{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AbsjF_-0Lort"
   },
   "source": [
    "**Import the necessary modules and prepare the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k5QR71xFLGkh"
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RpkR60AxLNkl"
   },
   "outputs": [],
   "source": [
    "rawBostonData = pd.read_csv('../Dataset/Boston.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Qwjq3ODLWUF"
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "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",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 493
    },
    "colab_type": "code",
    "id": "o_CbjkexLfyy",
    "outputId": "bb48461b-d6aa-43f3-d810-bc3d520928ad"
   },
   "outputs": [],
   "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())"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Exercise2_05.ipynb",
   "provenance": []
  },
  "kernelspec": {
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   "language": "python",
   "name": "python3"
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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