUiBEq",
        "outputId": "7ea2e7d9-d565-472d-84e1-8270d8294dbf"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>498.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.528854</td>\n",
              "      <td>3.613524</td>\n",
              "      <td>41.136779</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.971996</td>\n",
              "      <td>3.628775</td>\n",
              "      <td>3.960672</td>\n",
              "      <td>3.618972</td>\n",
              "      <td>21.544466</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>7.899767</td>\n",
              "      <td>12.864050</td>\n",
              "      <td>39.187747</td>\n",
              "      <td>0.054454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.182176</td>\n",
              "      <td>8.601545</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.108532</td>\n",
              "      <td>2.108580</td>\n",
              "      <td>2.119797</td>\n",
              "      <td>2.099203</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>1.476683</td>\n",
              "      <td>2.688778</td>\n",
              "      <td>12.497221</td>\n",
              "      <td>0.010632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.006320</td>\n",
              "      <td>30.460000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.130000</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.268000</td>\n",
              "      <td>10.057600</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.033292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.025000</td>\n",
              "      <td>0.082045</td>\n",
              "      <td>35.190000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.270000</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>2.232500</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>6.634500</td>\n",
              "      <td>11.189800</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.046464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.200000</td>\n",
              "      <td>0.256510</td>\n",
              "      <td>39.690000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.385000</td>\n",
              "      <td>3.010000</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>3.070000</td>\n",
              "      <td>20.950000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>7.999000</td>\n",
              "      <td>12.720000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.053507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>3.677082</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.367500</td>\n",
              "      <td>4.992500</td>\n",
              "      <td>5.407500</td>\n",
              "      <td>4.985000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>9.088000</td>\n",
              "      <td>14.170800</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.061397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>88.976200</td>\n",
              "      <td>57.740000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.930000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.940000</td>\n",
              "      <td>27.400000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>10.876000</td>\n",
              "      <td>46.198560</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.086711</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price  crime_rate  resid_area  ...  n_hot_rooms    rainfall       parks\n",
              "count  506.000000  506.000000  506.000000  ...   506.000000  506.000000  506.000000\n",
              "mean    22.528854    3.613524   41.136779  ...    12.864050   39.187747    0.054454\n",
              "std      9.182176    8.601545    6.860353  ...     2.688778   12.497221    0.010632\n",
              "min      5.000000    0.006320   30.460000  ...    10.057600    6.000000    0.033292\n",
              "25%     17.025000    0.082045   35.190000  ...    11.189800   28.000000    0.046464\n",
              "50%     21.200000    0.256510   39.690000  ...    12.720000   39.000000    0.053507\n",
              "75%     25.000000    3.677082   48.100000  ...    14.170800   50.000000    0.061397\n",
              "max     50.000000   88.976200   57.740000  ...    46.198560   60.000000    0.086711\n",
              "\n",
              "[8 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFKfWvx5jux8"
      },
      "source": [
        "Missing value "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDaR4mfTijHZ",
        "outputId": "e787bd48-c970-4957-cc8b-4ed0851edc07"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 19 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   price        506 non-null    float64\n",
            " 1   crime_rate   506 non-null    float64\n",
            " 2   resid_area   506 non-null    float64\n",
            " 3   air_qual     506 non-null    float64\n",
            " 4   room_num     506 non-null    float64\n",
            " 5   age          506 non-null    float64\n",
            " 6   dist1        506 non-null    float64\n",
            " 7   dist2        506 non-null    float64\n",
            " 8   dist3        506 non-null    float64\n",
            " 9   dist4        506 non-null    float64\n",
            " 10  teachers     506 non-null    float64\n",
            " 11  poor_prop    506 non-null    float64\n",
            " 12  airport      506 non-null    object \n",
