TensorFlow 7 Linear Regression - tflearn.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.3.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__\n",
    "#Python 3.5 with tensorflow 1.3.1\n",
    "#conda install python=3.5\n",
    "#conda install -c conda-forge tensorflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'tensorflow.python.estimator.estimator_lib' from 'C:\\\\Users\\\\ZekeLabs\\\\Anaconda3-N\\\\lib\\\\site-packages\\\\tensorflow\\\\python\\\\estimator\\\\estimator_lib.py'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TensorFlow’s high-level machine learning API (tf.estimator) makes it easy to configure, train, and evaluate a variety of machine learning models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "CSV_COLUMNS = [\n",
    "    \"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\",\n",
    "    \"marital_status\", \"occupation\", \"relationship\", \"race\", \"gender\",\n",
    "    \"capital_gain\", \"capital_loss\", \"hours_per_week\", \"native_country\",\n",
    "    \"income_bracket\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('adult.data.txt', names=CSV_COLUMNS, skipinitialspace=True)\n",
    "df_test = pd.read_csv('adult.test.txt', names=CSV_COLUMNS, skipinitialspace=True, skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "    <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>marital_status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>capital_gain</th>\n",
       "      <th>capital_loss</th>\n",
       "      <th>hours_per_week</th>\n",
       "      <th>native_country</th>\n",
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       "      <td>226802</td>\n",
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       "      <td>38</td>\n",
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       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
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       "      <td>United-States</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
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       "      <td>Male</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</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",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>198693</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>?</td>\n",
       "      <td>227026</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</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",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>63</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>104626</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
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       "      <td>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>24</td>\n",
       "      <td>Private</td>\n",
       "      <td>369667</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</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",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>104996</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</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>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>65</td>\n",
       "      <td>Private</td>\n",
       "      <td>184454</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>6418</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>11</th>\n",
       "      <td>36</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>212465</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26</td>\n",
       "      <td>Private</td>\n",
       "      <td>82091</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</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>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>58</td>\n",
       "      <td>?</td>\n",
       "      <td>299831</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
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       "      <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>14</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>279724</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>3103</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>346189</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>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>20</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>444554</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</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>17</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>128354</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>60548</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</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>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>85019</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>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>107914</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>47</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>238588</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</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>22</th>\n",
       "      <td>72</td>\n",
       "      <td>?</td>\n",
       "      <td>132015</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>?</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>6</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>220931</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>Peru</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>205947</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>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>25</th>\n",
       "      <td>45</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>432824</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>7298</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>236427</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>27</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>134446</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>99516</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>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>109282</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</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>0</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>...</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16251</th>\n",
