1.Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:25.084475Z",
     "start_time": "2020-03-16T09:29:24.155631Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import missingno as msno\n",
    "\n",
    "from utils import *\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# magic word for producing visualizations in notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# and setting the size of all plots.\n",
    "plt.rcParams['figure.figsize'] = [11, 7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:25.162267Z",
     "start_time": "2020-03-16T09:29:25.085473Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.Exploratory Data Analysis.ipynb\n",
      "2.Experiments - ML Challenge Solution - AUC  0.87833.ipynb\n",
      "2.Experiments.ipynb\n",
      "3.Experiments - XGBoostClassifier (GridSearch Optimization) - AUC 0.88335-Copy1.ipynb\n",
      "3.Experiments - XGBoostClassifier (GridSearch Optimization) - AUC 0.88335.ipynb\n",
      "4.Experiments - RUSBoostClassifier (Bayesian Optimization) - AUC 0.87795.ipynb\n",
      "5.Experiments - BalancedRandomForest (Bayesian Optimization) - AUC 0.87623.ipynb\n",
      "6.Experiments - Voting Classifier - AUC 0.88166.ipynb\n",
      "7.Experiments - DeepLearning with fast.ai Tabular-Copy1.ipynb\n",
      "7.Experiments - DeepLearning with fast.ai Tabular-Copy2.ipynb\n",
      "7.Experiments - DeepLearning with fast.ai Tabular.ipynb\n",
      "8.Experiments - XGBoostClassifier (Bayesian Optimization) - AUC 0.8814.ipynb\n",
      "README.md\n",
      "__pycache__\n",
      "data\n",
      "temp\n",
      "utils.py\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:25.255021Z",
     "start_time": "2020-03-16T09:29:25.163264Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "census-income.data\n",
      "census-income.data.html\n",
      "census-income.names\n",
      "census-income.test\n",
      "census.tar.gz\n",
      "sampleSubmission.csv\n",
      "sample_submission.csv\n",
      "solution.csv\n",
      "test.csv\n",
      "train.csv\n"
     ]
    }
   ],
   "source": [
    "!ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:25.347424Z",
     "start_time": "2020-03-16T09:29:25.258012Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73, Not in universe, 0, 0, High school graduate, 0, Not in universe, Widowed, Not in universe or children, Not in universe, White, All other, Female, Not in universe, Not in universe, Not in labor force, 0, 0, 0, Nonfiler, Not in universe, Not in universe, Other Rel 18+ ever marr not in subfamily, Other relative of householder, 1700.09, ?, ?, ?, Not in universe under 1 year old, ?, 0, Not in universe, United-States, United-States, United-States, Native- Born in the United States, 0, Not in universe, 2, 0, 95, - 50000.\n",
      "58, Self-employed-not incorporated, 4, 34, Some college but no degree, 0, Not in universe, Divorced, Construction, Precision production craft & repair, White, All other, Male, Not in universe, Not in universe, Children or Armed Forces, 0, 0, 0, Head of household, South, Arkansas, Householder, Householder, 1053.55, MSA to MSA, Same county, Same county, No, Yes, 1, Not in universe, United-States, United-States, United-States, Native- Born in the United States, 0, Not in universe, 2, 52, 94, - 50000.\n",
      "18, Not in universe, 0, 0, 10th grade, 0, High school, Never married, Not in universe or children, Not in universe, Asian or Pacific Islander, All other, Female, Not in universe, Not in universe, Not in labor force, 0, 0, 0, Nonfiler, Not in universe, Not in universe, Child 18+ never marr Not in a subfamily, Child 18 or older, 991.95, ?, ?, ?, Not in universe under 1 year old, ?, 0, Not in universe, Vietnam, Vietnam, Vietnam, Foreign born- Not a citizen of U S , 0, Not in universe, 2, 0, 95, - 50000.\n",
      "9, Not in universe, 0, 0, Children, 0, Not in universe, Never married, Not in universe or children, Not in universe, White, All other, Female, Not in universe, Not in universe, Children or Armed Forces, 0, 0, 0, Nonfiler, Not in universe, Not in universe, Child <18 never marr not in subfamily, Child under 18 never married, 1758.14, Nonmover, Nonmover, Nonmover, Yes, Not in universe, 0, Both parents present, United-States, United-States, United-States, Native- Born in the United States, 0, Not in universe, 0, 0, 94, - 50000.\n",
      "10, Not in universe, 0, 0, Children, 0, Not in universe, Never married, Not in universe or children, Not in universe, White, All other, Female, Not in universe, Not in universe, Children or Armed Forces, 0, 0, 0, Nonfiler, Not in universe, Not in universe, Child <18 never marr not in subfamily, Child under 18 never married, 1069.16, Nonmover, Nonmover, Nonmover, Yes, Not in universe, 0, Both parents present, United-States, United-States, United-States, Native- Born in the United States, 0, Not in universe, 0, 0, 94, - 50000.\n"
     ]
    }
   ],
   "source": [
    "!head -n 5 data/census-income.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above listing we can conclude the following:\n",
    "\n",
    "* There is no header present in the data file. We will have to compute our own based on the documentation\n",
    "* There is a space after the comma `,` so our separator will be `, ` or `,\\s` as RegEx. \n",
    "* The missing values are represented by a question mark `?`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:30.877629Z",
     "start_time": "2020-03-16T09:29:25.348418Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>32</th>\n",
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       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>High school graduate</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Self-employed-not incorporated</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>Some college but no degree</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Precision production craft &amp; repair</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10th grade</td>\n",
       "      <td>0</td>\n",
       "      <td>High school</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Foreign born- Not a citizen of U S</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Children</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Children</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                               1   2   3                           4   5   \\\n",
       "0  73                 Not in universe   0   0        High school graduate   0   \n",
       "1  58  Self-employed-not incorporated   4  34  Some college but no degree   0   \n",
