Final_DSBDA4.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('HousingData 2.csv')\n",
        "df.head()"
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              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
              "\n",
              "        B  LSTAT  MEDV  \n",
              "0  396.90   4.98  24.0  \n",
              "1  396.90   9.14  21.6  \n",
              "2  392.83   4.03  34.7  \n",
              "3  394.63   2.94  33.4  \n",
              "4  396.90    NaN  36.2  "
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              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
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              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
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              "      <td>17.8</td>\n",
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              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
              "      <td>242</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36.2</td>\n",
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        "#Feature Description\n",
        "**CRIM** - per capita crime rate by town\n",
        "**ZN** - proportion of residential land zoned for lots over 25,000 sq.ft.\n",
        "**INDUS** - proportion of non-retail business acres per town\n",
        "**CHAS** - Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
        "**NOX** - nitric oxides concentration (parts per 10 million)\n",
        "**RM** - average number of rooms per dwelling\n",
        "**AGE** - proportion of owner-occupied units built prior to 1940\n",
        "**DIS** - weighted distances to five Boston employment centres\n",
        "**RAD** - index of accessibility to radial highways\n",
        "**TAX** - full-value property-tax rate per '10,000 Dollar'\n",
        "**PTRATIO** - pupil-teacher ratio by town\n",
        "**B** - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
        "**LSTAT** - lower status of the population\n",
        "**MEDV** - Median value of owner-occupied homes in 1000's Dollar"
      ],
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              "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
              "count  486.000000  486.000000  486.000000  486.000000  506.000000  506.000000   \n",
              "mean     3.611874   11.211934   11.083992    0.069959    0.554695    6.284634   \n",
              "std      8.720192   23.388876    6.835896    0.255340    0.115878    0.702617   \n",
              "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
              "25%      0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
              "50%      0.253715    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
              "75%      3.560263   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
              "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
              "\n",
              "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
              "count  486.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
              "mean    68.518519    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
              "std     27.999513    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
              "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
              "25%     45.175000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
              "50%     76.800000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
              "75%     93.975000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
              "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
              "\n",
              "            LSTAT        MEDV  \n",
              "count  486.000000  506.000000  \n",
              "mean    12.715432   22.532806  \n",
              "std      7.155871    9.197104  \n",
              "min      1.730000    5.000000  \n",
              "25%      7.125000   17.025000  \n",
              "50%     11.430000   21.200000  \n",
              "75%     16.955000   25.000000  \n",
              "max     37.970000   50.000000  "
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>486.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>486.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>506.000000</td>\n",
              "      <td>486.000000</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>486.000000</td>\n",
              "      <td>506.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.611874</td>\n",
              "      <td>11.211934</td>\n",
              "      <td>11.083992</td>\n",
              "      <td>0.069959</td>\n",
              "      <td>0.554695</td>\n",
              "      <td>6.284634</td>\n",
              "      <td>68.518519</td>\n",
              "      <td>3.795043</td>\n",
              "      <td>9.549407</td>\n",
              "      <td>408.237154</td>\n",
              "      <td>18.455534</td>\n",
              "      <td>356.674032</td>\n",
              "      <td>12.715432</td>\n",