            " 13  n_hos_beds   498 non-null    float64\n",
            " 14  n_hot_rooms  506 non-null    float64\n",
            " 15  waterbody    506 non-null    object \n",
            " 16  rainfall     506 non-null    int64  \n",
            " 17  bus_ter      506 non-null    object \n",
            " 18  parks        506 non-null    float64\n",
            "dtypes: float64(15), int64(1), object(3)\n",
            "memory usage: 75.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgSSP7pcjzpE"
      },
      "source": [
        "df.n_hos_beds=df.n_hos_beds.fillna(df.n_hos_beds.mean())"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QhNTOVnvkOT7",
        "outputId": "4ecbd997-0b60-4daa-9481-d75ff5ef28a6"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 19 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   price        506 non-null    float64\n",
            " 1   crime_rate   506 non-null    float64\n",
            " 2   resid_area   506 non-null    float64\n",
            " 3   air_qual     506 non-null    float64\n",
            " 4   room_num     506 non-null    float64\n",
            " 5   age          506 non-null    float64\n",
            " 6   dist1        506 non-null    float64\n",
            " 7   dist2        506 non-null    float64\n",
            " 8   dist3        506 non-null    float64\n",
            " 9   dist4        506 non-null    float64\n",
            " 10  teachers     506 non-null    float64\n",
            " 11  poor_prop    506 non-null    float64\n",
            " 12  airport      506 non-null    object \n",
            " 13  n_hos_beds   506 non-null    float64\n",
            " 14  n_hot_rooms  506 non-null    float64\n",
            " 15  waterbody    506 non-null    object \n",
            " 16  rainfall     506 non-null    int64  \n",
            " 17  bus_ter      506 non-null    object \n",
            " 18  parks        506 non-null    float64\n",
            "dtypes: float64(15), int64(1), object(3)\n",
            "memory usage: 75.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eaaq2klilZQ_"
      },
      "source": [
        "Bivariate analysis:\n",
        "scatter plot,\n",
        "corelation matrix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "OEwM2bwKkgOS",
        "outputId": "4f12a2ea-0d1c-48f7-fb8a-8adcbadb9954"
      },
      "source": [
        "sns.jointplot(x=\"crime_rate\",y='price',data=df)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fefce530910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "19rh2mspp3tc"
      },
      "source": [
        "df.crime_rate=np.log(1+df.crime_rate)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "6R-M0FsPqE5a",
        "outputId": "772f0fc9-955c-471e-a4a8-c4abd09a5fce"
      },
      "source": [
        "sns.jointplot(x=\"crime_rate\",y='price',data=df)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7fefce22ea10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqwBb6BvqHvD"
      },
      "source": [
        "#above is another way to treat outliers"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tre03y_UqSsI"
      },
      "source": [
        "df['avg_dist']=(df.dist1+df.dist2+df.dist3+df.dist4)/4"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "mKGDIzRKqjrC",
        "outputId": "fae72fe7-eb57-4e56-948a-717f8fb38b4b"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>dist1</th>\n",
              "      <th>dist2</th>\n",
              "      <th>dist3</th>\n",
              "      <th>dist4</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.528854</td>\n",
              "      <td>0.813418</td>\n",
              "      <td>41.136779</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>3.971996</td>\n",
              "      <td>3.628775</td>\n",
              "      <td>3.960672</td>\n",
              "      <td>3.618972</td>\n",
              "      <td>21.544466</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>7.899767</td>\n",
              "      <td>12.864050</td>\n",
              "      <td>39.187747</td>\n",
              "      <td>0.054454</td>\n",
              "      <td>3.795104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.182176</td>\n",
              "      <td>1.022731</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.108532</td>\n",
              "      <td>2.108580</td>\n",
              "      <td>2.119797</td>\n",
              "      <td>2.099203</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>1.464939</td>\n",
              "      <td>2.688778</td>\n",
              "      <td>12.497221</td>\n",
              "      <td>0.010632</td>\n",
              "      <td>2.105859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>30.460000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.130000</td>\n",
              "      <td>0.920000</td>\n",
              "      <td>1.150000</td>\n",
              "      <td>0.730000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.268000</td>\n",
              "      <td>10.057600</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.033292</td>\n",
              "      <td>1.127500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.025000</td>\n",
              "      <td>0.078853</td>\n",
              "      <td>35.190000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>2.270000</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>2.232500</td>\n",
