Data-Analytics/Adult census data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adult census data from 1996\n",
    "======\n",
    "\n",
    "In this notebook we will have a look at the [census income data from 1996](http://archive.ics.uci.edu/ml/datasets/Census+Income). The dataset can be found is part of the [UC Irvines ML repository](http://archive.ics.uci.edu/ml/). Specifically the data can be found at http://archive.ics.uci.edu/ml/machine-learning-databases/adult/."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python imports\n",
    "------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.style.use('ggplot')\n",
    "matplotlib.rc_params_from_file(\"../styles/matplotlibrc\" ).update()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning importing an munging\n",
    "------\n",
    "\n",
    "The data is a set of csv files that have been split into a training set and test set already. To support easy read in to a pandas DF we preprocess the files by adding a header that we get from the datasets metadata and adding an index column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "def indexAndAnnotateDataSet(filename):\n",
    "    outname = filename.replace('.csv', '_clean.csv')\n",
    "    with open(outname, 'wb') as csvfile:\n",
    "        f = open(filename,'r')\n",
    "        filewriter = csv.writer(csvfile, delimiter=',')\n",
    "        columns = ['', 'age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 'target']\n",
    "        filewriter.writerow(columns)\n",
    "        idx = 0\n",
    "        for line in f:\n",
    "            lst = []\n",
    "            lst.append(str(idx))\n",
    "            for item in line.strip('.').split(','):\n",
    "                lst.append(item.strip().strip('.'))\n",
    "\n",
    "            filewriter = csv.writer(csvfile, delimiter=',')\n",
    "            filewriter.writerow(lst)\n",
    "            idx +=1\n",
    "        f.close()\n",
    "    return outname"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the datasets are fairly small we download them here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import urllib\n",
    "\n",
    "\n",
    "url_train_dataset = 'http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
    "urllib.urlretrieve(url_train_dataset, 'adult.csv')\n",
    "\n",
    "url_test_dataset = 'http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data'\n",
    "urllib.urlretrieve(url_test_dataset, 'adult_test.csv')\n",
    "\n",
    "clean_DS_1 = indexAndAnnotateDataSet(os.getcwd() +'/adult.csv')\n",
    "clean_DS_2 = indexAndAnnotateDataSet(os.getcwd() +'/adult_test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use scikit-learn's built-in cross-validation methodes. Hence, we join the training and test data into a larger data frame to increase the size of the dataset. Moreover, we will drop rows that contain `na` feature and the column called 'fnlwgt' as this is a control value introduced by the original authors of the dataset.\n",
    "\n",
    "Inhaling the csv files and appending the data frames we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-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>native-country</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</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>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</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>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>?</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</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>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>?</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</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>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Japan</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>15020</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>?</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>45</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>?</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>?</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</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>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</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>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass     education  education-num  \\\n",
       "0       39         State-gov     Bachelors             13   \n",
       "1       50  Self-emp-not-inc     Bachelors             13   \n",
       "2       38           Private       HS-grad              9   \n",
       "3       53           Private          11th              7   \n",
       "4       28           Private     Bachelors             13   \n",
       "5       37           Private       Masters             14   \n",
       "6       49           Private           9th              5   \n",
       "7       52  Self-emp-not-inc       HS-grad              9   \n",
       "8       31           Private       Masters             14   \n",
       "9       42           Private     Bachelors             13   \n",