       "2  18                 Not in universe   0   0                  10th grade   0   \n",
       "3   9                 Not in universe   0   0                    Children   0   \n",
       "4  10                 Not in universe   0   0                    Children   0   \n",
       "\n",
       "                6              7                            8   \\\n",
       "0  Not in universe        Widowed  Not in universe or children   \n",
       "1  Not in universe       Divorced                 Construction   \n",
       "2      High school  Never married  Not in universe or children   \n",
       "3  Not in universe  Never married  Not in universe or children   \n",
       "4  Not in universe  Never married  Not in universe or children   \n",
       "\n",
       "                                    9   ...             32             33  \\\n",
       "0                      Not in universe  ...  United-States  United-States   \n",
       "1  Precision production craft & repair  ...  United-States  United-States   \n",
       "2                      Not in universe  ...        Vietnam        Vietnam   \n",
       "3                      Not in universe  ...  United-States  United-States   \n",
       "4                      Not in universe  ...  United-States  United-States   \n",
       "\n",
       "              34                                   35 36               37  38  \\\n",
       "0  United-States    Native- Born in the United States  0  Not in universe   2   \n",
       "1  United-States    Native- Born in the United States  0  Not in universe   2   \n",
       "2        Vietnam  Foreign born- Not a citizen of U S   0  Not in universe   2   \n",
       "3  United-States    Native- Born in the United States  0  Not in universe   0   \n",
       "4  United-States    Native- Born in the United States  0  Not in universe   0   \n",
       "\n",
       "   39  40        41  \n",
       "0   0  95  - 50000.  \n",
       "1  52  94  - 50000.  \n",
       "2   0  95  - 50000.  \n",
       "3   0  94  - 50000.  \n",
       "4   0  94  - 50000.  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The C engine doesn't support RegEx so we set it to Python to avoid the warning\n",
    "#census = pd.read_csv('data/census-income.data', header=None, sep=',\\s', na_values=['?'], engine='python')\n",
    "census = pd.read_csv('data/census-income.data', header=None, sep=',\\s', na_values=['?', 'Do not know', 'Not identifiable'], engine='python')\n",
    "census.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a lists with columns names based on the provided documentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:30.892588Z",
     "start_time": "2020-03-16T09:29:30.878627Z"
    }
   },
   "outputs": [],
   "source": [
    "census_columns = [\n",
    "    'age',\n",
    "    'class of worker',\n",
    "    'detailed industry recode',\n",
    "    'detailed occupation recode',\n",
    "    'education',\n",
    "    'wage per hour',\n",
    "    'enroll in edu inst last wk',\n",
    "    'marital status',\n",
    "    'major industry code',\n",
    "    'major occupation code',\n",
    "    'race',\n",
    "    'hispanic origin',\n",
    "    'sex',\n",
    "    'member of a labor union',\n",
    "    'reason for unemployment',\n",
    "    'full or part time employment stat',\n",
    "    'capital gains',\n",
    "    'capital losses',\n",
    "    'dividends from stocks',\n",
    "    'tax filer stat',\n",
    "    'region of previous residence',\n",
    "    'state of previous residence',\n",
    "    'detailed household and family stat',\n",
    "    'detailed household summary in household',\n",
    "    'instance weight',\n",
    "    'migration code-change in msa',\n",
    "    'migration code-change in reg',\n",
    "    'migration code-move within reg',\n",
    "    'live in this house 1 year ago',\n",
    "    'migration prev res in sunbelt',\n",
    "    'num persons worked for employer',\n",
    "    'family members under 18',\n",
    "    'country of birth father',\n",
    "    'country of birth mother',\n",
    "    'country of birth self',\n",
    "    'citizenship',\n",
    "    'own business or self employed',\n",
    "    'fill inc questionnaire for veteran\\'s admin',\n",
    "    'veterans benefits',\n",
    "    'weeks worked in year',\n",
    "    'year',\n",
    "    'income class'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the column names to the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:30.908576Z",
     "start_time": "2020-03-16T09:29:30.893587Z"
    }
   },
   "outputs": [],
   "source": [
    "census.columns = census_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:30.939495Z",
     "start_time": "2020-03-16T09:29:30.910541Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>education</th>\n",
       "      <th>wage per hour</th>\n",
       "      <th>enroll in edu inst last wk</th>\n",
       "      <th>marital status</th>\n",
       "      <th>major industry code</th>\n",
       "      <th>major occupation code</th>\n",
       "      <th>...</th>\n",
       "      <th>country of birth father</th>\n",
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       "      <th>weeks worked in year</th>\n",
       "      <th>year</th>\n",
       "      <th>income class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73</td>\n",
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       "      <td>High school graduate</td>\n",
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       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>95</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>Self-employed-not incorporated</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>Some college but no degree</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Precision production craft &amp; repair</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10th grade</td>\n",
       "      <td>0</td>\n",
       "      <td>High school</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>Foreign born- Not a citizen of U S</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Children</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Children</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>Never married</td>\n",
       "      <td>Not in universe or children</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>...</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>United-States</td>\n",
       "      <td>Native- Born in the United States</td>\n",
       "      <td>0</td>\n",
