mlessentials/Chapter02/All_Code/Chapter_2_Regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o_xWRbP9oeJo"
   },
   "source": [
    "### **Exercise 2.01: Loading and preparing the data for analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "S_e0RsA-fBKd"
   },
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.graphics.api as smg\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import patsy\n",
    "from statsmodels.graphics.correlation import plot_corr\n",
    "from sklearn.model_selection import train_test_split\n",
    "plt.style.use('seaborn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset into a pandas dataframe\n",
    "\n",
    "rawBostonData = pd.read_csv('https://raw.githubusercontent.com/PacktWorkshops/The-Data-Science-Workshop/master/Chapter02/Dataset/Boston.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "YEO5woBrpYci",
    "outputId": "8eb44eb8-87a6-432a-cf97-a01e0727d68a"
   },
   "outputs": [
    {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>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>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>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>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>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>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM   ZN   INDUS   CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0    2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.02731   0.0    7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0    7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0    2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0    2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "   LSTAT  MEDV  \n",
       "0   4.98  24.0  \n",
       "1   9.14  21.6  \n",
       "2   4.03  34.7  \n",
       "3   2.94  33.4  \n",
       "4   5.33  36.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspect the dataframe\n",
    "\n",
    "rawBostonData.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Zz23WUNNpx6c"
   },
   "outputs": [],
   "source": [
    "# Drop missing values from the dataframe\n",
    "\n",
    "rawBostonData = rawBostonData.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gDCUyRscp43W"
   },
   "outputs": [],
   "source": [
    "# Drop duplicate records from the dataframe\n",
    "\n",
    "rawBostonData = rawBostonData.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
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    "colab_type": "code",
    "id": "Q9GyOY_9p5Pt",
    "outputId": "bcae4407-2c54-4027-9fff-48de3b803c3d"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "['CRIM',\n",
       " ' ZN ',\n",
       " 'INDUS ',\n",
       " 'CHAS',\n",
       " 'NOX',\n",
       " 'RM',\n",
       " 'AGE',\n",
       " 'DIS',\n",
       " 'RAD',\n",
       " 'TAX',\n",
       " 'PTRATIO',\n",
       " 'LSTAT',\n",
       " 'MEDV']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List the dataframe column names\n",
    "\n",
    "list(rawBostonData.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BFydSue8qKaZ"
   },
   "outputs": [],
   "source": [
    "# Rename the dataframe column headings\n",
    "\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'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "792LNU-3qKn9",
    "outputId": "63f9cca7-24ed-42dd-ab07-d43a29499393"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 506 entries, 0 to 505\n",
      "Data columns (total 13 columns):\n",
      "crimeRatePerCapita        506 non-null float64\n",
      "landOver25K_sqft          506 non-null float64\n",
      "non-retailLandProptn      506 non-null float64\n",
      "riverDummy                506 non-null int64\n",
      "nitrixOxide_pp10m         506 non-null float64\n",
      "AvgNo.RoomsPerDwelling    506 non-null float64\n",
      "ProptnOwnerOccupied       506 non-null float64\n",
      "weightedDist              506 non-null float64\n",
      "radialHighwaysAccess      506 non-null int64\n",
      "propTaxRate_per10K        506 non-null int64\n",
      "pupilTeacherRatio         506 non-null float64\n",
      "pctLowerStatus            506 non-null float64\n",
      "medianValue_Ks            506 non-null float64\n",
      "dtypes: float64(10), int64(3)\n",
      "memory usage: 55.3 KB\n"
     ]
    }
   ],
   "source": [
    "# Inspect the types of data in the dataframe\n",
    "\n",
    "renamedBostonData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "ZEyhe_JeqKyg",
    "outputId": "9dda9806-e319-45e5-a3e5-06df3ed90550"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>crimeRatePerCapita</td>\n",
       "      <td>506.0</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.25651</td>\n",
       "      <td>3.677082</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>landOver25K_sqft</td>\n",
       "      <td>506.0</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>non-retailLandProptn</td>\n",
       "      <td>506.0</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>riverDummy</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nitrixOxide_pp10m</td>\n",
       "      <td>506.0</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>AvgNo.RoomsPerDwelling</td>\n",