              "      <td>1.940000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>6.659000</td>\n",
              "      <td>11.189800</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.046464</td>\n",
              "      <td>2.100625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.200000</td>\n",
              "      <td>0.228336</td>\n",
              "      <td>39.690000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>3.385000</td>\n",
              "      <td>3.010000</td>\n",
              "      <td>3.375000</td>\n",
              "      <td>3.070000</td>\n",
              "      <td>20.950000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>7.963000</td>\n",
              "      <td>12.720000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.053507</td>\n",
              "      <td>3.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.542674</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>5.367500</td>\n",
              "      <td>4.992500</td>\n",
              "      <td>5.407500</td>\n",
              "      <td>4.985000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>9.076000</td>\n",
              "      <td>14.170800</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.061397</td>\n",
              "      <td>5.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>4.499545</td>\n",
              "      <td>57.740000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.930000</td>\n",
              "      <td>12.320000</td>\n",
              "      <td>11.940000</td>\n",
              "      <td>27.400000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>10.876000</td>\n",
              "      <td>46.198560</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.086711</td>\n",
              "      <td>12.127500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price  crime_rate  resid_area  ...    rainfall       parks    avg_dist\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean    22.528854    0.813418   41.136779  ...   39.187747    0.054454    3.795104\n",
              "std      9.182176    1.022731    6.860353  ...   12.497221    0.010632    2.105859\n",
              "min      5.000000    0.006300   30.460000  ...    6.000000    0.033292    1.127500\n",
              "25%     17.025000    0.078853   35.190000  ...   28.000000    0.046464    2.100625\n",
              "50%     21.200000    0.228336   39.690000  ...   39.000000    0.053507    3.207500\n",
              "75%     25.000000    1.542674   48.100000  ...   50.000000    0.061397    5.187500\n",
              "max     50.000000    4.499545   57.740000  ...   60.000000    0.086711   12.127500\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJP7jH9rqpCK"
      },
      "source": [
        "del df['dist1']\n",
        "del df['dist2']\n",
        "del df['dist3']\n",
        "del df['dist4']\n"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "QgZgB5QRq60S",
        "outputId": "4f2ea6c4-4383-4cdc-e800-92e100e30ab9"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>22.528854</td>\n",
              "      <td>0.813418</td>\n",
              "      <td>41.136779</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.574901</td>\n",
              "      <td>21.544466</td>\n",
              "      <td>12.653063</td>\n",
              "      <td>7.899767</td>\n",
              "      <td>12.864050</td>\n",
              "      <td>39.187747</td>\n",
              "      <td>0.054454</td>\n",
              "      <td>3.795104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.182176</td>\n",
              "      <td>1.022731</td>\n",
              "      <td>6.860353</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>28.148861</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>7.141062</td>\n",
              "      <td>1.464939</td>\n",
              "      <td>2.688778</td>\n",
              "      <td>12.497221</td>\n",
              "      <td>0.010632</td>\n",
              "      <td>2.105859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>30.460000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.268000</td>\n",
              "      <td>10.057600</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.033292</td>\n",
              "      <td>1.127500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.025000</td>\n",
              "      <td>0.078853</td>\n",
              "      <td>35.190000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.025000</td>\n",
              "      <td>19.800000</td>\n",
              "      <td>6.950000</td>\n",
              "      <td>6.659000</td>\n",
              "      <td>11.189800</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.046464</td>\n",
              "      <td>2.100625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>21.200000</td>\n",
              "      <td>0.228336</td>\n",
              "      <td>39.690000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>77.500000</td>\n",
              "      <td>20.950000</td>\n",
              "      <td>11.360000</td>\n",
              "      <td>7.963000</td>\n",
              "      <td>12.720000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>0.053507</td>\n",
              "      <td>3.207500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.542674</td>\n",
              "      <td>48.100000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>94.075000</td>\n",
