{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression using statsmodels\n",
    "\n",
    "Author: Carlos J. Costa, ISEG\n",
    "\n",
    "Purpose: Identify the weight of several features in the home prices in Boston.\n",
    "\n",
    "**1** import libraries needed: sklearn and pandas\n",
    "\n",
    "**2** use boston dataset from https://scikit-learn.org/stable/datasets/index.html and convert into two dataframes: X for the features and Y for the target.\n",
    "\n",
    "**3** Verify 5 lines of the features variables\n",
    "\n",
    "**4** Veify datatype\n",
    "\n",
    "**5** Create new features variables with only 2 variables\n",
    "\n",
    "**6** Create and fit the model:model = sm.OLS(target,sm.add_constant(feature)).fit()\n",
    "\n",
    "**7** Obtain summary\n",
    "\n",
    "**8** What's the differnece between model.summary2().tables[2] and model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston =load_boston()\n",
    "X = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "Y = pd.DataFrame(boston.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = X.iloc[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1=sm.add_constant(X1)\n",
    "model = sm.OLS(Y,X1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}