              "      <td>22.532806</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.720192</td>\n",
              "      <td>23.388876</td>\n",
              "      <td>6.835896</td>\n",
              "      <td>0.255340</td>\n",
              "      <td>0.115878</td>\n",
              "      <td>0.702617</td>\n",
              "      <td>27.999513</td>\n",
              "      <td>2.105710</td>\n",
              "      <td>8.707259</td>\n",
              "      <td>168.537116</td>\n",
              "      <td>2.164946</td>\n",
              "      <td>91.294864</td>\n",
              "      <td>7.155871</td>\n",
              "      <td>9.197104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.006320</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.460000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.385000</td>\n",
              "      <td>3.561000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>1.129600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>12.600000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>1.730000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.081900</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.190000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.449000</td>\n",
              "      <td>5.885500</td>\n",
              "      <td>45.175000</td>\n",
              "      <td>2.100175</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>279.000000</td>\n",
              "      <td>17.400000</td>\n",
              "      <td>375.377500</td>\n",
              "      <td>7.125000</td>\n",
              "      <td>17.025000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.253715</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.690000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.538000</td>\n",
              "      <td>6.208500</td>\n",
              "      <td>76.800000</td>\n",
              "      <td>3.207450</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>19.050000</td>\n",
              "      <td>391.440000</td>\n",
              "      <td>11.430000</td>\n",
              "      <td>21.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.560263</td>\n",
              "      <td>12.500000</td>\n",
              "      <td>18.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.624000</td>\n",
              "      <td>6.623500</td>\n",
              "      <td>93.975000</td>\n",
              "      <td>5.188425</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>20.200000</td>\n",
              "      <td>396.225000</td>\n",
              "      <td>16.955000</td>\n",
              "      <td>25.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>88.976200</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>27.740000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.871000</td>\n",
              "      <td>8.780000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>12.126500</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>711.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>396.900000</td>\n",
              "      <td>37.970000</td>\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtqMob0f9lVf",
        "outputId": "629d1e87-e6ee-4842-9a17-e8c2962f78fb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 506 entries, 0 to 505\n",
            "Data columns (total 14 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   CRIM     486 non-null    float64\n",
            " 1   ZN       486 non-null    float64\n",
            " 2   INDUS    486 non-null    float64\n",
            " 3   CHAS     486 non-null    float64\n",
            " 4   NOX      506 non-null    float64\n",
            " 5   RM       506 non-null    float64\n",
            " 6   AGE      486 non-null    float64\n",
            " 7   DIS      506 non-null    float64\n",
            " 8   RAD      506 non-null    int64  \n",
            " 9   TAX      506 non-null    int64  \n",
            " 10  PTRATIO  506 non-null    float64\n",
            " 11  B        506 non-null    float64\n",
            " 12  LSTAT    486 non-null    float64\n",
            " 13  MEDV     506 non-null    float64\n",
            "dtypes: float64(12), int64(2)\n",
            "memory usage: 55.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lnAc42ks9meN",
        "outputId": "626a7948-f17d-4603-df06-b58c3788036a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       20\n",
              "ZN         20\n",
              "INDUS      20\n",
              "CHAS       20\n",
              "NOX         0\n",
              "RM          0\n",
              "AGE        20\n",
              "DIS         0\n",
              "RAD         0\n",
              "TAX         0\n",
              "PTRATIO     0\n",
              "B           0\n",
              "LSTAT      20\n",