       "      <td>81</td>\n",
       "      <td>?</td>\n",
       "      <td>26711</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2936</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>16252</th>\n",
       "      <td>60</td>\n",
       "      <td>Private</td>\n",
       "      <td>117909</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</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>7688</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>16253</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>229647</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</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>1669</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16254</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>149347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</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>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16255</th>\n",
       "      <td>43</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>23157</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>1902</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16256</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>93977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</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>16257</th>\n",
       "      <td>73</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>159691</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</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>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16258</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>176967</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</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>16259</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>344436</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16260</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>430340</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</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>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16261</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>202168</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</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>15024</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>16262</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>82720</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>16263</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>269623</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</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>16264</th>\n",
       "      <td>64</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>136405</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Farming-fishing</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>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16265</th>\n",
       "      <td>50</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>139347</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</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>?</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16266</th>\n",
       "      <td>55</td>\n",
       "      <td>Private</td>\n",
       "      <td>224655</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Priv-house-serv</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>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16267</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>247547</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</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>16268</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>292710</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</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>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16269</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>173449</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</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>16270</th>\n",
       "      <td>48</td>\n",
       "      <td>Private</td>\n",
       "      <td>285570</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</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>16271</th>\n",
       "      <td>61</td>\n",
       "      <td>Private</td>\n",
       "      <td>89686</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</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>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16272</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>440129</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>16273</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>350977</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</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>16274</th>\n",
       "      <td>48</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>349230</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>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>16275</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>245211</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</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>16276</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>215419</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</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>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16277</th>\n",
       "      <td>64</td>\n",
       "      <td>?</td>\n",