       "      <td>Not in universe</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>- 50000.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age                 class of worker  detailed industry recode  \\\n",
       "0   73                 Not in universe                         0   \n",
       "1   58  Self-employed-not incorporated                         4   \n",
       "2   18                 Not in universe                         0   \n",
       "3    9                 Not in universe                         0   \n",
       "4   10                 Not in universe                         0   \n",
       "\n",
       "   detailed occupation recode                   education  wage per hour  \\\n",
       "0                           0        High school graduate              0   \n",
       "1                          34  Some college but no degree              0   \n",
       "2                           0                  10th grade              0   \n",
       "3                           0                    Children              0   \n",
       "4                           0                    Children              0   \n",
       "\n",
       "  enroll in edu inst last wk marital status          major industry code  \\\n",
       "0            Not in universe        Widowed  Not in universe or children   \n",
       "1            Not in universe       Divorced                 Construction   \n",
       "2                High school  Never married  Not in universe or children   \n",
       "3            Not in universe  Never married  Not in universe or children   \n",
       "4            Not in universe  Never married  Not in universe or children   \n",
       "\n",
       "                 major occupation code  ... country of birth father  \\\n",
       "0                      Not in universe  ...           United-States   \n",
       "1  Precision production craft & repair  ...           United-States   \n",
       "2                      Not in universe  ...                 Vietnam   \n",
       "3                      Not in universe  ...           United-States   \n",
       "4                      Not in universe  ...           United-States   \n",
       "\n",
       "  country of birth mother country of birth self  \\\n",
       "0           United-States         United-States   \n",
       "1           United-States         United-States   \n",
       "2                 Vietnam               Vietnam   \n",
       "3           United-States         United-States   \n",
       "4           United-States         United-States   \n",
       "\n",
       "                           citizenship own business or self employed  \\\n",
       "0    Native- Born in the United States                             0   \n",
       "1    Native- Born in the United States                             0   \n",
       "2  Foreign born- Not a citizen of U S                              0   \n",
       "3    Native- Born in the United States                             0   \n",
       "4    Native- Born in the United States                             0   \n",
       "\n",
       "  fill inc questionnaire for veteran's admin  veterans benefits  \\\n",
       "0                            Not in universe                  2   \n",
       "1                            Not in universe                  2   \n",
       "2                            Not in universe                  2   \n",
       "3                            Not in universe                  0   \n",
       "4                            Not in universe                  0   \n",
       "\n",
       "   weeks worked in year  year income class  \n",
       "0                     0    95     - 50000.  \n",
       "1                    52    94     - 50000.  \n",
       "2                     0    95     - 50000.  \n",
       "3                     0    94     - 50000.  \n",
       "4                     0    94     - 50000.  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:31.139695Z",
     "start_time": "2020-03-16T09:29:30.942469Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 42 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   age                                         199523 non-null  int64  \n",
      " 1   class of worker                             199523 non-null  object \n",
      " 2   detailed industry recode                    199523 non-null  int64  \n",
      " 3   detailed occupation recode                  199523 non-null  int64  \n",
      " 4   education                                   199523 non-null  object \n",
      " 5   wage per hour                               199523 non-null  int64  \n",
      " 6   enroll in edu inst last wk                  199523 non-null  object \n",
      " 7   marital status                              199523 non-null  object \n",
      " 8   major industry code                         199523 non-null  object \n",
      " 9   major occupation code                       199523 non-null  object \n",
      " 10  race                                        199523 non-null  object \n",
      " 11  hispanic origin                             198343 non-null  object \n",
      " 12  sex                                         199523 non-null  object \n",
      " 13  member of a labor union                     199523 non-null  object \n",
      " 14  reason for unemployment                     199523 non-null  object \n",
      " 15  full or part time employment stat           199523 non-null  object \n",
      " 16  capital gains                               199523 non-null  int64  \n",
      " 17  capital losses                              199523 non-null  int64  \n",
      " 18  dividends from stocks                       199523 non-null  int64  \n",
      " 19  tax filer stat                              199523 non-null  object \n",
      " 20  region of previous residence                199523 non-null  object \n",
      " 21  state of previous residence                 198815 non-null  object \n",
      " 22  detailed household and family stat          199523 non-null  object \n",
      " 23  detailed household summary in household     199523 non-null  object \n",
      " 24  instance weight                             199523 non-null  float64\n",
      " 25  migration code-change in msa                99397 non-null   object \n",
      " 26  migration code-change in reg                99827 non-null   object \n",
      " 27  migration code-move within reg              99827 non-null   object \n",
      " 28  live in this house 1 year ago               199523 non-null  object \n",
      " 29  migration prev res in sunbelt               99827 non-null   object \n",