              "      <td>22.600000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>9.076000</td>\n",
              "      <td>14.170800</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.061397</td>\n",
              "      <td>5.187500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>50.000000</td>\n",
              "      <td>4.499545</td>\n",
              "      <td>57.740000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.400000</td>\n",
              "      <td>37.970000</td>\n",
              "      <td>10.876000</td>\n",
              "      <td>46.198560</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>0.086711</td>\n",
              "      <td>12.127500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            price  crime_rate  resid_area  ...    rainfall       parks    avg_dist\n",
              "count  506.000000  506.000000  506.000000  ...  506.000000  506.000000  506.000000\n",
              "mean    22.528854    0.813418   41.136779  ...   39.187747    0.054454    3.795104\n",
              "std      9.182176    1.022731    6.860353  ...   12.497221    0.010632    2.105859\n",
              "min      5.000000    0.006300   30.460000  ...    6.000000    0.033292    1.127500\n",
              "25%     17.025000    0.078853   35.190000  ...   28.000000    0.046464    2.100625\n",
              "50%     21.200000    0.228336   39.690000  ...   39.000000    0.053507    3.207500\n",
              "75%     25.000000    1.542674   48.100000  ...   50.000000    0.061397    5.187500\n",
              "max     50.000000    4.499545   57.740000  ...   60.000000    0.086711   12.127500\n",
              "\n",
              "[8 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_v73uE0dq_hr"
      },
      "source": [
        "del df['bus_ter']"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "jmRbHlC1rRY0",
        "outputId": "7a6cd38d-0654-4100-e472-a1dd26c5dfb2"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>airport</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>waterbody</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>YES</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.19200</td>\n",
              "      <td>River</td>\n",
              "      <td>23</td>\n",
              "      <td>0.049347</td>\n",
              "      <td>4.0875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.026944</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.17280</td>\n",
              "      <td>Lake</td>\n",
              "      <td>42</td>\n",
              "      <td>0.046146</td>\n",
              "      <td>4.9675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.026924</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>NO</td>\n",
              "      <td>7.394</td>\n",
              "      <td>46.19856</td>\n",
              "      <td>None</td>\n",
              "      <td>38</td>\n",
              "      <td>0.045764</td>\n",
              "      <td>4.9675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.031857</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>YES</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.26720</td>\n",
              "      <td>Lake</td>\n",
              "      <td>45</td>\n",
              "      <td>0.047151</td>\n",
              "      <td>6.0650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.066770</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>NO</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.28960</td>\n",
              "      <td>Lake</td>\n",
              "      <td>55</td>\n",
              "      <td>0.039474</td>\n",
              "      <td>6.0625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  resid_area  ...  rainfall     parks  avg_dist\n",
              "0   24.0    0.006300       32.31  ...        23  0.049347    4.0875\n",
              "1   21.6    0.026944       37.07  ...        42  0.046146    4.9675\n",
              "2   34.7    0.026924       37.07  ...        38  0.045764    4.9675\n",
              "3   33.4    0.031857       32.18  ...        45  0.047151    6.0650\n",
              "4   36.2    0.066770       32.18  ...        55  0.039474    6.0625\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzl1MqE4slgp"
      },
      "source": [
        "Handling categorical values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIUjKx4FrSvn"
      },
      "source": [
        "df=pd.get_dummies(df)"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "7j2M3SzTsve7",
        "outputId": "25cd335c-3571-41af-9d44-1088ff2de65b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "      <th>airport_NO</th>\n",
              "      <th>airport_YES</th>\n",
              "      <th>waterbody_Lake</th>\n",
              "      <th>waterbody_Lake and River</th>\n",
              "      <th>waterbody_None</th>\n",
              "      <th>waterbody_River</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.19200</td>\n",
              "      <td>23</td>\n",
              "      <td>0.049347</td>\n",
              "      <td>4.0875</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.026944</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.17280</td>\n",
              "      <td>42</td>\n",
              "      <td>0.046146</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.026924</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>7.394</td>\n",
              "      <td>46.19856</td>\n",