       "      <td>321403</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Other-relative</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>16278</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>374983</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>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>16279</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>83891</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</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>16280</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>182148</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>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16281 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  fnlwgt     education  education_num  \\\n",
       "0       25           Private  226802          11th              7   \n",
       "1       38           Private   89814       HS-grad              9   \n",
       "2       28         Local-gov  336951    Assoc-acdm             12   \n",
       "3       44           Private  160323  Some-college             10   \n",
       "4       18                 ?  103497  Some-college             10   \n",
       "5       34           Private  198693          10th              6   \n",
       "6       29                 ?  227026       HS-grad              9   \n",
       "7       63  Self-emp-not-inc  104626   Prof-school             15   \n",
       "8       24           Private  369667  Some-college             10   \n",
       "9       55           Private  104996       7th-8th              4   \n",
       "10      65           Private  184454       HS-grad              9   \n",
       "11      36       Federal-gov  212465     Bachelors             13   \n",
       "12      26           Private   82091       HS-grad              9   \n",
       "13      58                 ?  299831       HS-grad              9   \n",
       "14      48           Private  279724       HS-grad              9   \n",
       "15      43           Private  346189       Masters             14   \n",
       "16      20         State-gov  444554  Some-college             10   \n",
       "17      43           Private  128354       HS-grad              9   \n",
       "18      37           Private   60548       HS-grad              9   \n",
       "19      40           Private   85019     Doctorate             16   \n",
       "20      34           Private  107914     Bachelors             13   \n",
       "21      34           Private  238588  Some-college             10   \n",
       "22      72                 ?  132015       7th-8th              4   \n",
       "23      25           Private  220931     Bachelors             13   \n",
       "24      25           Private  205947     Bachelors             13   \n",
       "25      45  Self-emp-not-inc  432824       HS-grad              9   \n",
       "26      22           Private  236427       HS-grad              9   \n",
       "27      23           Private  134446       HS-grad              9   \n",
       "28      54           Private   99516       HS-grad              9   \n",
       "29      32  Self-emp-not-inc  109282  Some-college             10   \n",
       "...    ...               ...     ...           ...            ...   \n",
       "16251   81                 ?   26711     Assoc-voc             11   \n",
       "16252   60           Private  117909     Assoc-voc             11   \n",
       "16253   39           Private  229647     Bachelors             13   \n",
       "16254   38           Private  149347       Masters             14   \n",
       "16255   43         Local-gov   23157       Masters             14   \n",
       "16256   23           Private   93977       HS-grad              9   \n",
       "16257   73      Self-emp-inc  159691  Some-college             10   \n",
       "16258   35           Private  176967  Some-college             10   \n",
       "16259   66           Private  344436       HS-grad              9   \n",
       "16260   27           Private  430340  Some-college             10   \n",
       "16261   40           Private  202168   Prof-school             15   \n",
       "16262   51           Private   82720       HS-grad              9   \n",
       "16263   22           Private  269623  Some-college             10   \n",
       "16264   64  Self-emp-not-inc  136405       HS-grad              9   \n",
       "16265   50         Local-gov  139347       Masters             14   \n",
       "16266   55           Private  224655       HS-grad              9   \n",
       "16267   38           Private  247547     Assoc-voc             11   \n",
       "16268   58           Private  292710    Assoc-acdm             12   \n",
       "16269   32           Private  173449       HS-grad              9   \n",
       "16270   48           Private  285570       HS-grad              9   \n",
       "16271   61           Private   89686       HS-grad              9   \n",
       "16272   31           Private  440129       HS-grad              9   \n",
       "16273   25           Private  350977       HS-grad              9   \n",
       "16274   48         Local-gov  349230       Masters             14   \n",
       "16275   33           Private  245211     Bachelors             13   \n",
       "16276   39           Private  215419     Bachelors             13   \n",
       "16277   64                 ?  321403       HS-grad              9   \n",
       "16278   38           Private  374983     Bachelors             13   \n",
       "16279   44           Private   83891     Bachelors             13   \n",
       "16280   35      Self-emp-inc  182148     Bachelors             13   \n",
       "\n",
       "           marital_status         occupation    relationship  \\\n",
       "0           Never-married  Machine-op-inspct       Own-child   \n",
       "1      Married-civ-spouse    Farming-fishing         Husband   \n",
       "2      Married-civ-spouse    Protective-serv         Husband   \n",
       "3      Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "4           Never-married                  ?       Own-child   \n",
       "5           Never-married      Other-service   Not-in-family   \n",
       "6           Never-married                  ?       Unmarried   \n",
       "7      Married-civ-spouse     Prof-specialty         Husband   \n",
       "8           Never-married      Other-service       Unmarried   \n",