       "10      37           Private  Some-college             10   \n",
       "11      30         State-gov     Bachelors             13   \n",
       "12      23           Private     Bachelors             13   \n",
       "13      32           Private    Assoc-acdm             12   \n",
       "14      40           Private     Assoc-voc             11   \n",
       "15      34           Private       7th-8th              4   \n",
       "16      25  Self-emp-not-inc       HS-grad              9   \n",
       "17      32           Private       HS-grad              9   \n",
       "18      38           Private          11th              7   \n",
       "19      43  Self-emp-not-inc       Masters             14   \n",
       "20      40           Private     Doctorate             16   \n",
       "21      54           Private       HS-grad              9   \n",
       "22      35       Federal-gov           9th              5   \n",
       "23      43           Private          11th              7   \n",
       "24      59           Private       HS-grad              9   \n",
       "25      56         Local-gov     Bachelors             13   \n",
       "26      19           Private       HS-grad              9   \n",
       "27      54                 ?  Some-college             10   \n",
       "28      39           Private       HS-grad              9   \n",
       "29      49           Private       HS-grad              9   \n",
       "...    ...               ...           ...            ...   \n",
       "32531   30                 ?     Bachelors             13   \n",
       "32532   34           Private     Doctorate             16   \n",
       "32533   54           Private     Bachelors             13   \n",
       "32534   37           Private  Some-college             10   \n",
       "32535   22           Private          12th              8   \n",
       "32536   34           Private     Bachelors             13   \n",
       "32537   30           Private       HS-grad              9   \n",
       "32538   38           Private     Bachelors             13   \n",
       "32539   71                 ?     Doctorate             16   \n",
       "32540   45         State-gov       HS-grad              9   \n",
       "32541   41                 ?       HS-grad              9   \n",
       "32542   72                 ?       HS-grad              9   \n",
       "32543   45         Local-gov    Assoc-acdm             12   \n",
       "32544   31           Private       Masters             14   \n",
       "32545   39         Local-gov    Assoc-acdm             12   \n",
       "32546   37           Private    Assoc-acdm             12   \n",
       "32547   43           Private       HS-grad              9   \n",
       "32548   65  Self-emp-not-inc   Prof-school             15   \n",
       "32549   43         State-gov  Some-college             10   \n",
       "32550   43  Self-emp-not-inc  Some-college             10   \n",
       "32551   32           Private          10th              6   \n",
       "32552   43           Private     Assoc-voc             11   \n",
       "32553   32           Private       Masters             14   \n",
       "32554   53           Private       Masters             14   \n",
       "32555   22           Private  Some-college             10   \n",
       "32556   27           Private    Assoc-acdm             12   \n",
       "32557   40           Private       HS-grad              9   \n",
       "32558   58           Private       HS-grad              9   \n",
       "32559   22           Private       HS-grad              9   \n",
       "32560   52      Self-emp-inc       HS-grad              9   \n",
       "\n",
       "              marital-status         occupation    relationship  \\\n",
       "0              Never-married       Adm-clerical   Not-in-family   \n",
       "1         Married-civ-spouse    Exec-managerial         Husband   \n",
       "2                   Divorced  Handlers-cleaners   Not-in-family   \n",
       "3         Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "4         Married-civ-spouse     Prof-specialty            Wife   \n",
       "5         Married-civ-spouse    Exec-managerial            Wife   \n",
       "6      Married-spouse-absent      Other-service   Not-in-family   \n",
       "7         Married-civ-spouse    Exec-managerial         Husband   \n",
       "8              Never-married     Prof-specialty   Not-in-family   \n",
       "9         Married-civ-spouse    Exec-managerial         Husband   \n",
       "10        Married-civ-spouse    Exec-managerial         Husband   \n",
       "11        Married-civ-spouse     Prof-specialty         Husband   \n",
       "12             Never-married       Adm-clerical       Own-child   \n",
       "13             Never-married              Sales   Not-in-family   \n",
       "14        Married-civ-spouse       Craft-repair         Husband   \n",
       "15        Married-civ-spouse   Transport-moving         Husband   \n",
       "16             Never-married    Farming-fishing       Own-child   \n",