              "      <td>38</td>\n",
              "      <td>0.045764</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.031857</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.26720</td>\n",
              "      <td>45</td>\n",
              "      <td>0.047151</td>\n",
              "      <td>6.0650</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.066770</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.28960</td>\n",
              "      <td>55</td>\n",
              "      <td>0.039474</td>\n",
              "      <td>6.0625</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  ...  waterbody_None  waterbody_River\n",
              "0   24.0    0.006300  ...               0                1\n",
              "1   21.6    0.026944  ...               0                0\n",
              "2   34.7    0.026924  ...               1                0\n",
              "3   33.4    0.031857  ...               0                0\n",
              "4   36.2    0.066770  ...               0                0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9UhxFX9sx3B"
      },
      "source": [
        "del df['airport_NO']"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UZoG9oCtWBE"
      },
      "source": [
        "del df['waterbody_None']"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "vqwABNPnteD4",
        "outputId": "1926d0d8-1906-43d6-a5b0-6866f10638dc"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "      <th>airport_YES</th>\n",
              "      <th>waterbody_Lake</th>\n",
              "      <th>waterbody_Lake and River</th>\n",
              "      <th>waterbody_River</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.19200</td>\n",
              "      <td>23</td>\n",
              "      <td>0.049347</td>\n",
              "      <td>4.0875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.026944</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.17280</td>\n",
              "      <td>42</td>\n",
              "      <td>0.046146</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.026924</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>7.394</td>\n",
              "      <td>46.19856</td>\n",
              "      <td>38</td>\n",
              "      <td>0.045764</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.031857</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.26720</td>\n",
              "      <td>45</td>\n",
              "      <td>0.047151</td>\n",
              "      <td>6.0650</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.066770</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.28960</td>\n",
              "      <td>55</td>\n",
              "      <td>0.039474</td>\n",
              "      <td>6.0625</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  ...  waterbody_Lake and River  waterbody_River\n",
              "0   24.0    0.006300  ...                         0                1\n",
              "1   21.6    0.026944  ...                         0                0\n",
              "2   34.7    0.026924  ...                         0                0\n",
              "3   33.4    0.031857  ...                         0                0\n",
              "4   36.2    0.066770  ...                         0                0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "id": "-K2YwMkvtiWw",
        "outputId": "94c56a60-ca01-4ff5-a379-70d2f9bbc2e2"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>parks</th>\n",
              "      <th>avg_dist</th>\n",
              "      <th>airport_YES</th>\n",
              "      <th>waterbody_Lake</th>\n",
              "      <th>waterbody_Lake and River</th>\n",
              "      <th>waterbody_River</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>price</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.466527</td>\n",
              "      <td>-0.484754</td>\n",
              "      <td>-0.429300</td>\n",
              "      <td>0.696304</td>\n",
              "      <td>-0.377999</td>\n",
              "      <td>0.505655</td>\n",
              "      <td>-0.740836</td>\n",
              "      <td>0.108880</td>\n",
              "      <td>0.017007</td>\n",
              "      <td>-0.047200</td>\n",
              "      <td>-0.391574</td>\n",
              "      <td>0.249289</td>\n",
              "      <td>0.182867</td>\n",
              "      <td>0.036233</td>\n",
              "      <td>-0.037497</td>\n",
              "      <td>0.071751</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>crime_rate</th>\n",
              "      <td>-0.466527</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.660283</td>\n",
              "      <td>0.707587</td>\n",
              "      <td>-0.288784</td>\n",
              "      <td>0.559591</td>\n",
              "      <td>-0.390052</td>\n",
              "      <td>0.608970</td>\n",
              "      <td>-0.004089</td>\n",
              "      <td>0.056570</td>\n",
              "      <td>0.082151</td>\n",
              "      <td>0.638951</td>\n",
              "      <td>-0.586371</td>\n",
              "      <td>-0.134486</td>\n",
              "      <td>-0.025390</td>\n",
              "      <td>0.009076</td>\n",
              "      <td>-0.060099</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>resid_area</th>\n",
              "      <td>-0.484754</td>\n",