              "MEDV        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['CRIM'].fillna(df['CRIM'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "TQg9YqUj9tJV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['ZN'].fillna(df['ZN'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "hcwmsS7g-oBv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['ZN'].fillna(df['ZN'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "V0HzulLm-vVa"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['INDUS'].fillna(df['INDUS'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "_o__XBpX-x-e"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['CHAS'].fillna(df['CHAS'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "5TZbHp_7-1Zp"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['AGE'].fillna(df['AGE'].mean(),inplace=True)"
      ],
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        "id": "7z63Npt7-3xG"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['LSTAT'].fillna(df['LSTAT'].mean(),inplace=True)"
      ],
      "metadata": {
        "id": "MSlrIyDQ-66a"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "fRwJDNrz-8oi",
        "outputId": "f59e5652-8a3f-4440-f494-7e015a838c39"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CRIM       0\n",
              "ZN         0\n",
              "INDUS      0\n",
              "CHAS       0\n",
              "NOX        0\n",
              "RM         0\n",
              "AGE        0\n",
              "DIS        0\n",
              "RAD        0\n",
              "TAX        0\n",
              "PTRATIO    0\n",
              "B          0\n",
              "LSTAT      0\n",
              "MEDV       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "3ULiyW3M_BUr",
        "outputId": "859d7b5c-85d8-4454-b0f7-67f0720269cc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             CRIM        ZN     INDUS      CHAS       NOX        RM       AGE  \\\n",
              "CRIM     1.000000 -0.182930  0.391161 -0.052223  0.410377 -0.215434  0.344934   \n",
              "ZN      -0.182930  1.000000 -0.513336 -0.036147 -0.502287  0.316550 -0.541274   \n",
              "INDUS    0.391161 -0.513336  1.000000  0.058035  0.740965 -0.381457  0.614592   \n",
              "CHAS    -0.052223 -0.036147  0.058035  1.000000  0.073286  0.102284  0.075206   \n",
              "NOX      0.410377 -0.502287  0.740965  0.073286  1.000000 -0.302188  0.711461   \n",
              "RM      -0.215434  0.316550 -0.381457  0.102284 -0.302188  1.000000 -0.241351   \n",
              "AGE      0.344934 -0.541274  0.614592  0.075206  0.711461 -0.241351  1.000000   \n",
              "DIS     -0.366523  0.638388 -0.699639 -0.091680 -0.769230  0.205246 -0.724353   \n",
              "RAD      0.608886 -0.306316  0.593176  0.001425  0.611441 -0.209847  0.449989   \n",
              "TAX      0.566528 -0.308334  0.716062 -0.031483  0.668023 -0.292048  0.500589   \n",
              "PTRATIO  0.273384 -0.403085  0.384806 -0.109310  0.188933 -0.355501  0.262723   \n",
              "B       -0.370163  0.167431 -0.354597  0.050055 -0.380051  0.128069 -0.265282   \n",
              "LSTAT    0.434044 -0.407549  0.567354 -0.046166  0.572379 -0.602962  0.574893   \n",
              "MEDV    -0.379695  0.365943 -0.478657  0.179882 -0.427321  0.695360 -0.380223   \n",
              "\n",
              "              DIS       RAD       TAX   PTRATIO         B     LSTAT      MEDV  \n",
              "CRIM    -0.366523  0.608886  0.566528  0.273384 -0.370163  0.434044 -0.379695  \n",
              "ZN       0.638388 -0.306316 -0.308334 -0.403085  0.167431 -0.407549  0.365943  \n",
              "INDUS   -0.699639  0.593176  0.716062  0.384806 -0.354597  0.567354 -0.478657  \n",
              "CHAS    -0.091680  0.001425 -0.031483 -0.109310  0.050055 -0.046166  0.179882  \n",
              "NOX     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.572379 -0.427321  \n",
              "RM       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.602962  0.695360  \n",
              "AGE     -0.724353  0.449989  0.500589  0.262723 -0.265282  0.574893 -0.380223  \n",
              "DIS      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.483429  0.249929  \n",
              "RAD     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.468440 -0.381626  \n",
              "TAX     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.524545 -0.468536  \n",
              "PTRATIO -0.232471  0.464741  0.460853  1.000000 -0.177383  0.373343 -0.507787  \n",
              "B        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.368886  0.333461  \n",
              "LSTAT   -0.483429  0.468440  0.524545  0.373343 -0.368886  1.000000 -0.721975  \n",
              "MEDV     0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.721975  1.000000  "
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CRIM</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.182930</td>\n",
              "      <td>0.391161</td>\n",
              "      <td>-0.052223</td>\n",
              "      <td>0.410377</td>\n",
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              "      <td>0.344934</td>\n",
              "      <td>-0.366523</td>\n",
              "      <td>0.608886</td>\n",
              "      <td>0.566528</td>\n",
              "      <td>0.273384</td>\n",