       "17             Never-married  Machine-op-inspct       Unmarried   \n",
       "18        Married-civ-spouse              Sales         Husband   \n",
       "19                  Divorced    Exec-managerial       Unmarried   \n",
       "20        Married-civ-spouse     Prof-specialty         Husband   \n",
       "21                 Separated      Other-service       Unmarried   \n",
       "22        Married-civ-spouse    Farming-fishing         Husband   \n",
       "23        Married-civ-spouse   Transport-moving         Husband   \n",
       "24                  Divorced       Tech-support       Unmarried   \n",
       "25        Married-civ-spouse       Tech-support         Husband   \n",
       "26             Never-married       Craft-repair       Own-child   \n",
       "27        Married-civ-spouse                  ?         Husband   \n",
       "28                  Divorced    Exec-managerial   Not-in-family   \n",
       "29        Married-civ-spouse       Craft-repair         Husband   \n",
       "...                      ...                ...             ...   \n",
       "32531          Never-married                  ?   Not-in-family   \n",
       "32532     Married-civ-spouse     Prof-specialty         Husband   \n",
       "32533     Married-civ-spouse    Exec-managerial         Husband   \n",
       "32534               Divorced       Adm-clerical       Unmarried   \n",
       "32535          Never-married    Protective-serv       Own-child   \n",
       "32536          Never-married    Exec-managerial   Not-in-family   \n",
       "32537          Never-married       Craft-repair   Not-in-family   \n",
       "32538               Divorced     Prof-specialty       Unmarried   \n",
       "32539     Married-civ-spouse                  ?         Husband   \n",
       "32540              Separated       Adm-clerical       Own-child   \n",
       "32541              Separated                  ?   Not-in-family   \n",
       "32542     Married-civ-spouse                  ?         Husband   \n",
       "32543               Divorced     Prof-specialty       Unmarried   \n",
       "32544               Divorced      Other-service   Not-in-family   \n",
       "32545     Married-civ-spouse       Adm-clerical            Wife   \n",
       "32546               Divorced       Tech-support   Not-in-family   \n",
       "32547     Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "32548          Never-married     Prof-specialty   Not-in-family   \n",
       "32549               Divorced       Adm-clerical  Other-relative   \n",
       "32550     Married-civ-spouse       Craft-repair         Husband   \n",
       "32551     Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "32552     Married-civ-spouse              Sales         Husband   \n",
       "32553          Never-married       Tech-support   Not-in-family   \n",
       "32554     Married-civ-spouse    Exec-managerial         Husband   \n",
       "32555          Never-married    Protective-serv   Not-in-family   \n",
       "32556     Married-civ-spouse       Tech-support            Wife   \n",
       "32557     Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "32558                Widowed       Adm-clerical       Unmarried   \n",
       "32559          Never-married       Adm-clerical       Own-child   \n",
       "32560     Married-civ-spouse    Exec-managerial            Wife   \n",
       "\n",
       "                     race     sex  capital-gain  capital-loss  hours-per-week  \\\n",
       "0                   White    Male          2174             0              40   \n",
       "1                   White    Male             0             0              13   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male             0             0              40   \n",
       "4                   Black  Female             0             0              40   \n",
       "5                   White  Female             0             0              40   \n",
       "6                   Black  Female             0             0              16   \n",
       "7                   White    Male             0             0              45   \n",
       "8                   White  Female         14084             0              50   \n",
       "9                   White    Male          5178             0              40   \n",
       "10                  Black    Male             0             0              80   \n",
       "11     Asian-Pac-Islander    Male             0             0              40   \n",
       "12                  White  Female             0             0              30   \n",
       "13                  Black    Male             0             0              50   \n",
       "14     Asian-Pac-Islander    Male             0             0              40   \n",
       "15     Amer-Indian-Eskimo    Male             0             0              45   \n",
       "16                  White    Male             0             0              35   \n",
       "17                  White    Male             0             0              40   \n",
       "18                  White    Male             0             0              50   \n",