      " 30  num persons worked for employer             199523 non-null  int64  \n",
      " 31  family members under 18                     199523 non-null  object \n",
      " 32  country of birth father                     192810 non-null  object \n",
      " 33  country of birth mother                     193404 non-null  object \n",
      " 34  country of birth self                       196130 non-null  object \n",
      " 35  citizenship                                 199523 non-null  object \n",
      " 36  own business or self employed               199523 non-null  int64  \n",
      " 37  fill inc questionnaire for veteran's admin  199523 non-null  object \n",
      " 38  veterans benefits                           199523 non-null  int64  \n",
      " 39  weeks worked in year                        199523 non-null  int64  \n",
      " 40  year                                        199523 non-null  int64  \n",
      " 41  income class                                199523 non-null  object \n",
      "dtypes: float64(1), int64(12), object(29)\n",
      "memory usage: 63.9+ MB\n"
     ]
    }
   ],
   "source": [
    "census.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try to reduce the size of the data-frame and set correct data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:31.278596Z",
     "start_time": "2020-03-16T09:29:31.141596Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe:  63.93418884277344  MB\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9309942a9e749b8848367e0994c1945",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=42.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "___MEMORY USAGE AFTER COMPLETION:___\n",
      "Memory usage is: 48.7117919921875 MB\n",
      "This is 76.19% of the initial size\n"
     ]
    }
   ],
   "source": [
    "census, _ = reduce_mem_usage(census)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:31.480089Z",
     "start_time": "2020-03-16T09:29:31.279593Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 42 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   age                                         199523 non-null  uint8  \n",
      " 1   class of worker                             199523 non-null  object \n",
      " 2   detailed industry recode                    199523 non-null  uint8  \n",
      " 3   detailed occupation recode                  199523 non-null  uint8  \n",
      " 4   education                                   199523 non-null  object \n",
      " 5   wage per hour                               199523 non-null  uint16 \n",
      " 6   enroll in edu inst last wk                  199523 non-null  object \n",
      " 7   marital status                              199523 non-null  object \n",
      " 8   major industry code                         199523 non-null  object \n",
      " 9   major occupation code                       199523 non-null  object \n",
      " 10  race                                        199523 non-null  object \n",
      " 11  hispanic origin                             198343 non-null  object \n",
      " 12  sex                                         199523 non-null  object \n",
      " 13  member of a labor union                     199523 non-null  object \n",
      " 14  reason for unemployment                     199523 non-null  object \n",
      " 15  full or part time employment stat           199523 non-null  object \n",
      " 16  capital gains                               199523 non-null  uint32 \n",
      " 17  capital losses                              199523 non-null  uint16 \n",
      " 18  dividends from stocks                       199523 non-null  uint32 \n",
      " 19  tax filer stat                              199523 non-null  object \n",
      " 20  region of previous residence                199523 non-null  object \n",
      " 21  state of previous residence                 198815 non-null  object \n",
      " 22  detailed household and family stat          199523 non-null  object \n",
      " 23  detailed household summary in household     199523 non-null  object \n",
      " 24  instance weight                             199523 non-null  float32\n",
      " 25  migration code-change in msa                99397 non-null   object \n",
      " 26  migration code-change in reg                99827 non-null   object \n",
      " 27  migration code-move within reg              99827 non-null   object \n",
      " 28  live in this house 1 year ago               199523 non-null  object \n",
      " 29  migration prev res in sunbelt               99827 non-null   object \n",
      " 30  num persons worked for employer             199523 non-null  uint8  \n",
      " 31  family members under 18                     199523 non-null  object \n",
      " 32  country of birth father                     192810 non-null  object \n",
      " 33  country of birth mother                     193404 non-null  object \n",
      " 34  country of birth self                       196130 non-null  object \n",
      " 35  citizenship                                 199523 non-null  object \n",
      " 36  own business or self employed               199523 non-null  uint8  \n",
      " 37  fill inc questionnaire for veteran's admin  199523 non-null  object \n",
      " 38  veterans benefits                           199523 non-null  uint8  \n",
      " 39  weeks worked in year                        199523 non-null  uint8  \n",
      " 40  year                                        199523 non-null  uint8  \n",
      " 41  income class                                199523 non-null  object \n",
      "dtypes: float32(1), object(29), uint16(2), uint32(2), uint8(8)\n",
      "memory usage: 48.7+ MB\n"
     ]
    }
   ],
   "source": [
    "census.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The documentation says to ignore the `instance weight` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:31.756474Z",
     "start_time": "2020-03-16T09:29:31.481054Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 41 columns):\n",
      " #   Column                                      Non-Null Count   Dtype \n",
      "---  ------                                      --------------   ----- \n",
      " 0   age                                         199523 non-null  uint8 \n",
      " 1   class of worker                             199523 non-null  object\n",
      " 2   detailed industry recode                    199523 non-null  uint8 \n",
      " 3   detailed occupation recode                  199523 non-null  uint8 \n",