       "      <td>506.0</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>6.20850</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ProptnOwnerOccupied</td>\n",
       "      <td>506.0</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>77.50000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>weightedDist</td>\n",
       "      <td>506.0</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>3.20745</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>radialHighwaysAccess</td>\n",
       "      <td>506.0</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>propTaxRate_per10K</td>\n",
       "      <td>506.0</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pupilTeacherRatio</td>\n",
       "      <td>506.0</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pctLowerStatus</td>\n",
       "      <td>506.0</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>11.36000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medianValue_Ks</td>\n",
       "      <td>506.0</td>\n",
       "      <td>22.532806</td>\n",
       "      <td>9.197104</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>21.20000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count        mean         std        min         25%  \\\n",
       "crimeRatePerCapita      506.0    3.613524    8.601545    0.00632    0.082045   \n",
       "landOver25K_sqft        506.0   11.363636   23.322453    0.00000    0.000000   \n",
       "non-retailLandProptn    506.0   11.136779    6.860353    0.46000    5.190000   \n",
       "riverDummy              506.0    0.069170    0.253994    0.00000    0.000000   \n",
       "nitrixOxide_pp10m       506.0    0.554695    0.115878    0.38500    0.449000   \n",
       "AvgNo.RoomsPerDwelling  506.0    6.284634    0.702617    3.56100    5.885500   \n",
       "ProptnOwnerOccupied     506.0   68.574901   28.148861    2.90000   45.025000   \n",
       "weightedDist            506.0    3.795043    2.105710    1.12960    2.100175   \n",
       "radialHighwaysAccess    506.0    9.549407    8.707259    1.00000    4.000000   \n",
       "propTaxRate_per10K      506.0  408.237154  168.537116  187.00000  279.000000   \n",
       "pupilTeacherRatio       506.0   18.455534    2.164946   12.60000   17.400000   \n",
       "pctLowerStatus          506.0   12.653063    7.141062    1.73000    6.950000   \n",
       "medianValue_Ks          506.0   22.532806    9.197104    5.00000   17.025000   \n",
       "\n",
       "                              50%         75%       max  \n",
       "crimeRatePerCapita        0.25651    3.677082   88.9762  \n",
       "landOver25K_sqft          0.00000   12.500000  100.0000  \n",
       "non-retailLandProptn      9.69000   18.100000   27.7400  \n",
       "riverDummy                0.00000    0.000000    1.0000  \n",
       "nitrixOxide_pp10m         0.53800    0.624000    0.8710  \n",
       "AvgNo.RoomsPerDwelling    6.20850    6.623500    8.7800  \n",
       "ProptnOwnerOccupied      77.50000   94.075000  100.0000  \n",
       "weightedDist              3.20745    5.188425   12.1265  \n",
       "radialHighwaysAccess      5.00000   24.000000   24.0000  \n",
       "propTaxRate_per10K      330.00000  666.000000  711.0000  \n",
       "pupilTeacherRatio        19.05000   20.200000   22.0000  \n",
       "pctLowerStatus           11.36000   16.955000   37.9700  \n",
       "medianValue_Ks           21.20000   25.000000   50.0000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate basic statistics using the values in the dataframe\n",
    "\n",
    "renamedBostonData.describe(include=[np.number]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "n5wmtur8rXFG"
   },
   "outputs": [],
   "source": [
    "# Split the data set into training and test sets\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 793
    },
    "colab_type": "code",
    "id": "G8w04aBtrXWQ",
    "outputId": "a0751f02-6f76-43e6-e0e1-1c522255c60f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and plot a correlation matrix\n",
    "\n",
    "corrMatrix = train_data.corr(method = 'pearson')\n",
    "xnames=list(train_data.columns)\n",
    "ynames=list(train_data.columns)\n",
    "plot_corr(corrMatrix, xnames=xnames, ynames=ynames,\\\n",
    "          title=None, normcolor=False, cmap='RdYlBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aWfnyCJhr7SP"
   },
   "source": [
    "### **Exercise 2.02: Graphical investigation of linear relationships using Python**\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "colab_type": "code",
    "id": "pZMrvpvirkMa",
    "outputId": "4b3a1921-82cf-449a-c57a-6fee9d804b20"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the seaborn function regplot to create a scatter plot and fit a regression line through it\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "sns.regplot(x='medianValue_Ks', y='crimeRatePerCapita', ci=None,\n",
    "data=train_data, ax=ax, color='k', scatter_kws={\"s\": 20,\"color\":\\\n",
    "\"royalblue\", \"alpha\":1})\n",
    "ax.set_ylabel('Crime rate per Capita', fontsize=15, fontname='DejaVu Sans')\n",
    "ax.set_xlabel(\"Median value of owner-occupied homes in $1000's\",\\\n",
    "fontsize=15, fontname='DejaVu Sans')\n",
    "ax.set_xlim(left=None, right=None)\n",