       "19                  White  Female             0             0              45   \n",
       "20                  White    Male             0             0              60   \n",
       "21                  Black  Female             0             0              20   \n",
       "22                  Black    Male             0             0              40   \n",
       "23                  White    Male             0          2042              40   \n",
       "24                  White  Female             0             0              40   \n",
       "25                  White    Male             0             0              40   \n",
       "26                  White    Male             0             0              40   \n",
       "27     Asian-Pac-Islander    Male             0             0              60   \n",
       "28                  White    Male             0             0              80   \n",
       "29                  White    Male             0             0              40   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "32531  Asian-Pac-Islander  Female             0             0              99   \n",
       "32532               White    Male             0             0              60   \n",
       "32533  Asian-Pac-Islander    Male             0             0              50   \n",
       "32534               White  Female             0             0              39   \n",
       "32535               Black    Male             0             0              35   \n",
       "32536               White  Female             0             0              55   \n",
       "32537               Black    Male             0             0              46   \n",
       "32538               Black  Female         15020             0              45   \n",
       "32539               White    Male             0             0              10   \n",
       "32540               White  Female             0             0              40   \n",
       "32541               Black  Female             0             0              32   \n",
       "32542               White    Male             0             0              25   \n",
       "32543               White  Female             0             0              48   \n",
       "32544               Other  Female             0             0              30   \n",
       "32545               White  Female             0             0              20   \n",
       "32546               White  Female             0             0              40   \n",
       "32547               White    Male             0             0              40   \n",
       "32548               White    Male          1086             0              60   \n",
       "32549               White  Female             0             0              40   \n",
       "32550               White    Male             0             0              50   \n",
       "32551  Amer-Indian-Eskimo    Male             0             0              40   \n",
       "32552               White    Male             0             0              45   \n",
       "32553  Asian-Pac-Islander    Male             0             0              11   \n",
       "32554               White    Male             0             0              40   \n",
       "32555               White    Male             0             0              40   \n",
       "32556               White  Female             0             0              38   \n",
       "32557               White    Male             0             0              40   \n",
       "32558               White  Female             0             0              40   \n",
       "32559               White    Male             0             0              20   \n",
       "32560               White  Female         15024             0              40   \n",
       "\n",
       "      native-country target  \n",
       "0      United-States  <=50K  \n",
       "1      United-States  <=50K  \n",
       "2      United-States  <=50K  \n",
       "3      United-States  <=50K  \n",
       "4               Cuba  <=50K  \n",
       "5      United-States  <=50K  \n",
       "6            Jamaica  <=50K  \n",
       "7      United-States   >50K  \n",
       "8      United-States   >50K  \n",
       "9      United-States   >50K  \n",
       "10     United-States   >50K  \n",
       "11             India   >50K  \n",
       "12     United-States  <=50K  \n",
       "13     United-States  <=50K  \n",
       "14                 ?   >50K  \n",
       "15            Mexico  <=50K  \n",
       "16     United-States  <=50K  \n",
       "17     United-States  <=50K  \n",
       "18     United-States  <=50K  \n",
       "19     United-States   >50K  \n",
       "20     United-States   >50K  \n",
       "21     United-States  <=50K  \n",
       "22     United-States  <=50K  \n",
       "23     United-States  <=50K  \n",
       "24     United-States  <=50K  \n",
       "25     United-States   >50K  \n",
       "26     United-States  <=50K  \n",
       "27             South   >50K  \n",
       "28     United-States  <=50K  \n",