       "9      Married-civ-spouse       Craft-repair         Husband   \n",
       "10     Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "11     Married-civ-spouse       Adm-clerical         Husband   \n",
       "12          Never-married       Adm-clerical   Not-in-family   \n",
       "13     Married-civ-spouse                  ?         Husband   \n",
       "14     Married-civ-spouse  Machine-op-inspct         Husband   \n",
       "15     Married-civ-spouse    Exec-managerial         Husband   \n",
       "16          Never-married      Other-service       Own-child   \n",
       "17     Married-civ-spouse       Adm-clerical            Wife   \n",
       "18                Widowed  Machine-op-inspct       Unmarried   \n",
       "19     Married-civ-spouse     Prof-specialty         Husband   \n",
       "20     Married-civ-spouse       Tech-support         Husband   \n",
       "21          Never-married      Other-service       Own-child   \n",
       "22               Divorced                  ?   Not-in-family   \n",
       "23          Never-married     Prof-specialty   Not-in-family   \n",
       "24     Married-civ-spouse     Prof-specialty         Husband   \n",
       "25     Married-civ-spouse       Craft-repair         Husband   \n",
       "26          Never-married       Adm-clerical       Own-child   \n",
       "27              Separated  Machine-op-inspct       Unmarried   \n",
       "28     Married-civ-spouse       Craft-repair         Husband   \n",
       "29          Never-married     Prof-specialty   Not-in-family   \n",
       "...                   ...                ...             ...   \n",
       "16251  Married-civ-spouse                  ?         Husband   \n",
       "16252  Married-civ-spouse     Prof-specialty         Husband   \n",
       "16253       Never-married       Tech-support   Not-in-family   \n",
       "16254  Married-civ-spouse     Prof-specialty         Husband   \n",
       "16255  Married-civ-spouse    Exec-managerial         Husband   \n",
       "16256       Never-married  Machine-op-inspct       Own-child   \n",
       "16257            Divorced    Exec-managerial   Not-in-family   \n",
       "16258  Married-civ-spouse    Protective-serv         Husband   \n",
       "16259             Widowed              Sales  Other-relative   \n",
       "16260       Never-married              Sales   Not-in-family   \n",
       "16261  Married-civ-spouse     Prof-specialty         Husband   \n",
       "16262  Married-civ-spouse       Craft-repair         Husband   \n",
       "16263       Never-married       Craft-repair       Own-child   \n",
       "16264             Widowed    Farming-fishing   Not-in-family   \n",
       "16265  Married-civ-spouse     Prof-specialty            Wife   \n",
       "16266           Separated    Priv-house-serv   Not-in-family   \n",
       "16267       Never-married       Adm-clerical       Unmarried   \n",
       "16268            Divorced     Prof-specialty   Not-in-family   \n",
       "16269  Married-civ-spouse  Handlers-cleaners         Husband   \n",
       "16270  Married-civ-spouse       Adm-clerical         Husband   \n",
       "16271  Married-civ-spouse              Sales         Husband   \n",
       "16272  Married-civ-spouse       Craft-repair         Husband   \n",
       "16273       Never-married      Other-service       Own-child   \n",
       "16274            Divorced      Other-service   Not-in-family   \n",
       "16275       Never-married     Prof-specialty       Own-child   \n",
       "16276            Divorced     Prof-specialty   Not-in-family   \n",
       "16277             Widowed                  ?  Other-relative   \n",
       "16278  Married-civ-spouse     Prof-specialty         Husband   \n",
       "16279            Divorced       Adm-clerical       Own-child   \n",
       "16280  Married-civ-spouse    Exec-managerial         Husband   \n",
       "\n",
       "                     race  gender  capital_gain  capital_loss  hours_per_week  \\\n",
       "0                   Black    Male             0             0              40   \n",
       "1                   White    Male             0             0              50   \n",
       "2                   White    Male             0             0              40   \n",
       "3                   Black    Male          7688             0              40   \n",
       "4                   White  Female             0             0              30   \n",
       "5                   White    Male             0             0              30   \n",
       "6                   Black    Male             0             0              40   \n",
       "7                   White    Male          3103             0              32   \n",
       "8                   White  Female             0             0              40   \n",
       "9                   White    Male             0             0              10   \n",
       "10                  White    Male          6418             0              40   \n",
       "11                  White    Male             0             0              40   \n",
       "12                  White  Female             0             0              39   \n",
       "13                  White    Male             0             0              35   \n",
       "14                  White    Male          3103             0              48   \n",
       "15                  White    Male             0             0              50   \n",
       "16                  White    Male             0             0              25   \n",
       "17                  White  Female             0             0              30   \n",
       "18                  White  Female             0             0              20   \n",
       "19     Asian-Pac-Islander    Male             0             0              45   \n",
       "20                  White    Male             0             0              47   \n",
       "21                  Black  Female             0             0              35   \n",