      " 4   education                                   199523 non-null  object\n",
      " 5   wage per hour                               199523 non-null  uint16\n",
      " 6   enroll in edu inst last wk                  199523 non-null  object\n",
      " 7   marital status                              199523 non-null  object\n",
      " 8   major industry code                         199523 non-null  object\n",
      " 9   major occupation code                       199523 non-null  object\n",
      " 10  race                                        199523 non-null  object\n",
      " 11  hispanic origin                             198343 non-null  object\n",
      " 12  sex                                         199523 non-null  object\n",
      " 13  member of a labor union                     199523 non-null  object\n",
      " 14  reason for unemployment                     199523 non-null  object\n",
      " 15  full or part time employment stat           199523 non-null  object\n",
      " 16  capital gains                               199523 non-null  uint32\n",
      " 17  capital losses                              199523 non-null  uint16\n",
      " 18  dividends from stocks                       199523 non-null  uint32\n",
      " 19  tax filer stat                              199523 non-null  object\n",
      " 20  region of previous residence                199523 non-null  object\n",
      " 21  state of previous residence                 198815 non-null  object\n",
      " 22  detailed household and family stat          199523 non-null  object\n",
      " 23  detailed household summary in household     199523 non-null  object\n",
      " 24  migration code-change in msa                99397 non-null   object\n",
      " 25  migration code-change in reg                99827 non-null   object\n",
      " 26  migration code-move within reg              99827 non-null   object\n",
      " 27  live in this house 1 year ago               199523 non-null  object\n",
      " 28  migration prev res in sunbelt               99827 non-null   object\n",
      " 29  num persons worked for employer             199523 non-null  uint8 \n",
      " 30  family members under 18                     199523 non-null  object\n",
      " 31  country of birth father                     192810 non-null  object\n",
      " 32  country of birth mother                     193404 non-null  object\n",
      " 33  country of birth self                       196130 non-null  object\n",
      " 34  citizenship                                 199523 non-null  object\n",
      " 35  own business or self employed               199523 non-null  uint8 \n",
      " 36  fill inc questionnaire for veteran's admin  199523 non-null  object\n",
      " 37  veterans benefits                           199523 non-null  uint8 \n",
      " 38  weeks worked in year                        199523 non-null  uint8 \n",
      " 39  year                                        199523 non-null  uint8 \n",
      " 40  income class                                199523 non-null  object\n",
      "dtypes: object(29), uint16(2), uint32(2), uint8(8)\n",
      "memory usage: 48.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Make a copy first\n",
    "census_clean = census.copy()\n",
    "census_clean = census_clean.drop('instance weight', axis=1)\n",
    "census_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the columns and their missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:35.506440Z",
     "start_time": "2020-03-16T09:29:31.757469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = msno.bar(census_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:35.707901Z",
     "start_time": "2020-03-16T09:29:35.508436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nan_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>migration code-change in msa</th>\n",
       "      <td>50.182686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration code-change in reg</th>\n",
       "      <td>49.967172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration code-move within reg</th>\n",
       "      <td>49.967172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration prev res in sunbelt</th>\n",
       "      <td>49.967172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth father</th>\n",
       "      <td>3.364524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth mother</th>\n",
       "      <td>3.066814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth self</th>\n",
       "      <td>1.700556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic origin</th>\n",
       "      <td>0.591411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state of previous residence</th>\n",
       "      <td>0.354846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                nan_count\n",
       "migration code-change in msa    50.182686\n",
       "migration code-change in reg    49.967172\n",
       "migration code-move within reg  49.967172\n",
       "migration prev res in sunbelt   49.967172\n",
       "country of birth father          3.364524\n",
       "country of birth mother          3.066814\n",
       "country of birth self            1.700556\n",
       "hispanic origin                  0.591411\n",
       "state of previous residence      0.354846"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_nans = nans_count(census_clean)\n",
    "column_nans.sort_values('nan_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:36.032067Z",
     "start_time": "2020-03-16T09:29:35.708899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ee6191b08d545b89a787e92cf5e4e44",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=29.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class of worker: ['Not in universe', 'Self-employed-not incorporated', 'Private', 'Local government', 'Federal government', 'Self-employed-incorporated', 'State government', 'Never worked', 'Without pay']\n",
      "education: ['High school graduate', 'Some college but no degree', '10th grade', 'Children', 'Bachelors degree(BA AB BS)', 'Masters degree(MA MS MEng MEd MSW MBA)', 'Less than 1st grade', 'Associates degree-academic program', '7th and 8th grade', '12th grade no diploma', 'Associates degree-occup /vocational', 'Prof school degree (MD DDS DVM LLB JD)', '5th or 6th grade', '11th grade', 'Doctorate degree(PhD EdD)', '9th grade', '1st 2nd 3rd or 4th grade']\n",
      "enroll in edu inst last wk: ['Not in universe', 'High school', 'College or university']\n",
      "marital status: ['Widowed', 'Divorced', 'Never married', 'Married-civilian spouse present', 'Separated', 'Married-spouse absent', 'Married-A F spouse present']\n",