       "29     United-States  <=50K  \n",
       "...              ...    ...  \n",
       "32531  United-States  <=50K  \n",
       "32532  United-States   >50K  \n",
       "32533          Japan   >50K  \n",
       "32534  United-States  <=50K  \n",
       "32535  United-States  <=50K  \n",
       "32536  United-States   >50K  \n",
       "32537  United-States  <=50K  \n",
       "32538  United-States   >50K  \n",
       "32539  United-States   >50K  \n",
       "32540  United-States  <=50K  \n",
       "32541  United-States  <=50K  \n",
       "32542  United-States  <=50K  \n",
       "32543  United-States  <=50K  \n",
       "32544  United-States  <=50K  \n",
       "32545  United-States   >50K  \n",
       "32546  United-States  <=50K  \n",
       "32547         Mexico  <=50K  \n",
       "32548  United-States  <=50K  \n",
       "32549  United-States  <=50K  \n",
       "32550  United-States  <=50K  \n",
       "32551  United-States  <=50K  \n",
       "32552  United-States  <=50K  \n",
       "32553         Taiwan  <=50K  \n",
       "32554  United-States   >50K  \n",
       "32555  United-States  <=50K  \n",
       "32556  United-States  <=50K  \n",
       "32557  United-States   >50K  \n",
       "32558  United-States  <=50K  \n",
       "32559  United-States  <=50K  \n",
       "32560  United-States   >50K  \n",
       "\n",
       "[32561 rows x 14 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df = pd.DataFrame.from_csv(clean_DS_1).dropna().drop('fnlwgt',1)\n",
    "raw_test_df = pd.DataFrame.from_csv(clean_DS_2).dropna().drop('fnlwgt',1)\n",
    "raw_data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the 'education' and 'education-num' column we might suspect duplicate information. Let's check for that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>Preschool</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1st-4th</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>5th-6th</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        education  education-num\n",
       "224     Preschool              1\n",
       "160       1st-4th              2\n",
       "56        5th-6th              3\n",
       "15        7th-8th              4\n",
       "6             9th              5\n",
       "77           10th              6\n",
       "3            11th              7\n",
       "415          12th              8\n",
       "2         HS-grad              9\n",
       "10   Some-college             10\n",
       "14      Assoc-voc             11\n",
       "13     Assoc-acdm             12\n",
       "0       Bachelors             13\n",
       "5         Masters             14\n",
       "52    Prof-school             15\n",
       "20      Doctorate             16"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data_df[['education', 'education-num']].drop_duplicates().sort('education-num')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As suspected the two columns are mere duplicates of each other to cast the strings into integer categoricals. In the following we will drop the 'education' column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parsing the categoricals to integers\n",
    "------\n",
    "\n",
    "All scikit-learn classification algorithms expect numerical features as input. We will hence cast the categoricals to integers and keep the lists areound to resolve classes later on if necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_of_catList= []\n",
    "list_of_catList.append(raw_data_df.workclass.unique())\n",
    "list_of_catList.append(raw_data_df['marital-status'].unique())\n",
    "list_of_catList.append(raw_data_df.occupation.unique())\n",
    "list_of_catList.append(raw_data_df.relationship.unique())\n",
    "list_of_catList.append(raw_data_df.race.unique())\n",
    "list_of_catList.append(raw_data_df.sex.unique())\n",
    "list_of_catList.append(raw_data_df['native-country'].unique())\n",
    "\n",
    "def cleanFeatureDF(feature_df):\n",
    "    df = feature_df\n",
    "    for lst in list_of_catList:\n",
    "        for cat in lst:\n",
    "            df = df.replace(cat, lst.tolist().index(cat))\n",
    "    return df\n",
    "\n",
    "def cleanTargetDF(target_df):\n",
    "    df = target_df\n",
    "    list_of_target = target_df.unique()\n",
    "    for target in list_of_target:\n",
    "        df = df.replace(target, list_of_target.tolist().index(target))\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "numerical_data_df = cleanFeatureDF(raw_data_df.drop('target',1).drop('education',1))\n",
    "numerical_data_df['target'] = cleanTargetDF(raw_data_df.target)\n",
    "\n",
    "numerical_test_df = cleanFeatureDF(raw_test_df.drop('target',1).drop('education',1))\n",
    "numerical_test_df['target'] = cleanTargetDF(raw_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feeding the learning algorithms\n",
    "------\n",
    "\n",
    "As we are dealing with a very dense dataset containing mostly categoricals we might assume that tree classifiers should do well on this problem, so we will look into training a simple tree and a random forest.\n",
    "\n",