       "22                  White  Female             0             0               6   \n",
       "23                  White    Male             0             0              43   \n",
       "24                  White    Male             0             0              40   \n",
       "25                  White    Male          7298             0              90   \n",
       "26                  White    Male             0             0              20   \n",
       "27                  Black    Male             0             0              54   \n",
       "28                  White    Male             0             0              35   \n",
       "29                  White    Male             0             0              60   \n",
       "...                   ...     ...           ...           ...             ...   \n",
       "16251               White    Male          2936             0              20   \n",
       "16252               White    Male          7688             0              40   \n",
       "16253               White  Female             0          1669              40   \n",
       "16254               White    Male             0             0              50   \n",
       "16255               White    Male             0          1902              50   \n",
       "16256               White    Male             0             0              40   \n",
       "16257               White  Female             0             0              40   \n",
       "16258               White    Male             0             0              40   \n",
       "16259               White  Female             0             0               8   \n",
       "16260               White  Female             0             0              45   \n",
       "16261               White    Male         15024             0              55   \n",
       "16262               White    Male             0             0              40   \n",
       "16263               White    Male             0             0              40   \n",
       "16264               White    Male             0             0              32   \n",
       "16265               White  Female             0             0              40   \n",
       "16266               White  Female             0             0              32   \n",
       "16267               Black  Female             0             0              40   \n",
       "16268               White    Male             0             0              36   \n",
       "16269               White    Male             0             0              40   \n",
       "16270               White    Male             0             0              40   \n",
       "16271               White    Male             0             0              48   \n",
       "16272               White    Male             0             0              40   \n",
       "16273               White  Female             0             0              40   \n",
       "16274               White    Male             0             0              40   \n",
       "16275               White    Male             0             0              40   \n",
       "16276               White  Female             0             0              36   \n",
       "16277               Black    Male             0             0              40   \n",
       "16278               White    Male             0             0              50   \n",
       "16279  Asian-Pac-Islander    Male          5455             0              40   \n",
       "16280               White    Male             0             0              60   \n",
       "\n",
       "      native_country income_bracket  \n",
       "0      United-States         <=50K.  \n",
       "1      United-States         <=50K.  \n",
       "2      United-States          >50K.  \n",
       "3      United-States          >50K.  \n",
       "4      United-States         <=50K.  \n",
       "5      United-States         <=50K.  \n",
       "6      United-States         <=50K.  \n",
       "7      United-States          >50K.  \n",
       "8      United-States         <=50K.  \n",
       "9      United-States         <=50K.  \n",
       "10     United-States          >50K.  \n",
       "11     United-States         <=50K.  \n",
       "12     United-States         <=50K.  \n",
       "13     United-States         <=50K.  \n",
       "14     United-States          >50K.  \n",
       "15     United-States          >50K.  \n",
       "16     United-States         <=50K.  \n",
       "17     United-States         <=50K.  \n",
       "18     United-States         <=50K.  \n",
       "19                 ?          >50K.  \n",
       "20     United-States          >50K.  \n",
       "21     United-States         <=50K.  \n",
       "22     United-States         <=50K.  \n",
       "23              Peru         <=50K.  \n",
       "24     United-States         <=50K.  \n",
       "25     United-States          >50K.  \n",
       "26     United-States         <=50K.  \n",
       "27     United-States         <=50K.  \n",
       "28     United-States         <=50K.  \n",
       "29     United-States         <=50K.  \n",
       "...              ...            ...  \n",
       "16251  United-States         <=50K.  \n",
       "16252  United-States          >50K.  \n",
       "16253  United-States         <=50K.  \n",
       "16254  United-States          >50K.  \n",
       "16255  United-States          >50K.  \n",
       "16256  United-States         <=50K.  \n",
       "16257  United-States         <=50K.  \n",
       "16258  United-States         <=50K.  \n",
       "16259  United-States         <=50K.  \n",
       "16260  United-States         <=50K.  \n",
       "16261  United-States          >50K.  \n",
       "16262  United-States         <=50K.  \n",
       "16263  United-States         <=50K.  \n",
       "16264  United-States         <=50K.  \n",
       "16265              ?          >50K.  \n",
       "16266  United-States         <=50K.  \n",
       "16267  United-States         <=50K.  \n",