              "      <td>0.660283</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763651</td>\n",
              "      <td>-0.391676</td>\n",
              "      <td>0.644779</td>\n",
              "      <td>-0.383248</td>\n",
              "      <td>0.603800</td>\n",
              "      <td>0.005799</td>\n",
              "      <td>-0.003761</td>\n",
              "      <td>0.055845</td>\n",
              "      <td>0.707635</td>\n",
              "      <td>-0.708022</td>\n",
              "      <td>-0.115401</td>\n",
              "      <td>-0.026590</td>\n",
              "      <td>0.051649</td>\n",
              "      <td>-0.098976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>air_qual</th>\n",
              "      <td>-0.429300</td>\n",
              "      <td>0.707587</td>\n",
              "      <td>0.763651</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>0.731470</td>\n",
              "      <td>-0.188933</td>\n",
              "      <td>0.590879</td>\n",
              "      <td>-0.049553</td>\n",
              "      <td>0.007238</td>\n",
              "      <td>0.091956</td>\n",
              "      <td>0.915544</td>\n",
              "      <td>-0.769247</td>\n",
              "      <td>-0.073903</td>\n",
              "      <td>-0.046393</td>\n",
              "      <td>0.013849</td>\n",
              "      <td>-0.037772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>room_num</th>\n",
              "      <td>0.696304</td>\n",
              "      <td>-0.288784</td>\n",
              "      <td>-0.391676</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.240265</td>\n",
              "      <td>0.355501</td>\n",
              "      <td>-0.613808</td>\n",
              "      <td>0.032009</td>\n",
              "      <td>0.014583</td>\n",
              "      <td>-0.064718</td>\n",
              "      <td>-0.282817</td>\n",
              "      <td>0.205241</td>\n",
              "      <td>0.163774</td>\n",
              "      <td>-0.004195</td>\n",
              "      <td>0.010554</td>\n",
              "      <td>0.046251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.377999</td>\n",
              "      <td>0.559591</td>\n",
              "      <td>0.644779</td>\n",
              "      <td>0.731470</td>\n",
              "      <td>-0.240265</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.261515</td>\n",
              "      <td>0.602339</td>\n",
              "      <td>-0.021012</td>\n",
              "      <td>0.013918</td>\n",
              "      <td>0.074684</td>\n",
              "      <td>0.673850</td>\n",
              "      <td>-0.747906</td>\n",
              "      <td>0.005101</td>\n",
              "      <td>0.003452</td>\n",
              "      <td>-0.004354</td>\n",
              "      <td>-0.088609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>teachers</th>\n",
              "      <td>0.505655</td>\n",
              "      <td>-0.390052</td>\n",
              "      <td>-0.383248</td>\n",
              "      <td>-0.188933</td>\n",
              "      <td>0.355501</td>\n",
              "      <td>-0.261515</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.374044</td>\n",
              "      <td>-0.008056</td>\n",
              "      <td>-0.037007</td>\n",
              "      <td>-0.045928</td>\n",
              "      <td>-0.187004</td>\n",
              "      <td>0.232452</td>\n",
              "      <td>0.069437</td>\n",
              "      <td>0.048717</td>\n",
              "      <td>-0.046981</td>\n",
              "      <td>0.094256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>poor_prop</th>\n",
              "      <td>-0.740836</td>\n",
              "      <td>0.608970</td>\n",
              "      <td>0.603800</td>\n",
              "      <td>0.590879</td>\n",
              "      <td>-0.613808</td>\n",
              "      <td>0.602339</td>\n",
              "      <td>-0.374044</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.066008</td>\n",
              "      <td>0.017036</td>\n",
              "      <td>0.061444</td>\n",
              "      <td>0.552310</td>\n",
              "      <td>-0.496967</td>\n",
              "      <td>-0.095054</td>\n",
              "      <td>0.003197</td>\n",
              "      <td>0.020620</td>\n",
              "      <td>-0.109004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_hos_beds</th>\n",
              "      <td>0.108880</td>\n",
              "      <td>-0.004089</td>\n",
              "      <td>0.005799</td>\n",
              "      <td>-0.049553</td>\n",
              "      <td>0.032009</td>\n",
              "      <td>-0.021012</td>\n",
              "      <td>-0.008056</td>\n",
              "      <td>-0.066008</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003130</td>\n",
              "      <td>0.058596</td>\n",
              "      <td>-0.071272</td>\n",
              "      <td>-0.027871</td>\n",
              "      <td>-0.006365</td>\n",
              "      <td>0.042278</td>\n",
              "      <td>0.059482</td>\n",
              "      <td>-0.074148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <td>0.017007</td>\n",
              "      <td>0.056570</td>\n",
              "      <td>-0.003761</td>\n",
              "      <td>0.007238</td>\n",
              "      <td>0.014583</td>\n",