    "But before that we need to split our data frame into training features and the target variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_feature_df = numerical_data_df.drop('target',1)\n",
    "train_target_df = numerical_data_df.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Learning a tree\n",
    "\n",
    "Let's learn a simple tree using scikit-learn's [tree classifier](http://scikit-learn.org/stable/modules/tree.html#classification)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import tree\n",
    "from sklearn.learning_curve import learning_curve\n",
    "from sklearn.learning_curve import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tree_clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we are interested how the naive tree is doing in learning our training set. To this end we record the learning curve of our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plotLearningCurve(classifier, feature_df, target_df, train_set_sizes):\n",
    "    train_sizes, train_scores, test_scores = learning_curve(classifier,\n",
    "                                                            feature_df,\n",
    "                                                            target_df,\n",
    "                                                            train_sizes=train_set_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5eee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_sizes=np.linspace(.1, 1.0, 15)\n",
    "plotLearningCurve(tree_clf, train_feature_df, train_target_df, train_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our learning curve is pretty bad. The scores almost don't change at all with varying the data set size. Moreover the curve shows a large gap which hints to a large bias or overfitting of the data set. To improve on the learning we need to investigate the hyper-parameter space for the tree, which in this case is the tree depth. scikit-learn's tree does not impose any maximal tree depth, so let's look at the validation curves using 'accuracy' as a score in the dpeth hyper-parameter space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def plotValidationAccuracyCurve(classifier, feature_df, target_df, param_name, param_range):\n",
    "    train_scores, test_scores = validation_curve(classifier,\n",
    "                                                 feature_df,\n",
    "                                                 target_df,\n",
    "                                                 param_name=param_name,\n",
    "                                                 param_range=param_range,\n",
    "                                                 cv=10,\n",
    "                                                 scoring=\"accuracy\",\n",
    "                                                 n_jobs=1)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "    plt.plot(param_range, train_scores_mean, 'o-', label=\"Training score\", color=\"r\")\n",
    "    plt.fill_between(param_range, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.2, color=\"r\")\n",
    "    plt.plot(param_range, test_scores_mean, 'o-', label=\"Cross-validation score\",\n",
    "                 color=\"g\")\n",
    "    plt.fill_between(param_range, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\n",
    "    plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f5ee4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "param_range = range(2,17)\n",
    "plotValidationAccuracyCurve(tree_clf, train_feature_df, train_target_df, 'max_depth', param_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeed we see a sweet spot at around depth 12, after which the cross-validation score starts to drop. We will fix the classifier at depth 12 and learn the final classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(compute_importances=None, criterion='gini',\n",
       "            max_depth=12, max_features=None, max_leaf_nodes=None,\n",
       "            min_density=None, min_samples_leaf=1, min_samples_split=2,\n",
       "            random_state=None, splitter='best')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_clf.max_depth = 12\n",
    "tree_clf.fit(train_feature_df, train_target_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Training a Random Forest\n",
    "\n",
    "RFs have the advantage that their inherent randomness counter-acts the learning of train-set intrisic patterns which don't generalize. They tend not to overfit and are stable against outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rf_clf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start with having a look at their learning behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d764610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_sizes=np.linspace(.1, 1.0, 15)\n",
    "plotLearningCurve(rf_clf, train_feature_df, train_target_df, train_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We face the same problems as with the single tree, hence we need to dig into the hyper-parameter space. We explore the maximal depth and the number of trees in the forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cef1a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Validation curve to find optimal max_depth of trees\n",