       "16268  United-States         <=50K.  \n",
       "16269  United-States         <=50K.  \n",
       "16270  United-States         <=50K.  \n",
       "16271  United-States         <=50K.  \n",
       "16272  United-States         <=50K.  \n",
       "16273  United-States         <=50K.  \n",
       "16274  United-States         <=50K.  \n",
       "16275  United-States         <=50K.  \n",
       "16276  United-States         <=50K.  \n",
       "16277  United-States         <=50K.  \n",
       "16278  United-States         <=50K.  \n",
       "16279  United-States         <=50K.  \n",
       "16280  United-States          >50K.  \n",
       "\n",
       "[16281 rows x 15 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ML understands only numbers\n",
    "#This line changes the label to numbers\n",
    "train_labels = (df_train[\"income_bracket\"].apply(lambda x: \">50K\" in x)).astype(int)\n",
    "test_labels = (df_test[\"income_bracket\"].apply(lambda x: \">50K\" in x)).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: income_bracket, dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gender = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"gender\", [\"Female\", \"Male\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "occupation = tf.feature_column.categorical_column_with_hash_bucket(\n",
    "    \"occupation\", hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "education = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"education\", [\n",
    "        \"Bachelors\", \"HS-grad\", \"11th\", \"Masters\", \"9th\",\n",
    "        \"Some-college\", \"Assoc-acdm\", \"Assoc-voc\", \"7th-8th\",\n",
    "        \"Doctorate\", \"Prof-school\", \"5th-6th\", \"10th\", \"1st-4th\",\n",
    "        \"Preschool\", \"12th\"\n",
    "    ])\n",
    "marital_status = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"marital_status\", [\n",
    "        \"Married-civ-spouse\", \"Divorced\", \"Married-spouse-absent\",\n",
    "        \"Never-married\", \"Separated\", \"Married-AF-spouse\", \"Widowed\"\n",
    "    ])\n",
    "relationship = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"relationship\", [\n",
    "        \"Husband\", \"Not-in-family\", \"Wife\", \"Own-child\", \"Unmarried\",\n",
    "        \"Other-relative\"\n",
    "    ])\n",
    "workclass = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "    \"workclass\", [\n",
    "        \"Self-emp-not-inc\", \"Private\", \"State-gov\", \"Federal-gov\",\n",
    "        \"Local-gov\", \"?\", \"Self-emp-inc\", \"Without-pay\", \"Never-worked\"\n",
    "    ])\n",
    "native_country = tf.feature_column.categorical_column_with_hash_bucket(\n",
    "    \"native_country\", hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age = tf.feature_column.numeric_column(\"age\")\n",
    "education_num = tf.feature_column.numeric_column(\"education_num\")\n",
    "capital_gain = tf.feature_column.numeric_column(\"capital_gain\")\n",
    "capital_loss = tf.feature_column.numeric_column(\"capital_loss\")\n",
    "hours_per_week = tf.feature_column.numeric_column(\"hours_per_week\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_buckets = tf.feature_column.bucketized_column(\n",
    "    age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "education_x_occupation = tf.feature_column.crossed_column(\n",
    "    [\"education\", \"occupation\"], hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_buckets_x_education_x_occupation = tf.feature_column.crossed_column(\n",
    "    [age_buckets, \"education\", \"occupation\"], hash_bucket_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmp9h70h7ct\n",
      "INFO:tensorflow:Using config: {'_save_checkpoints_secs': 600, '_log_step_count_steps': 100, '_keep_checkpoint_every_n_hours': 10000, '_keep_checkpoint_max': 5, '_save_checkpoints_steps': None, '_model_dir': 'C:\\\\Users\\\\ZekeLabs\\\\AppData\\\\Local\\\\Temp\\\\tmp9h70h7ct', '_tf_random_seed': 1, '_save_summary_steps': 100, '_session_config': None}\n"
     ]
    }
   ],
   "source": [
    "base_columns = [\n",
    "    gender, native_country, education, occupation, workclass, relationship,\n",
    "    age_buckets,\n",
    "]\n",
    "crossed_columns = [\n",
    "    tf.feature_column.crossed_column(\n",
    "        [\"education\", \"occupation\"], hash_bucket_size=1000),\n",
    "    tf.feature_column.crossed_column(\n",
    "        [age_buckets, \"education\", \"occupation\"], hash_bucket_size=1000),\n",
    "    tf.feature_column.crossed_column(\n",
    "        [\"native_country\", \"occupation\"], hash_bucket_size=1000)\n",
    "]\n",
    "\n",
    "#model_dir = tempfile.mkdtemp()\n",
    "m = tf.estimator.LinearClassifier(feature_columns=base_columns + crossed_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def input_fn(data_file, num_epochs, shuffle):\n",
    "  \"\"\"Input builder function.\"\"\"\n",
    "  df_data = pd.read_csv(\n",
    "      'adult.data.txt',\n",
    "      names=CSV_COLUMNS,\n",
    "      skipinitialspace=True,\n",
    "      engine=\"python\",\n",
    "      skiprows=1)\n",
    "    \n",
    "  # remove NaN elements\n",
    "  df_data = df_data.dropna(how=\"any\", axis=0)\n",
    "    \n",
    "  labels = df_data[\"income_bracket\"].apply(lambda x: \">50K\" in x).astype(int)\n",
    "  return tf.estimator.inputs.pandas_input_fn(\n",
    "      x=df_data,\n",
    "      y=labels,\n",
    "      batch_size=100,\n",
    "      num_epochs=num_epochs,\n",
    "      shuffle=shuffle,\n",
    "      num_threads=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmp9h70h7ct\\model.ckpt.\n",
      "INFO:tensorflow:loss = 69.3147, step = 1\n",
      "INFO:tensorflow:Saving checkpoints for 10 into C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmp9h70h7ct\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 38.6446.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.linear.LinearClassifier at 0x1f6cfcb9f98>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_steps = 10\n",
    "m.train(input_fn=input_fn('adult.data.txt', num_epochs=None, shuffle=True),\n",