              "      <td>-0.370163</td>\n",
              "      <td>0.434044</td>\n",
              "      <td>-0.379695</td>\n",
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              "    <tr>\n",
              "      <th>ZN</th>\n",
              "      <td>-0.182930</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.513336</td>\n",
              "      <td>-0.036147</td>\n",
              "      <td>-0.502287</td>\n",
              "      <td>0.316550</td>\n",
              "      <td>-0.541274</td>\n",
              "      <td>0.638388</td>\n",
              "      <td>-0.306316</td>\n",
              "      <td>-0.308334</td>\n",
              "      <td>-0.403085</td>\n",
              "      <td>0.167431</td>\n",
              "      <td>-0.407549</td>\n",
              "      <td>0.365943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INDUS</th>\n",
              "      <td>0.391161</td>\n",
              "      <td>-0.513336</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.058035</td>\n",
              "      <td>0.740965</td>\n",
              "      <td>-0.381457</td>\n",
              "      <td>0.614592</td>\n",
              "      <td>-0.699639</td>\n",
              "      <td>0.593176</td>\n",
              "      <td>0.716062</td>\n",
              "      <td>0.384806</td>\n",
              "      <td>-0.354597</td>\n",
              "      <td>0.567354</td>\n",
              "      <td>-0.478657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHAS</th>\n",
              "      <td>-0.052223</td>\n",
              "      <td>-0.036147</td>\n",
              "      <td>0.058035</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.073286</td>\n",
              "      <td>0.102284</td>\n",
              "      <td>0.075206</td>\n",
              "      <td>-0.091680</td>\n",
              "      <td>0.001425</td>\n",
              "      <td>-0.031483</td>\n",
              "      <td>-0.109310</td>\n",
              "      <td>0.050055</td>\n",
              "      <td>-0.046166</td>\n",
              "      <td>0.179882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOX</th>\n",
              "      <td>0.410377</td>\n",
              "      <td>-0.502287</td>\n",
              "      <td>0.740965</td>\n",
              "      <td>0.073286</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>0.711461</td>\n",
              "      <td>-0.769230</td>\n",
              "      <td>0.611441</td>\n",
              "      <td>0.668023</td>\n",
              "      <td>0.188933</td>\n",
              "      <td>-0.380051</td>\n",
              "      <td>0.572379</td>\n",
              "      <td>-0.427321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RM</th>\n",
              "      <td>-0.215434</td>\n",
              "      <td>0.316550</td>\n",
              "      <td>-0.381457</td>\n",
              "      <td>0.102284</td>\n",
              "      <td>-0.302188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.241351</td>\n",
              "      <td>0.205246</td>\n",
              "      <td>-0.209847</td>\n",
              "      <td>-0.292048</td>\n",
              "      <td>-0.355501</td>\n",
              "      <td>0.128069</td>\n",
              "      <td>-0.602962</td>\n",
              "      <td>0.695360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>0.344934</td>\n",
              "      <td>-0.541274</td>\n",
              "      <td>0.614592</td>\n",
              "      <td>0.075206</td>\n",
              "      <td>0.711461</td>\n",
              "      <td>-0.241351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.724353</td>\n",
              "      <td>0.449989</td>\n",
              "      <td>0.500589</td>\n",
              "      <td>0.262723</td>\n",
              "      <td>-0.265282</td>\n",
              "      <td>0.574893</td>\n",
              "      <td>-0.380223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>-0.366523</td>\n",
              "      <td>0.638388</td>\n",
              "      <td>-0.699639</td>\n",
              "      <td>-0.091680</td>\n",
              "      <td>-0.769230</td>\n",
              "      <td>0.205246</td>\n",
              "      <td>-0.724353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.494588</td>\n",
              "      <td>-0.534432</td>\n",
              "      <td>-0.232471</td>\n",
              "      <td>0.291512</td>\n",
              "      <td>-0.483429</td>\n",
              "      <td>0.249929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAD</th>\n",
              "      <td>0.608886</td>\n",
              "      <td>-0.306316</td>\n",
              "      <td>0.593176</td>\n",
              "      <td>0.001425</td>\n",
              "      <td>0.611441</td>\n",
              "      <td>-0.209847</td>\n",
              "      <td>0.449989</td>\n",
              "      <td>-0.494588</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.910228</td>\n",
              "      <td>0.464741</td>\n",
              "      <td>-0.444413</td>\n",
              "      <td>0.468440</td>\n",