    "param_range = range(2,17)\n",
    "plotValidationAccuracyCurve(rf_clf, train_feature_df, train_target_df, 'max_depth', param_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find the optimal value at around depth 14. Let's look for the optimal forest size at that maximal depth (Note: we might only find a local optimum or non at all. To find an actual optimal point we would need a grid search in hyper-parameter space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f40d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Validation curve to find optimal number of trees\n",
    "param_range = range(2,17)\n",
    "rf_clf.max_depth = 14\n",
    "plotValidationAccuracyCurve(rf_clf, train_feature_df, train_target_df, 'n_estimators', param_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that around a number of 9 trees the scores start to saturate, so we can choose this value as a sweet spot. Let's see how our learning curve looks at this point in hyper-parameter space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf33090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_sizes=np.linspace(.1, 1.0, 15)\n",
    "rf_clf.max_depth = 14\n",
    "rf_clf.n_estimators = 9\n",
    "plotLearningCurve(rf_clf, train_feature_df, train_target_df, train_sizes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Much better! We see that the curves are starting to converge and haven't saturated yet. We hence reduced our bias and it seems that increasing the data-set size might even improve our classifier.\n",
    "\n",
    "Finally let's learn the final RF classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, compute_importances=None,\n",
       "            criterion='gini', max_depth=14, max_features='auto',\n",
       "            max_leaf_nodes=None, min_density=None, min_samples_leaf=1,\n",
       "            min_samples_split=2, n_estimators=9, n_jobs=1, oob_score=False,\n",
       "            random_state=None, verbose=0)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_clf.max_depth = 14\n",
    "rf_clf.n_estimators = 9\n",
    "rf_clf.fit(train_feature_df, train_target_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpreting the models\n",
    "------\n",
    "\n",
    "To understand the differences in the model of a random forest vs. the single tree let us try to interpret their predictions.\n",
    "\n",
    "### Performance on the test set\n",
    "A first step is to see how well the two models do on the test set using various scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree accuracy: 0.899\n",
      "RF accuracy: 0.886\n",
      "\n",
      "\n",
      "Tree confusion matrix: \n",
      "[[23621  1099]\n",
      " [ 2198  5643]]\n",
      "RF confusion matrix: \n",
      "[[23803   917]\n",
      " [ 2810  5031]]\n"
     ]
    }
   ],
   "source": [
    "tree_prediction = tree_clf.predict(numerical_test_df.drop('target',1))\n",
    "tree_confusion_matrix = confusion_matrix(numerical_test_df.target, tree_prediction)\n",
    "tree_accuracy = accuracy_score(numerical_test_df.target, tree_prediction)\n",
    "\n",
    "rf_prediction = rf_clf.predict(numerical_test_df.drop('target',1))\n",
    "rf_confusion_matrix = confusion_matrix(numerical_test_df.target, rf_prediction)\n",
    "rf_accuracy = accuracy_score(numerical_test_df.target, rf_prediction)\n",
    "\n",
    "print \"Tree accuracy: %0.3f\" % tree_accuracy\n",
    "print \"RF accuracy: %0.3f\" % rf_accuracy\n",
    "print \"\\n\"\n",
    "print \"Tree confusion matrix: \\n\", tree_confusion_matrix\n",
    "print \"RF confusion matrix: \\n\", rf_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the single tree is slighlty more accurate, but that both classifiers perform close to 90% accurate. Looking at the confusion matrix it becomes obvious that the RF is more careful in classifying positives as the total number of false positives is smaller. Let's look at some more metrics to draw a final conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree precision: 0.837\n",
      "RF precision: 0.846\n",
      "\n",
      "\n",
      "Tree recall: 0.720\n",
      "RF recall: 0.642\n"
     ]
    }
   ],
   "source": [
    "print \"Tree precision: %0.3f\" % precision_score(numerical_test_df.target, tree_prediction)\n",
    "print \"RF precision: %0.3f\" % precision_score(numerical_test_df.target, rf_prediction)\n",
    "print \"\\n\"\n",
    "print \"Tree recall: %0.3f\" % recall_score(numerical_test_df.target, tree_prediction)\n",
    "print \"RF recall: %0.3f\" % recall_score(numerical_test_df.target, rf_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As already suspected. The RF is more precise than the single tree in terms of assiging the correct class. However the single tree is outperforming the RF in terms of finding true positives in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Features of the classifiers?\n",
    "\n",