              "      <td>0.013918</td>\n",
              "      <td>-0.037007</td>\n",
              "      <td>0.017036</td>\n",
              "      <td>-0.003130</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.014869</td>\n",
              "      <td>0.023757</td>\n",
              "      <td>-0.020700</td>\n",
              "      <td>-0.055338</td>\n",
              "      <td>0.037925</td>\n",
              "      <td>0.014755</td>\n",
              "      <td>-0.064096</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rainfall</th>\n",
              "      <td>-0.047200</td>\n",
              "      <td>0.082151</td>\n",
              "      <td>0.055845</td>\n",
              "      <td>0.091956</td>\n",
              "      <td>-0.064718</td>\n",
              "      <td>0.074684</td>\n",
              "      <td>-0.045928</td>\n",
              "      <td>0.061444</td>\n",
              "      <td>0.058596</td>\n",
              "      <td>0.014869</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.078278</td>\n",
              "      <td>-0.037285</td>\n",
              "      <td>-0.013171</td>\n",
              "      <td>-0.016170</td>\n",
              "      <td>0.109234</td>\n",
              "      <td>-0.037016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>parks</th>\n",
              "      <td>-0.391574</td>\n",
              "      <td>0.638951</td>\n",
              "      <td>0.707635</td>\n",
              "      <td>0.915544</td>\n",
              "      <td>-0.282817</td>\n",
              "      <td>0.673850</td>\n",
              "      <td>-0.187004</td>\n",
              "      <td>0.552310</td>\n",
              "      <td>-0.071272</td>\n",
              "      <td>0.023757</td>\n",
              "      <td>0.078278</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.707924</td>\n",
              "      <td>-0.052503</td>\n",
              "      <td>-0.034991</td>\n",
              "      <td>0.013265</td>\n",
              "      <td>-0.048862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_dist</th>\n",
              "      <td>0.249289</td>\n",
              "      <td>-0.586371</td>\n",
              "      <td>-0.708022</td>\n",
              "      <td>-0.769247</td>\n",
              "      <td>0.205241</td>\n",
              "      <td>-0.747906</td>\n",
              "      <td>0.232452</td>\n",
              "      <td>-0.496967</td>\n",
              "      <td>-0.027871</td>\n",
              "      <td>-0.020700</td>\n",
              "      <td>-0.037285</td>\n",
              "      <td>-0.707924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.021402</td>\n",
              "      <td>0.034890</td>\n",
              "      <td>-0.021320</td>\n",
              "      <td>0.032247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>airport_YES</th>\n",
              "      <td>0.182867</td>\n",
              "      <td>-0.134486</td>\n",
              "      <td>-0.115401</td>\n",
              "      <td>-0.073903</td>\n",
              "      <td>0.163774</td>\n",
              "      <td>0.005101</td>\n",
              "      <td>0.069437</td>\n",
              "      <td>-0.095054</td>\n",
              "      <td>-0.006365</td>\n",
              "      <td>-0.055338</td>\n",
              "      <td>-0.013171</td>\n",
              "      <td>-0.052503</td>\n",
              "      <td>0.021402</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.035491</td>\n",
              "      <td>-0.070341</td>\n",
              "      <td>0.017341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waterbody_Lake</th>\n",
              "      <td>0.036233</td>\n",
              "      <td>-0.025390</td>\n",
              "      <td>-0.026590</td>\n",
              "      <td>-0.046393</td>\n",
              "      <td>-0.004195</td>\n",
              "      <td>0.003452</td>\n",
              "      <td>0.048717</td>\n",
              "      <td>0.003197</td>\n",
              "      <td>0.042278</td>\n",
              "      <td>0.037925</td>\n",
              "      <td>-0.016170</td>\n",
              "      <td>-0.034991</td>\n",
              "      <td>0.034890</td>\n",
              "      <td>0.035491</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.196747</td>\n",
              "      <td>-0.366563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waterbody_Lake and River</th>\n",
              "      <td>-0.037497</td>\n",
              "      <td>0.009076</td>\n",
              "      <td>0.051649</td>\n",
              "      <td>0.013849</td>\n",
              "      <td>0.010554</td>\n",
              "      <td>-0.004354</td>\n",
              "      <td>-0.046981</td>\n",
              "      <td>0.020620</td>\n",
              "      <td>0.059482</td>\n",
              "      <td>0.014755</td>\n",
              "      <td>0.109234</td>\n",
              "      <td>0.013265</td>\n",
              "      <td>-0.021320</td>\n",
              "      <td>-0.070341</td>\n",
              "      <td>-0.196747</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.304095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>waterbody_River</th>\n",
              "      <td>0.071751</td>\n",
              "      <td>-0.060099</td>\n",
              "      <td>-0.098976</td>\n",
              "      <td>-0.037772</td>\n",
              "      <td>0.046251</td>\n",