    "ax.set_ylim(bottom=None, top=30)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ncyc8Kz9uvC6"
   },
   "source": [
    "### **Exercise 2.03: Examining a possible log-linear relationship using Python**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 441
    },
    "colab_type": "code",
    "id": "P1FzdmrsrkcU",
    "outputId": "15c55406-d4d6-44e0-ee17-54213bc6ad9c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the seaborn function regplot to create a log-linear plot and fit a regression line through it\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "y = np.log(train_data['crimeRatePerCapita'])\n",
    "sns.regplot(x='medianValue_Ks', y=y, ci=95, data=train_data, ax=ax,\\\n",
    "color='k', scatter_kws={\"s\": 20,\"color\": \"royalblue\", \"alpha\":1})\n",
    "ax.set_ylabel('log of Crime rate per Capita', fontsize=15,\\\n",
    "fontname='DejaVu Sans')\n",
    "ax.set_xlabel(\"Median value of owner-occupied homes in $1000's\",\\\n",
    "fontsize=15, fontname='DejaVu Sans')\n",
    "ax.set_xlim(left=None, right=None)\n",
    "ax.set_ylim(bottom=None, top=None)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LvamFp1BzZk2"
   },
   "source": [
    "### **Exercise 2.04: Fit a simple linear regression model using the Statsmodels formula API**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "eG0rTO_FrkqI",
    "outputId": "311c1272-3fad-4020-f45e-9d55a737533e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     crimeRatePerCapita   R-squared:                       0.144\n",
      "Model:                            OLS   Adj. R-squared:                  0.141\n",
      "Method:                 Least Squares   F-statistic:                     59.02\n",
      "Date:                Sat, 11 Jan 2020   Prob (F-statistic):           1.56e-13\n",
      "Time:                        17:34:53   Log-Likelihood:                -1217.4\n",
      "No. Observations:                 354   AIC:                             2439.\n",
      "Df Residuals:                     352   BIC:                             2447.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept         11.2094      1.079     10.386      0.000       9.087      13.332\n",
      "medianValue_Ks    -0.3502      0.046     -7.683      0.000      -0.440      -0.261\n",
      "==============================================================================\n",
      "Omnibus:                      447.354   Durbin-Watson:                   1.928\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            39791.431\n",
      "Skew:                           5.897   Prob(JB):                         0.00\n",
      "Kurtosis:                      53.583   Cond. No.                         63.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Use the statsmodels API to create a simple linear regression\n",
    "\n",
    "linearModel = smf.ols(formula='crimeRatePerCapita ~ medianValue_Ks',\\\n",
    "data=train_data)\n",
    "linearModelResult = linearModel.fit()\n",
    "print(linearModelResult.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MnnGgmBh0G2I"
   },
   "source": [
    "### **Activity 2.01: Fit a log-linear model using the Statsmodels formula API**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "PFG3qNdQzRRR",
    "outputId": "fb499a61-9a16-40be-e97c-d4f46a3b13a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                OLS Regression Results                                \n",
      "======================================================================================\n",
      "Dep. Variable:     np.log(crimeRatePerCapita)   R-squared:                       0.238\n",
      "Model:                                    OLS   Adj. R-squared:                  0.236\n",
      "Method:                         Least Squares   F-statistic:                     109.9\n",
      "Date:                        Sat, 11 Jan 2020   Prob (F-statistic):           1.48e-22\n",
      "Time:                                17:34:53   Log-Likelihood:                -727.67\n",
      "No. Observations:                         354   AIC:                             1459.\n",
      "Df Residuals:                             352   BIC:                             1467.\n",
      "Df Model:                                   1                                         \n",
      "Covariance Type:                    nonrobust                                         \n",
      "==================================================================================\n",
      "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Intercept          1.9107      0.271      7.062      0.000       1.379       2.443\n",
      "medianValue_Ks    -0.1198      0.011    -10.482      0.000      -0.142      -0.097\n",
      "==============================================================================\n",
      "Omnibus:                       11.420   Durbin-Watson:                   1.907\n",
      "Prob(Omnibus):                  0.003   Jarque-Bera (JB):               10.764\n",
      "Skew:                           0.376   Prob(JB):                      0.00460\n",
      "Kurtosis:                       2.594   Cond. No.                         63.7\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# Use the statsmodels API to create a log-linear regression model\n",
