Chapter02/Exercise2.05/Exercise2_05.ipynb

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      "name": "Exercise2_05.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AbsjF_-0Lort",
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      "source": [
        "**Import the necessary modules and prepare the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k5QR71xFLGkh",
        "colab": {}
      },
      "source": [
        "import statsmodels.formula.api as smf\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RpkR60AxLNkl",
        "colab": {}
      },
      "source": [
        "rawBostonData = pd.read_csv('https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter02/Dataset/Boston.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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      "source": [
        "rawBostonData = rawBostonData.dropna()\n",
        "rawBostonData = rawBostonData.drop_duplicates() \n",
        "renamedBostonData = rawBostonData.rename(columns = {'CRIM':'crimeRatePerCapita',\n",
        " ' ZN ':'landOver25K_sqft',\n",
        " 'INDUS ':'non-retailLandProptn',\n",
        " 'CHAS':'riverDummy',\n",
        " 'NOX':'nitrixOxide_pp10m',\n",
        " 'RM':'AvgNo.RoomsPerDwelling',\n",
        " 'AGE':'ProptnOwnerOccupied',\n",
        " 'DIS':'weightedDist',\n",
        " 'RAD':'radialHighwaysAccess',\n",
        " 'TAX':'propTaxRate_per10K',\n",
        " 'PTRATIO':'pupilTeacherRatio',\n",
        " 'LSTAT':'pctLowerStatus',\n",
        " 'MEDV':'medianValue_Ks'})\n",
        "X = renamedBostonData.drop('crimeRatePerCapita', axis = 1)\n",
        "y = renamedBostonData[['crimeRatePerCapita']]\n",
        "seed = 10 \n",
        "test_data_size = 0.3 \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_data_size, random_state = seed)\n",
        "train_data = pd.concat([X_train, y_train], axis = 1)\n",
        "test_data = pd.concat([X_test, y_test], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3B9t1_oXO6GB"
      },
      "source": [
        "**Exercise 2.05: Fit a multiple linear regression model using the Statsmodels formula API** "
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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      "source": [
        "multiLinearModel = smf.ols(formula=\\\n",
        "'crimeRatePerCapita ~ pctLowerStatus + radialHighwaysAccess +\\\n",
        "medianValue_Ks + nitrixOxide_pp10m', data=train_data)\n",
        "multiLinearModResult = multiLinearModel.fit()\n",
        "print(multiLinearModResult.summary())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:     crimeRatePerCapita   R-squared:                       0.398\n",
            "Model:                            OLS   Adj. R-squared:                  0.391\n",
            "Method:                 Least Squares   F-statistic:                     57.77\n",
            "Date:                Sun, 19 Jan 2020   Prob (F-statistic):           2.19e-37\n",
            "Time:                        00:23:59   Log-Likelihood:                -1154.9\n",
            "No. Observations:                 354   AIC:                             2320.\n",
            "Df Residuals:                     349   BIC:                             2339.\n",
            "Df Model:                           4                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "========================================================================================\n",
            "                           coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------\n",
            "Intercept                0.8912      2.670      0.334      0.739      -4.360       6.142\n",
            "pctLowerStatus           0.1028      0.080      1.277      0.202      -0.055       0.261\n",
            "radialHighwaysAccess     0.4948      0.048     10.216      0.000       0.400       0.590\n",
            "medianValue_Ks          -0.1103      0.058     -1.916      0.056      -0.224       0.003\n",
            "nitrixOxide_pp10m       -2.1039      4.131     -0.509      0.611     -10.229       6.021\n",
            "==============================================================================\n",
            "Omnibus:                      534.476   Durbin-Watson:                   1.999\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           100129.789\n",
            "Skew:                           7.866   Prob(JB):                         0.00\n",
            "Kurtosis:                      83.876   Cond. No.                         374.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}