              "      <td>-0.381626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX</th>\n",
              "      <td>0.566528</td>\n",
              "      <td>-0.308334</td>\n",
              "      <td>0.716062</td>\n",
              "      <td>-0.031483</td>\n",
              "      <td>0.668023</td>\n",
              "      <td>-0.292048</td>\n",
              "      <td>0.500589</td>\n",
              "      <td>-0.534432</td>\n",
              "      <td>0.910228</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.460853</td>\n",
              "      <td>-0.441808</td>\n",
              "      <td>0.524545</td>\n",
              "      <td>-0.468536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PTRATIO</th>\n",
              "      <td>0.273384</td>\n",
              "      <td>-0.403085</td>\n",
              "      <td>0.384806</td>\n",
              "      <td>-0.109310</td>\n",
              "      <td>0.188933</td>\n",
              "      <td>-0.355501</td>\n",
              "      <td>0.262723</td>\n",
              "      <td>-0.232471</td>\n",
              "      <td>0.464741</td>\n",
              "      <td>0.460853</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.177383</td>\n",
              "      <td>0.373343</td>\n",
              "      <td>-0.507787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>-0.370163</td>\n",
              "      <td>0.167431</td>\n",
              "      <td>-0.354597</td>\n",
              "      <td>0.050055</td>\n",
              "      <td>-0.380051</td>\n",
              "      <td>0.128069</td>\n",
              "      <td>-0.265282</td>\n",
              "      <td>0.291512</td>\n",
              "      <td>-0.444413</td>\n",
              "      <td>-0.441808</td>\n",
              "      <td>-0.177383</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.368886</td>\n",
              "      <td>0.333461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTAT</th>\n",
              "      <td>0.434044</td>\n",
              "      <td>-0.407549</td>\n",
              "      <td>0.567354</td>\n",
              "      <td>-0.046166</td>\n",
              "      <td>0.572379</td>\n",
              "      <td>-0.602962</td>\n",
              "      <td>0.574893</td>\n",
              "      <td>-0.483429</td>\n",
              "      <td>0.468440</td>\n",
              "      <td>0.524545</td>\n",
              "      <td>0.373343</td>\n",
              "      <td>-0.368886</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.721975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEDV</th>\n",
              "      <td>-0.379695</td>\n",
              "      <td>0.365943</td>\n",
              "      <td>-0.478657</td>\n",
              "      <td>0.179882</td>\n",
              "      <td>-0.427321</td>\n",
              "      <td>0.695360</td>\n",
              "      <td>-0.380223</td>\n",
              "      <td>0.249929</td>\n",
              "      <td>-0.381626</td>\n",
              "      <td>-0.468536</td>\n",
              "      <td>-0.507787</td>\n",
              "      <td>0.333461</td>\n",
              "      <td>-0.721975</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          },
          "metadata": {},
          "execution_count": 17
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      ]
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      "cell_type": "code",
      "source": [
        "df.shape"
      ],
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        "id": "izlOjhbwAWGV",
        "outputId": "408de4c3-8c3c-4dc4-ddfc-8155e1f99bfa"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(506, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
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      "source": [
        "Splitting dataset into DEPENDENT (x) and INDEPENDENT (y) variables"
      ],
      "metadata": {
        "id": "aroduACPfRKD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y=df['MEDV']\n",
        "y"
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        "colab": {
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        "id": "nseLtiKHQHmI",
        "outputId": "5061ae89-d89b-4c01-af38-b89a82c3c9f1"
      },
      "execution_count": 19,
      "outputs": [
        {
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          "data": {
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              "0      24.0\n",
              "1      21.6\n",
              "2      34.7\n",
              "3      33.4\n",
              "4      36.2\n",
              "       ... \n",
              "501    22.4\n",
              "502    20.6\n",
              "503    23.9\n",
              "504    22.0\n",
              "505    11.9\n",
              "Name: MEDV, Length: 506, dtype: float64"
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          "metadata": {},
          "execution_count": 19
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      "cell_type": "code",
      "source": [
        "x=df.drop(['MEDV'],axis=1)\n",
        "x"