    "Finally it is important to understand what features the classifiers learned. In the case of our linear model this will still be easier to interpret. Let's have a look at the `feature_importances_` of the classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1109317d0>"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110311a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_imp_df = pd.DataFrame()\n",
    "feature_imp_df['feature'] = train_feature_df.columns\n",
    "feature_imp_df['tree_importance'] = tree_clf.feature_importances_/tree_clf.feature_importances_.sum()\n",
    "feature_imp_df['rf_importance'] = rf_clf.feature_importances_/rf_clf.feature_importances_.sum()\n",
    "feature_imp_df.sort('rf_importance', ascending = True).plot(x='feature', kind='barh', figsize=(10,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We immediately see that the tree as well as the RF share the three most important features 'capital-gain', 'education-num' and 'marital-status' to be indicative of whether or not the income is larger than 50K. However the single tree places slightly more importance on those features than the RF which might be connected to some overfitting due to missing randomness that the RF has. Hover the 4th feature is interesting! RF indicates that 'relationship' is a strong indicator of large income, whereas the single tree completely dismisses this feature as unimportant.\n",
    "\n",
    "Let's look at this feature in more detail!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Not-in-family' 'Husband' 'Wife' 'Own-child' 'Unmarried' 'Other-relative']\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117b35bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "AABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117118150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marital_status_list = list_of_catList[3]\n",
    "df_to_plot = numerical_data_df[['relationship','target']]\n",
    "\n",
    "df_to_plot[df_to_plot.target ==1].hist()\n",
    "df_to_plot[df_to_plot.target ==0].hist()\n",
    "print marital_status_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this is indeed a feature! Not being in the relationship status 'Husband' significantly decreases your chance of making more than 50K a year! Husband's themselves are relatively safe since their status appears mostly uncorrelated with the money they earn. Intuitively it is clear that RF could pick this up since every tree in the forest might split on a different value of the 'relationship' category and the final voting will outweight the insignificant 'husband' value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summary\n",
    "------\n",
    "\n",
    "Though the analysis done here is fairly trivial it turns out that using two simple classifiers we are already able to gain insight into a interesting feature. Namley that not being in a relationship status 'Husband' significantly reduces the chances of making more than 50K of income a year. For husbands themselves however, the status is non-decisive. THe feature was detected using a Random Forest classifier but could not be picked up using a single Decision tree.\n",
    "\n",
    "I hope you find this notebook useful and it motivates you to play some more with it. Contributions are always welcome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Miscellenea\n",
    "\n",
    "####References\n",
    "are mostly given throughout the text. But importantly\n",
    "\n",
    "- [1] [scikit-learn](http://scikit-learn.org/stable/index.html)\n",
    "\n",
    "####Stylesheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "        font-style: oblique;\n",
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       "    }\n",
       "    @font-face {\n",
       "        font-family: \"Computer Modern\";\n",
       "        font-weight: bold;\n",
       "        font-style: oblique;\n",
       "        src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
       "    }\n",
       "    div.cell{\n",
       "        width:800px;\n",
       "        margin-left:16% !important;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    h1 {\n",
       "        font-family: Helvetica, serif;\n",
       "    }\n",
       "    h4{\n",
       "        margin-top:12px;\n",
       "        margin-bottom: 3px;\n",
       "       }\n",
       "    div.text_cell_render{\n",
       "        font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
       "        line-height: 145%;\n",
       "        font-size: 130%;\n",
       "        width:800px;\n",
       "        margin-left:auto;\n",
       "        margin-right:auto;\n",
       "    }\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
       "    }\n",
       "    .prompt{\n",
       "        display: None;\n",
       "    }\n",
       "    .text_cell_render h5 {\n",
       "        font-weight: 300;\n",
       "        font-size: 22pt;\n",
       "        color: #4057A1;\n",
       "        font-style: italic;\n",
       "        margin-bottom: .5em;\n",
       "        margin-top: 0.5em;\n",
       "        display: block;\n",
       "    }\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "\n",
    "def css_styling():\n",
    "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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