      "major industry code: ['Not in universe or children', 'Construction', 'Entertainment', 'Finance insurance and real estate', 'Education', 'Business and repair services', 'Manufacturing-nondurable goods', 'Personal services except private HH', 'Manufacturing-durable goods', 'Other professional services', 'Mining', 'Transportation', 'Wholesale trade', 'Public administration', 'Retail trade', 'Social services', 'Private household services', 'Utilities and sanitary services', 'Communications', 'Hospital services', 'Medical except hospital', 'Agriculture', 'Forestry and fisheries', 'Armed Forces']\n",
      "major occupation code: ['Not in universe', 'Precision production craft & repair', 'Professional specialty', 'Executive admin and managerial', 'Handlers equip cleaners etc ', 'Adm support including clerical', 'Machine operators assmblrs & inspctrs', 'Other service', 'Sales', 'Private household services', 'Technicians and related support', 'Transportation and material moving', 'Farming forestry and fishing', 'Protective services', 'Armed Forces']\n",
      "race: ['White', 'Asian or Pacific Islander', 'Amer Indian Aleut or Eskimo', 'Black', 'Other']\n",
      "hispanic origin: ['All other', nan, 'Central or South American', 'Mexican (Mexicano)', 'Mexican-American', 'Other Spanish', 'Puerto Rican', 'Cuban', 'Chicano']\n",
      "sex: ['Female', 'Male']\n",
      "member of a labor union: ['Not in universe', 'No', 'Yes']\n",
      "reason for unemployment: ['Not in universe', 'Job loser - on layoff', 'Other job loser', 'New entrant', 'Re-entrant', 'Job leaver']\n",
      "full or part time employment stat: ['Not in labor force', 'Children or Armed Forces', 'Full-time schedules', 'Unemployed full-time', 'Unemployed part- time', 'PT for non-econ reasons usually FT', 'PT for econ reasons usually PT', 'PT for econ reasons usually FT']\n",
      "tax filer stat: ['Nonfiler', 'Head of household', 'Joint both under 65', 'Single', 'Joint both 65+', 'Joint one under 65 & one 65+']\n",
      "region of previous residence: ['Not in universe', 'South', 'Northeast', 'Midwest', 'West', 'Abroad']\n",
      "state of previous residence: ['Not in universe', 'Arkansas', 'Utah', 'Michigan', 'Minnesota', 'Alaska', 'Kansas', 'Indiana', nan, 'Massachusetts', 'New Mexico', 'Nevada', 'Tennessee', 'Colorado', 'Abroad', 'Kentucky', 'California', 'Arizona', 'North Carolina', 'Connecticut', 'Florida', 'Vermont', 'Maryland', 'Oklahoma', 'Oregon', 'Ohio', 'South Carolina', 'Texas', 'Montana', 'Wyoming', 'Georgia', 'Pennsylvania', 'Iowa', 'New Hampshire', 'Missouri', 'Alabama', 'North Dakota', 'New Jersey', 'Louisiana', 'West Virginia', 'Delaware', 'Illinois', 'Maine', 'Wisconsin', 'New York', 'Idaho', 'District of Columbia', 'South Dakota', 'Nebraska', 'Virginia', 'Mississippi']\n",
      "detailed household and family stat: ['Other Rel 18+ ever marr not in subfamily', 'Householder', 'Child 18+ never marr Not in a subfamily', 'Child <18 never marr not in subfamily', 'Spouse of householder', 'Secondary individual', 'Other Rel 18+ never marr not in subfamily', 'Nonfamily householder', 'Grandchild <18 never marr not in subfamily', 'Grandchild <18 never marr child of subfamily RP', 'Child 18+ ever marr Not in a subfamily', 'Child 18+ never marr RP of subfamily', 'Child 18+ spouse of subfamily RP', 'Other Rel <18 never marr child of subfamily RP', 'Child under 18 of RP of unrel subfamily', 'Grandchild 18+ never marr not in subfamily', 'Child 18+ ever marr RP of subfamily', 'Other Rel 18+ ever marr RP of subfamily', 'RP of unrelated subfamily', 'Other Rel 18+ spouse of subfamily RP', 'Other Rel <18 never marr not in subfamily', 'Other Rel <18 spouse of subfamily RP', 'In group quarters', 'Grandchild 18+ spouse of subfamily RP', 'Other Rel 18+ never marr RP of subfamily', 'Child <18 never marr RP of subfamily', 'Child <18 ever marr not in subfamily', 'Other Rel <18 ever marr RP of subfamily', 'Grandchild 18+ ever marr not in subfamily', 'Child <18 spouse of subfamily RP', 'Spouse of RP of unrelated subfamily', 'Other Rel <18 never married RP of subfamily', 'Grandchild 18+ never marr RP of subfamily', 'Grandchild 18+ ever marr RP of subfamily', 'Child <18 ever marr RP of subfamily', 'Other Rel <18 ever marr not in subfamily', 'Grandchild <18 never marr RP of subfamily', 'Grandchild <18 ever marr not in subfamily']\n",
      "detailed household summary in household: ['Other relative of householder', 'Householder', 'Child 18 or older', 'Child under 18 never married', 'Spouse of householder', 'Nonrelative of householder', 'Group Quarters- Secondary individual', 'Child under 18 ever married']\n",
      "migration code-change in msa: [nan, 'MSA to MSA', 'Nonmover', 'NonMSA to nonMSA', 'Not in universe', 'Abroad to MSA', 'MSA to nonMSA', 'Abroad to nonMSA', 'NonMSA to MSA']\n",
      "migration code-change in reg: [nan, 'Same county', 'Nonmover', 'Different region', 'Different county same state', 'Not in universe', 'Different division same region', 'Abroad', 'Different state same division']\n",
      "migration code-move within reg: [nan, 'Same county', 'Nonmover', 'Different state in South', 'Different county same state', 'Not in universe', 'Different state in Northeast', 'Abroad', 'Different state in Midwest', 'Different state in West']\n",
      "live in this house 1 year ago: ['Not in universe under 1 year old', 'No', 'Yes']\n",
      "migration prev res in sunbelt: [nan, 'Yes', 'Not in universe', 'No']\n",
      "family members under 18: ['Not in universe', 'Both parents present', 'Mother only present', 'Neither parent present', 'Father only present']\n",
      "country of birth father: ['United-States', 'Vietnam', 'Philippines', nan, 'Columbia', 'Germany', 'Mexico', 'Japan', 'Peru', 'Dominican-Republic', 'South Korea', 'Cuba', 'El-Salvador', 'Canada', 'Scotland', 'Outlying-U S (Guam USVI etc)', 'Italy', 'Guatemala', 'Ecuador', 'Puerto-Rico', 'Cambodia', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'Hungary', 'Yugoslavia', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Portugal', 'Iran', 'France', 'India', 'Hong Kong', 'Haiti', 'Greece', 'Holand-Netherlands', 'Thailand', 'Laos', 'Panama']\n",
      "country of birth mother: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'El-Salvador', 'Peru', 'Puerto-Rico', 'Cuba', 'Philippines', 'Dominican-Republic', 'Germany', 'England', 'Guatemala', 'Scotland', 'Portugal', 'Italy', 'Ecuador', 'Yugoslavia', 'China', 'Poland', 'Hungary', 'Nicaragua', 'Taiwan', 'Ireland', 'Canada', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'France', 'Cambodia', 'India', 'Hong Kong', 'Haiti', 'Japan', 'Greece', 'Holand-Netherlands', 'Thailand', 'Panama', 'Laos', 'Outlying-U S (Guam USVI etc)']\n",