    "    steps=train_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:enqueue_data was called with num_epochs and num_threads > 1. num_epochs is applied per thread, so this will produce more epochs than you probably intend. If you want to limit epochs, use one thread.\n",
      "WARNING:tensorflow:enqueue_data was called with shuffle=False and num_threads > 1. This will create multiple threads, all reading the array/dataframe in order. If you want examples read in order, use one thread; if you want multiple threads, enable shuffling.\n",
      "WARNING:tensorflow:Casting <dtype: 'float32'> labels to bool.\n",
      "WARNING:tensorflow:Casting <dtype: 'float32'> labels to bool.\n",
      "INFO:tensorflow:Starting evaluation at 2017-09-04-13:48:13\n",
      "INFO:tensorflow:Restoring parameters from C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmp9h70h7ct\\model.ckpt-10\n",
      "INFO:tensorflow:Finished evaluation at 2017-09-04-13:48:46\n",
      "INFO:tensorflow:Saving dict for global step 10: accuracy = 0.824693, accuracy_baseline = 0.759183, auc = 0.860044, auc_precision_recall = 0.654608, average_loss = 0.388272, global_step = 10, label/mean = 0.240817, loss = 38.8272, prediction/mean = 0.249112\n",
      "accuracy: 0.824693\n",
      "accuracy_baseline: 0.759183\n",
      "auc: 0.860044\n",
      "auc_precision_recall: 0.654608\n",
      "average_loss: 0.388272\n",
      "global_step: 10\n",
      "label/mean: 0.240817\n",
      "loss: 38.8272\n",
      "prediction/mean: 0.249112\n"
     ]
    }
   ],
   "source": [
    "results = m.evaluate(\n",
    "    input_fn=input_fn('adult.test.txt', num_epochs=1, shuffle=False),\n",
    "    steps=None)\n",
    "#print(\"model directory = %s\" % model_dir)\n",
    "for key in sorted(results):\n",
    "  print(\"%s: %s\" % (key, results[key]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "labels = df_test[\"income_bracket\"].apply(lambda x: \">50K\" in x).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat = confusion_matrix(test.target, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method predict in module tensorflow.python.estimator.estimator:\n",
      "\n",
      "predict(input_fn, predict_keys=None, hooks=None, checkpoint_path=None) method of tensorflow.python.estimator.canned.linear.LinearClassifier instance\n",
      "    Returns predictions for given features.\n",
      "    \n",
      "    Args:\n",
      "      input_fn: Input function returning features which is a dictionary of\n",
      "        string feature name to `Tensor` or `SparseTensor`. If it returns a\n",
      "        tuple, first item is extracted as features. Prediction continues until\n",
      "        `input_fn` raises an end-of-input exception (`OutOfRangeError` or\n",
      "        `StopIteration`).\n",
      "      predict_keys: list of `str`, name of the keys to predict. It is used if\n",
      "        the `EstimatorSpec.predictions` is a `dict`. If `predict_keys` is used\n",
      "        then rest of the predictions will be filtered from the dictionary. If\n",
      "        `None`, returns all.\n",
      "      hooks: List of `SessionRunHook` subclass instances. Used for callbacks\n",
      "        inside the prediction call.\n",
      "      checkpoint_path: Path of a specific checkpoint to predict. If `None`, the\n",
      "        latest checkpoint in `model_dir` is used.\n",
      "    \n",
      "    Yields:\n",
      "      Evaluated values of `predictions` tensors.\n",
      "    \n",
      "    Raises:\n",
      "      ValueError: Could not find a trained model in model_dir.\n",
      "      ValueError: if batch length of predictions are not same.\n",
      "      ValueError: If there is a conflict between `predict_keys` and\n",
      "        `predictions`. For example if `predict_keys` is not `None` but\n",
      "        `EstimatorSpec.predictions` is not a `dict`.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(m.predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res = m.predict(input_fn=input_fn('adult.test.txt', num_epochs=1, shuffle=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object Estimator.predict at 0x000001F6D5E45D58>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pandas_input_fn in module tensorflow.python.estimator.inputs.pandas_io:\n",
      "\n",
      "pandas_input_fn(x, y=None, batch_size=128, num_epochs=1, shuffle=None, queue_capacity=1000, num_threads=1, target_column='target')\n",
      "    Returns input function that would feed Pandas DataFrame into the model.\n",
      "    \n",
      "    Note: `y`'s index must match `x`'s index.\n",
      "    \n",
      "    Args:\n",
      "      x: pandas `DataFrame` object.\n",
      "      y: pandas `Series` object. `None` if absent.\n",
      "      batch_size: int, size of batches to return.\n",
      "      num_epochs: int, number of epochs to iterate over data. If not `None`,\n",
      "        read attempts that would exceed this value will raise `OutOfRangeError`.\n",
      "      shuffle: bool, whether to read the records in random order.\n",
      "      queue_capacity: int, size of the read queue. If `None`, it will be set\n",
      "        roughly to the size of `x`.\n",
      "      num_threads: Integer, number of threads used for reading and enqueueing. In\n",
      "        order to have predicted and repeatable order of reading and enqueueing,\n",
      "        such as in prediction and evaluation mode, `num_threads` should be 1.\n",
      "      target_column: str, name to give the target column `y`.\n",
      "    \n",
      "    Returns:\n",
      "      Function, that has signature of ()->(dict of `features`, `target`)\n",
      "    \n",
      "    Raises:\n",
      "      ValueError: if `x` already contains a column with the same name as `y`, or\n",
      "        if the indexes of `x` and `y` don't match.\n",
      "      TypeError: `shuffle` is not bool.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(tf.estimator.inputs.pandas_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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