              "      <td>-0.088609</td>\n",
              "      <td>0.094256</td>\n",
              "      <td>-0.109004</td>\n",
              "      <td>-0.074148</td>\n",
              "      <td>-0.064096</td>\n",
              "      <td>-0.037016</td>\n",
              "      <td>-0.048862</td>\n",
              "      <td>0.032247</td>\n",
              "      <td>0.017341</td>\n",
              "      <td>-0.366563</td>\n",
              "      <td>-0.304095</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             price  ...  waterbody_River\n",
              "price                     1.000000  ...         0.071751\n",
              "crime_rate               -0.466527  ...        -0.060099\n",
              "resid_area               -0.484754  ...        -0.098976\n",
              "air_qual                 -0.429300  ...        -0.037772\n",
              "room_num                  0.696304  ...         0.046251\n",
              "age                      -0.377999  ...        -0.088609\n",
              "teachers                  0.505655  ...         0.094256\n",
              "poor_prop                -0.740836  ...        -0.109004\n",
              "n_hos_beds                0.108880  ...        -0.074148\n",
              "n_hot_rooms               0.017007  ...        -0.064096\n",
              "rainfall                 -0.047200  ...        -0.037016\n",
              "parks                    -0.391574  ...        -0.048862\n",
              "avg_dist                  0.249289  ...         0.032247\n",
              "airport_YES               0.182867  ...         0.017341\n",
              "waterbody_Lake            0.036233  ...        -0.366563\n",
              "waterbody_Lake and River -0.037497  ...        -0.304095\n",
              "waterbody_River           0.071751  ...         1.000000\n",
              "\n",
              "[17 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qx0i7zMNvApL"
      },
      "source": [
        "del df['parks']"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "zE8lKOD1vvgq",
        "outputId": "fdec374e-fb6d-4b40-b4fa-193f9bc2be89"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>price</th>\n",
              "      <th>crime_rate</th>\n",
              "      <th>resid_area</th>\n",
              "      <th>air_qual</th>\n",
              "      <th>room_num</th>\n",
              "      <th>age</th>\n",
              "      <th>teachers</th>\n",
              "      <th>poor_prop</th>\n",
              "      <th>n_hos_beds</th>\n",
              "      <th>n_hot_rooms</th>\n",
              "      <th>rainfall</th>\n",
              "      <th>avg_dist</th>\n",
              "      <th>airport_YES</th>\n",
              "      <th>waterbody_Lake</th>\n",
              "      <th>waterbody_Lake and River</th>\n",
              "      <th>waterbody_River</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24.0</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>32.31</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>24.7</td>\n",
              "      <td>4.98</td>\n",
              "      <td>5.480</td>\n",
              "      <td>11.19200</td>\n",
              "      <td>23</td>\n",
              "      <td>4.0875</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>21.6</td>\n",
              "      <td>0.026944</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>22.2</td>\n",
              "      <td>9.14</td>\n",
              "      <td>7.332</td>\n",
              "      <td>12.17280</td>\n",
              "      <td>42</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34.7</td>\n",
              "      <td>0.026924</td>\n",
              "      <td>37.07</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>22.2</td>\n",
              "      <td>4.03</td>\n",
              "      <td>7.394</td>\n",
              "      <td>46.19856</td>\n",
              "      <td>38</td>\n",
              "      <td>4.9675</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33.4</td>\n",
              "      <td>0.031857</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>21.3</td>\n",
              "      <td>2.94</td>\n",
              "      <td>9.268</td>\n",
              "      <td>11.26720</td>\n",
              "      <td>45</td>\n",
              "      <td>6.0650</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.2</td>\n",
              "      <td>0.066770</td>\n",
              "      <td>32.18</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>21.3</td>\n",
              "      <td>5.33</td>\n",
              "      <td>8.824</td>\n",
              "      <td>11.28960</td>\n",
              "      <td>55</td>\n",
              "      <td>6.0625</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   price  crime_rate  ...  waterbody_Lake and River  waterbody_River\n",
              "0   24.0    0.006300  ...                         0                1\n",
              "1   21.6    0.026944  ...                         0                0\n",
              "2   34.7    0.026924  ...                         0                0\n",
              "3   33.4    0.031857  ...                         0                0\n",
              "4   36.2    0.066770  ...                         0                0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwMH50o8vy_k"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}