    "\n",
    "logLinearModel = smf.ols(formula='np.log(crimeRatePerCapita) ~ medianValue_Ks',\\\n",
    "data=train_data)\n",
    "logLinearModResult = logLinearModel.fit()\n",
    "print(logLinearModResult.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bySbtbZi2ApH"
   },
   "source": [
    "### **Exercise 2.05: Fit a multiple linear regression model using the Statsmodels formula API**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "colab_type": "code",
    "id": "cEuWdnJ0zRgM",
    "outputId": "536d1a68-f07a-40eb-c965-effacecf9e1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "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:                Sat, 11 Jan 2020   Prob (F-statistic):           2.19e-37\n",
      "Time:                        17:34:53   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"
     ]
    }
   ],
   "source": [
    "# Use the statsmodels API to create a multiple linear regression model\n",
    "\n",
    "multiLinearModel = smf.ols(formula=\\\n",
    "'crimeRatePerCapita ~ pctLowerStatus + radialHighwaysAccess +\\\n",
    "medianValue_Ks + nitrixOxide_pp10m', data=train_data)\n",
    "multiLinearModResult = multiLinearModel.fit()\n",
    "print(multiLinearModResult.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2bjo67Vj2kyj"
   },
   "source": [
    "### **Activity 2.02: Fit a multiple log-linear regression model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 629
    },
    "colab_type": "code",
    "id": "2P3Ta9Atp5eU",
    "outputId": "de0bc877-2a98-4eb0-ee8d-76c51c370087"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                OLS Regression Results                                \n",
      "======================================================================================\n",
      "Dep. Variable:     np.log(crimeRatePerCapita)   R-squared:                       0.884\n",
      "Model:                                    OLS   Adj. R-squared:                  0.881\n",
      "Method:                         Least Squares   F-statistic:                     261.5\n",
      "Date:                        Sat, 11 Jan 2020   Prob (F-statistic):          7.79e-154\n",
      "Time:                                17:34:53   Log-Likelihood:                -394.39\n",
      "No. Observations:                         354   AIC:                             810.8\n",
      "Df Residuals:                             343   BIC:                             853.3\n",
      "Df Model:                                  10                                         \n",
      "Covariance Type:                    nonrobust                                         \n",
      "==========================================================================================================\n",
      "                                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------------------\n",
      "Intercept                                 -5.4707      1.490     -3.671      0.000      -8.402      -2.540\n",
      "pctLowerStatus                             0.1541      0.049      3.161      0.002       0.058       0.250\n",
      "radialHighwaysAccess                       0.4697      0.052      9.070      0.000       0.368       0.572\n",
      "medianValue_Ks                            -0.1457      0.044     -3.325      0.001      -0.232      -0.059\n",
      "nitrixOxide_pp10m                          3.4509      3.000      1.150      0.251      -2.450       9.352\n",
      "pctLowerStatus:radialHighwaysAccess       -0.0006      0.001     -0.576      0.565      -0.003       0.002\n",
      "pctLowerStatus:medianValue_Ks             -0.0041      0.001     -4.159      0.000      -0.006      -0.002\n",
      "pctLowerStatus:nitrixOxide_pp10m          -0.0783      0.081     -0.964      0.336      -0.238       0.082\n",
      "radialHighwaysAccess:medianValue_Ks       -0.0027      0.001     -2.694      0.007      -0.005      -0.001\n",
      "radialHighwaysAccess:nitrixOxide_pp10m    -0.4234      0.066     -6.404      0.000      -0.553      -0.293\n",
      "medianValue_Ks:nitrixOxide_pp10m           0.3552      0.092      3.869      0.000       0.175       0.536\n",
      "==============================================================================\n",
      "Omnibus:                        4.124   Durbin-Watson:                   1.966\n",
      "Prob(Omnibus):                  0.127   Jarque-Bera (JB):                4.107\n",
      "Skew:                           0.175   Prob(JB):                        0.128\n",
      "Kurtosis:                       3.395   Cond. No.                     3.29e+04\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.29e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "# Use the statsmodels API to create a multiple log-linear regression model\n",
    "\n",
    "multiLogLinMod = smf.ols(formula=\\\n",
    "'np.log(crimeRatePerCapita) ~ \\\n",
    "(pctLowerStatus + radialHighwaysAccess + medianValue_Ks + nitrixOxide_pp10m)**2',\\\n",
    "data=train_data)\n",
    "multiLogLinModResult = multiLogLinMod.fit()\n",
    "print(multiLogLinModResult.summary())"
   ]
  }
 ],
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