      ],
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          "height": 424
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      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        CRIM    ZN  INDUS  CHAS    NOX     RM        AGE     DIS  RAD  TAX  \\\n",
              "0    0.00632  18.0   2.31   0.0  0.538  6.575  65.200000  4.0900    1  296   \n",
              "1    0.02731   0.0   7.07   0.0  0.469  6.421  78.900000  4.9671    2  242   \n",
              "2    0.02729   0.0   7.07   0.0  0.469  7.185  61.100000  4.9671    2  242   \n",
              "3    0.03237   0.0   2.18   0.0  0.458  6.998  45.800000  6.0622    3  222   \n",
              "4    0.06905   0.0   2.18   0.0  0.458  7.147  54.200000  6.0622    3  222   \n",
              "..       ...   ...    ...   ...    ...    ...        ...     ...  ...  ...   \n",
              "501  0.06263   0.0  11.93   0.0  0.573  6.593  69.100000  2.4786    1  273   \n",
              "502  0.04527   0.0  11.93   0.0  0.573  6.120  76.700000  2.2875    1  273   \n",
              "503  0.06076   0.0  11.93   0.0  0.573  6.976  91.000000  2.1675    1  273   \n",
              "504  0.10959   0.0  11.93   0.0  0.573  6.794  89.300000  2.3889    1  273   \n",
              "505  0.04741   0.0  11.93   0.0  0.573  6.030  68.518519  2.5050    1  273   \n",
              "\n",
              "     PTRATIO       B      LSTAT  \n",
              "0       15.3  396.90   4.980000  \n",
              "1       17.8  396.90   9.140000  \n",
              "2       17.8  392.83   4.030000  \n",
              "3       18.7  394.63   2.940000  \n",
              "4       18.7  396.90  12.715432  \n",
              "..       ...     ...        ...  \n",
              "501     21.0  391.99  12.715432  \n",
              "502     21.0  396.90   9.080000  \n",
              "503     21.0  396.90   5.640000  \n",
              "504     21.0  393.45   6.480000  \n",
              "505     21.0  396.90   7.880000  \n",
              "\n",
              "[506 rows x 13 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
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              "      <td>0.00632</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>7.147</td>\n",
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              "      <td>2.5050</td>\n",
              "      <td>1</td>\n",
              "      <td>273</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.880000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f6a27fe-f6c1-43f9-961c-3602ed62601b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      \n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f6a27fe-f6c1-43f9-961c-3602ed62601b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1f6a27fe-f6c1-43f9-961c-3602ed62601b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Transformation\n",
        "StandardScaler"
      ],
      "metadata": {
        "id": "9XLlj5YuYr0b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler()\n",
        "x_scaled=scaler.fit_transform(x)\n",
        "data_normalised=pd.DataFrame(x_scaled)\n",
        "data_normalised"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rg1NzxBdYqz7",
        "outputId": "45c3495d-8bfe-4125-e03d-cc672625ab8c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0         1         2         3         4         5             6   \\\n",
              "0   -0.422328  0.296443 -1.311010 -0.279851 -0.144217  0.413672 -1.210593e-01   \n",
              "1   -0.419870 -0.489639 -0.599771 -0.279851 -0.740262  0.194274  3.787156e-01   \n",
              "2   -0.419872 -0.489639 -0.599771 -0.279851 -0.740262  1.282714 -2.706270e-01   \n",
              "3   -0.419277 -0.489639 -1.330435 -0.279851 -0.835284  1.016303 -8.287698e-01   \n",
              "4   -0.414981 -0.489639 -1.330435 -0.279851 -0.835284  1.228577 -5.223385e-01   \n",
              "..        ...       ...       ...       ...       ...       ...           ...   \n",
              "501 -0.415733 -0.489639  0.126411 -0.279851  0.158124  0.439316  2.121240e-02   \n",
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              "504 -0.410232 -0.489639  0.126411 -0.279851  0.158124  0.725672  7.581068e-01   \n",
              "505 -0.417515 -0.489639  0.126411 -0.279851  0.158124 -0.362767  5.184109e-16   \n",
              "\n",
              "           7         8         9         10        11        12  \n",
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              "3    1.077737 -0.752922 -1.106115  0.113032  0.416163 -1.395333  \n",
              "4    1.077737 -0.752922 -1.106115  0.113032  0.441052  0.000000  \n",
              "..        ...       ...       ...       ...       ...       ...  \n",
              "501 -0.625796 -0.982843 -0.803212  1.176466  0.387217  0.000000  \n",
              "502 -0.716639 -0.982843 -0.803212  1.176466  0.441052 -0.518917  \n",
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              "504 -0.668437 -0.982843 -0.803212  1.176466  0.403225 -0.890038  \n",
              "505 -0.613246 -0.982843 -0.803212  1.176466  0.441052 -0.690204  \n",