      "country of birth self: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'Peru', 'Cuba', 'Philippines', 'Dominican-Republic', 'El-Salvador', 'Canada', 'Scotland', 'Portugal', 'Guatemala', 'Ecuador', 'Germany', 'Outlying-U S (Guam USVI etc)', 'Puerto-Rico', 'Italy', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'Hungary', 'France', 'Cambodia', 'India', 'Hong Kong', 'Japan', 'Haiti', 'Holand-Netherlands', 'Greece', 'Thailand', 'Panama', 'Yugoslavia', 'Laos']\n",
      "citizenship: ['Native- Born in the United States', 'Foreign born- Not a citizen of U S ', 'Foreign born- U S citizen by naturalization', 'Native- Born abroad of American Parent(s)', 'Native- Born in Puerto Rico or U S Outlying']\n",
      "fill inc questionnaire for veteran's admin: ['Not in universe', 'No', 'Yes']\n",
      "income class: ['- 50000.', '50000+.']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical = census_clean.select_dtypes(include='object')\n",
    "unique_values(categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:42.122745Z",
     "start_time": "2020-03-16T09:29:36.033033Z"
    }
   },
   "outputs": [],
   "source": [
    "# replace field that contains Not in Universe with NaN\n",
    "census_clean = census_clean.replace(r'Not in universe\\w*?', np.nan, regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:42.492756Z",
     "start_time": "2020-03-16T09:29:42.123743Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "918217a332cd49458fb88193fe634abc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=29.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class of worker: [nan, 'Self-employed-not incorporated', 'Private', 'Local government', 'Federal government', 'Self-employed-incorporated', 'State government', 'Never worked', 'Without pay']\n",
      "education: ['High school graduate', 'Some college but no degree', '10th grade', 'Children', 'Bachelors degree(BA AB BS)', 'Masters degree(MA MS MEng MEd MSW MBA)', 'Less than 1st grade', 'Associates degree-academic program', '7th and 8th grade', '12th grade no diploma', 'Associates degree-occup /vocational', 'Prof school degree (MD DDS DVM LLB JD)', '5th or 6th grade', '11th grade', 'Doctorate degree(PhD EdD)', '9th grade', '1st 2nd 3rd or 4th grade']\n",
      "enroll in edu inst last wk: [nan, 'High school', 'College or university']\n",
      "marital status: ['Widowed', 'Divorced', 'Never married', 'Married-civilian spouse present', 'Separated', 'Married-spouse absent', 'Married-A F spouse present']\n",
      "major industry code: [nan, 'Construction', 'Entertainment', 'Finance insurance and real estate', 'Education', 'Business and repair services', 'Manufacturing-nondurable goods', 'Personal services except private HH', 'Manufacturing-durable goods', 'Other professional services', 'Mining', 'Transportation', 'Wholesale trade', 'Public administration', 'Retail trade', 'Social services', 'Private household services', 'Utilities and sanitary services', 'Communications', 'Hospital services', 'Medical except hospital', 'Agriculture', 'Forestry and fisheries', 'Armed Forces']\n",
      "major occupation code: [nan, 'Precision production craft & repair', 'Professional specialty', 'Executive admin and managerial', 'Handlers equip cleaners etc ', 'Adm support including clerical', 'Machine operators assmblrs & inspctrs', 'Other service', 'Sales', 'Private household services', 'Technicians and related support', 'Transportation and material moving', 'Farming forestry and fishing', 'Protective services', 'Armed Forces']\n",
      "race: ['White', 'Asian or Pacific Islander', 'Amer Indian Aleut or Eskimo', 'Black', 'Other']\n",
      "hispanic origin: ['All other', nan, 'Central or South American', 'Mexican (Mexicano)', 'Mexican-American', 'Other Spanish', 'Puerto Rican', 'Cuban', 'Chicano']\n",
      "sex: ['Female', 'Male']\n",
      "member of a labor union: [nan, 'No', 'Yes']\n",
      "reason for unemployment: [nan, 'Job loser - on layoff', 'Other job loser', 'New entrant', 'Re-entrant', 'Job leaver']\n",
      "full or part time employment stat: ['Not in labor force', 'Children or Armed Forces', 'Full-time schedules', 'Unemployed full-time', 'Unemployed part- time', 'PT for non-econ reasons usually FT', 'PT for econ reasons usually PT', 'PT for econ reasons usually FT']\n",
      "tax filer stat: ['Nonfiler', 'Head of household', 'Joint both under 65', 'Single', 'Joint both 65+', 'Joint one under 65 & one 65+']\n",
      "region of previous residence: [nan, 'South', 'Northeast', 'Midwest', 'West', 'Abroad']\n",
      "state of previous residence: [nan, 'Arkansas', 'Utah', 'Michigan', 'Minnesota', 'Alaska', 'Kansas', 'Indiana', 'Massachusetts', 'New Mexico', 'Nevada', 'Tennessee', 'Colorado', 'Abroad', 'Kentucky', 'California', 'Arizona', 'North Carolina', 'Connecticut', 'Florida', 'Vermont', 'Maryland', 'Oklahoma', 'Oregon', 'Ohio', 'South Carolina', 'Texas', 'Montana', 'Wyoming', 'Georgia', 'Pennsylvania', 'Iowa', 'New Hampshire', 'Missouri', 'Alabama', 'North Dakota', 'New Jersey', 'Louisiana', 'West Virginia', 'Delaware', 'Illinois', 'Maine', 'Wisconsin', 'New York', 'Idaho', 'District of Columbia', 'South Dakota', 'Nebraska', 'Virginia', 'Mississippi']\n",
      "detailed household and family stat: ['Other Rel 18+ ever marr not in subfamily', 'Householder', 'Child 18+ never marr Not in a subfamily', 'Child <18 never marr not in subfamily', 'Spouse of householder', 'Secondary individual', 'Other Rel 18+ never marr not in subfamily', 'Nonfamily householder', 'Grandchild <18 never marr not in subfamily', 'Grandchild <18 never marr child of subfamily RP', 'Child 18+ ever marr Not in a subfamily', 'Child 18+ never marr RP of subfamily', 'Child 18+ spouse of subfamily RP', 'Other Rel <18 never marr child of subfamily RP', 'Child under 18 of RP of unrel subfamily', 'Grandchild 18+ never marr not in subfamily', 'Child 18+ ever marr RP of subfamily', 'Other Rel 18+ ever marr RP of subfamily', 'RP of unrelated subfamily', 'Other Rel 18+ spouse of subfamily RP', 'Other Rel <18 never marr not in subfamily', 'Other Rel <18 spouse of subfamily RP', 'In group quarters', 'Grandchild 18+ spouse of subfamily RP', 'Other Rel 18+ never marr RP of subfamily', 'Child <18 never marr RP of subfamily', 'Child <18 ever marr not in subfamily', 'Other Rel <18 ever marr RP of subfamily', 'Grandchild 18+ ever marr not in subfamily', 'Child <18 spouse of subfamily RP', 'Spouse of RP of unrelated subfamily', 'Other Rel <18 never married RP of subfamily', 'Grandchild 18+ never marr RP of subfamily', 'Grandchild 18+ ever marr RP of subfamily', 'Child <18 ever marr RP of subfamily', 'Other Rel <18 ever marr not in subfamily', 'Grandchild <18 never marr RP of subfamily', 'Grandchild <18 ever marr not in subfamily']\n",
