{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 02\n",
    "\n",
    "Estimate a regression using the Income data\n",
    "\n",
    "\n",
    "## Forecast of income\n",
    "\n",
    "We'll be working with a dataset from US Census indome ([data dictionary](https://archive.ics.uci.edu/ml/datasets/Adult)).\n",
    "\n",
    "Many businesses would like to personalize their offer based on customer’s income. High-income customers could be, for instance, exposed to premium products. As a customer’s income is not always explicitly known, predictive model could estimate income of a person based on other information.\n",
    "\n",
    "Our goal is to create a predictive model that will be able to output an estimation of a person income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>Education</th>\n",
       "      <th>Education-Num</th>\n",
       "      <th>Martial Status</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Relationship</th>\n",
       "      <th>Race</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
       "      <th>Hours per week</th>\n",
       "      <th>Country</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>51806.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>68719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>51255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>47398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>30493.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age         Workclass  fnlwgt  Education  Education-Num  \\\n",
       "0   39         State-gov   77516  Bachelors             13   \n",
       "1   50  Self-emp-not-inc   83311  Bachelors             13   \n",
       "2   38           Private  215646    HS-grad              9   \n",
       "3   53           Private  234721       11th              7   \n",
       "4   28           Private  338409  Bachelors             13   \n",
       "\n",
       "       Martial Status         Occupation   Relationship   Race     Sex  \\\n",
       "0       Never-married       Adm-clerical  Not-in-family  White    Male   \n",
       "1  Married-civ-spouse    Exec-managerial        Husband  White    Male   \n",
       "2            Divorced  Handlers-cleaners  Not-in-family  White    Male   \n",
       "3  Married-civ-spouse  Handlers-cleaners        Husband  Black    Male   \n",
       "4  Married-civ-spouse     Prof-specialty           Wife  Black  Female   \n",
       "\n",
       "   Capital Gain  Capital Loss  Hours per week        Country   Income  \n",
       "0          2174             0              40  United-States  51806.0  \n",
       "1             0             0              13  United-States  68719.0  \n",
       "2             0             0              40  United-States  51255.0  \n",
       "3             0             0              40  United-States  47398.0  \n",
       "4             0             0              40           Cuba  30493.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# read the data and set the datetime as the index\n",
    "import zipfile\n",
    "with zipfile.ZipFile('../datasets/income.csv.zip', 'r') as z:\n",
    "    f = z.open('income.csv')\n",
    "    income = pd.read_csv(f, index_col=0)\n",
    "\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.1 \n",
    "\n",
    "What is the relation between the age and Income?\n",
    "\n",
    "For a one percent increase in the Age how much the income increases?\n",
    "\n",
    "Using sklearn estimate a linear regression and predict the income when the Age is 30 and 40 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d835da7f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7da5502cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "income.plot(x='Age', y='Income', kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.2\n",
    "Evaluate the model using the MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Exercise 2.3\n",
    "\n",
    "Run a regression model using as features the Age and Age$^2$ using the OLS equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.4\n",
    "\n",
    "\n",
    "Estimate a regression using more features.\n",
    "\n",
    "How is the performance compared to using only the Age?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2.5\n",
    "\n",
    "\n",
    "Estimate a logistic regression to predict if a person is in the United States.\n",
    "\n",
    "What is the performance of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    29170\n",
       "0.0     3391\n",
       "Name: isUS, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income['isUS'] = (income['Country'] == 'United-States')*1.0\n",
    "income['isUS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
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 },
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}