              "\n",
              "[506 rows x 13 columns]"
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              "      <td>-1.330435</td>\n",
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              "      <td>-0.279851</td>\n",
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              "      <td>-0.716639</td>\n",
              "      <td>-0.982843</td>\n",
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              "      <td>0.984960</td>\n",
              "      <td>8.201227e-01</td>\n",
              "      <td>-0.773684</td>\n",
              "      <td>-0.982843</td>\n",
              "      <td>-0.803212</td>\n",
              "      <td>1.176466</td>\n",
              "      <td>0.441052</td>\n",
              "      <td>-1.009938</td>\n",
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              "      <td>0.158124</td>\n",
              "      <td>0.725672</td>\n",
              "      <td>7.581068e-01</td>\n",
              "      <td>-0.668437</td>\n",
              "      <td>-0.982843</td>\n",
              "      <td>-0.803212</td>\n",
              "      <td>1.176466</td>\n",
              "      <td>0.403225</td>\n",
              "      <td>-0.890038</td>\n",
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              "      <td>-0.279851</td>\n",
              "      <td>0.158124</td>\n",
              "      <td>-0.362767</td>\n",
              "      <td>5.184109e-16</td>\n",
              "      <td>-0.613246</td>\n",
              "      <td>-0.982843</td>\n",
              "      <td>-0.803212</td>\n",
              "      <td>1.176466</td>\n",
              "      <td>0.441052</td>\n",
              "      <td>-0.690204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a46d8ac-7ecc-41aa-88aa-73ac7181b41b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2a46d8ac-7ecc-41aa-88aa-73ac7181b41b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a46d8ac-7ecc-41aa-88aa-73ac7181b41b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Divide dataset into training and testing dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "xtrain,xtest,ytrain,ytest=train_test_split(x_scaled,y,test_size=0.2,random_state=0)\n",
        "#This line splits the input data (x) and target variable (y) into training and testing sets. "
      ],
      "metadata": {
        "id": "g5g37AKJgTA-"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Import LinearRegression Model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "lm=LinearRegression()"
      ],
      "metadata": {
        "id": "mBPlMprPhgD3"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=lm.fit(xtrain,ytrain)"
      ],
      "metadata": {
        "id": "9QnYn4FMhv5_"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ytrain_pred=lm.predict(xtrain)\n",
        "ytest_pred=lm.predict(xtest)"
      ],
      "metadata": {
        "id": "hwyX7FmAiJdm"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dt=pd.DataFrame(ytrain_pred,ytrain)\n",
        "dt=pd.DataFrame(ytest_pred,ytest)"
      ],
      "metadata": {
        "id": "5dCm67Coizwx"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error,r2_score\n",
        "mse=mean_squared_error(ytest,ytest_pred)\n",
        "print(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzpUganZje0Q",
        "outputId": "47d76b20-01eb-409e-e975-cbb7c14016e2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34.98738954423858\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse=mean_squared_error(ytrain,ytrain_pred)\n",
        "print(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBgLZvnenw2F",
        "outputId": "5c57b50f-ea9d-4bc9-8f8b-ece0ecd9f113"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.01911591303659\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Training Accuracy\n",
        "print(lm.score(xtrain,ytrain))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjxTeO7jn1Ia",
        "outputId": "d749f9ae-65fb-487c-8bfc-a33320995245"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.764878538775553\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing Accuracy\n",
        "print(lm.score(xtest,ytest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-gaObaToF-M",
        "outputId": "8f7c5e05-1686-45d3-c34c-5c644da76620"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.5703296053895581\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plotting Linear Regression Model\n",
        "plt.scatter(ytrain,ytrain_pred,c='blue',marker='o',label='training data')\n",
        "plt.scatter(ytest,ytest_pred,c='lightgreen',marker='s',label='testing data')\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('True Values vs Predicted Values')\n",
        "plt.legend(loc='upper left')\n",
        "plt.plot\n",
        "plt.show"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "YDSiNJGDoRBi",
        "outputId": "02720e3e-0a0f-4b86-ca28-e2f17f9abdd1"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show(close=None, block=None)>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}