      "detailed household summary in household: ['Other relative of householder', 'Householder', 'Child 18 or older', 'Child under 18 never married', 'Spouse of householder', 'Nonrelative of householder', 'Group Quarters- Secondary individual', 'Child under 18 ever married']\n",
      "migration code-change in msa: [nan, 'MSA to MSA', 'Nonmover', 'NonMSA to nonMSA', 'Abroad to MSA', 'MSA to nonMSA', 'Abroad to nonMSA', 'NonMSA to MSA']\n",
      "migration code-change in reg: [nan, 'Same county', 'Nonmover', 'Different region', 'Different county same state', 'Different division same region', 'Abroad', 'Different state same division']\n",
      "migration code-move within reg: [nan, 'Same county', 'Nonmover', 'Different state in South', 'Different county same state', 'Different state in Northeast', 'Abroad', 'Different state in Midwest', 'Different state in West']\n",
      "live in this house 1 year ago: [nan, 'No', 'Yes']\n",
      "migration prev res in sunbelt: [nan, 'Yes', 'No']\n",
      "family members under 18: [nan, 'Both parents present', 'Mother only present', 'Neither parent present', 'Father only present']\n",
      "country of birth father: ['United-States', 'Vietnam', 'Philippines', nan, 'Columbia', 'Germany', 'Mexico', 'Japan', 'Peru', 'Dominican-Republic', 'South Korea', 'Cuba', 'El-Salvador', 'Canada', 'Scotland', 'Outlying-U S (Guam USVI etc)', 'Italy', 'Guatemala', 'Ecuador', 'Puerto-Rico', 'Cambodia', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'Hungary', 'Yugoslavia', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Portugal', 'Iran', 'France', 'India', 'Hong Kong', 'Haiti', 'Greece', 'Holand-Netherlands', 'Thailand', 'Laos', 'Panama']\n",
      "country of birth mother: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'El-Salvador', 'Peru', 'Puerto-Rico', 'Cuba', 'Philippines', 'Dominican-Republic', 'Germany', 'England', 'Guatemala', 'Scotland', 'Portugal', 'Italy', 'Ecuador', 'Yugoslavia', 'China', 'Poland', 'Hungary', 'Nicaragua', 'Taiwan', 'Ireland', 'Canada', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'France', 'Cambodia', 'India', 'Hong Kong', 'Haiti', 'Japan', 'Greece', 'Holand-Netherlands', 'Thailand', 'Panama', 'Laos', 'Outlying-U S (Guam USVI etc)']\n",
      "country of birth self: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'Peru', 'Cuba', 'Philippines', 'Dominican-Republic', 'El-Salvador', 'Canada', 'Scotland', 'Portugal', 'Guatemala', 'Ecuador', 'Germany', 'Outlying-U S (Guam USVI etc)', 'Puerto-Rico', 'Italy', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'Hungary', 'France', 'Cambodia', 'India', 'Hong Kong', 'Japan', 'Haiti', 'Holand-Netherlands', 'Greece', 'Thailand', 'Panama', 'Yugoslavia', 'Laos']\n",
      "citizenship: ['Native- Born in the United States', 'Foreign born- Not a citizen of U S ', 'Foreign born- U S citizen by naturalization', 'Native- Born abroad of American Parent(s)', 'Native- Born in Puerto Rico or U S Outlying']\n",
      "fill inc questionnaire for veteran's admin: [nan, 'No', 'Yes']\n",
      "income class: ['- 50000.', '50000+.']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical = census_clean.select_dtypes(include='object')\n",
    "unique_values(categorical)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.209838Z",
     "start_time": "2020-03-16T09:29:42.493753Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = msno.bar(census_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.410309Z",
     "start_time": "2020-03-16T09:29:46.210811Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nan_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fill inc questionnaire for veteran's admin</th>\n",
       "      <td>99.005628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason for unemployment</th>\n",
       "      <td>96.957744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>enroll in edu inst last wk</th>\n",
       "      <td>93.694962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state of previous residence</th>\n",
       "      <td>92.449492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration prev res in sunbelt</th>\n",
       "      <td>92.094646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region of previous residence</th>\n",
       "      <td>92.094646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>member of a labor union</th>\n",
       "      <td>90.445212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family members under 18</th>\n",
       "      <td>72.288408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration code-change in msa</th>\n",
       "      <td>50.942498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration code-change in reg</th>\n",
       "      <td>50.726984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>migration code-move within reg</th>\n",
       "      <td>50.726984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>live in this house 1 year ago</th>\n",
       "      <td>50.726984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major occupation code</th>\n",
       "      <td>50.462353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>major industry code</th>\n",
       "      <td>50.462353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class of worker</th>\n",
       "      <td>50.242328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth father</th>\n",
       "      <td>3.364524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth mother</th>\n",
       "      <td>3.066814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth self</th>\n",
       "      <td>1.700556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic origin</th>\n",
       "      <td>0.591411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            nan_count\n",
       "fill inc questionnaire for veteran's admin  99.005628\n",
       "reason for unemployment                     96.957744\n",
       "enroll in edu inst last wk                  93.694962\n",
       "state of previous residence                 92.449492\n",
       "migration prev res in sunbelt               92.094646\n",
       "region of previous residence                92.094646\n",
       "member of a labor union                     90.445212\n",
       "family members under 18                     72.288408\n",
       "migration code-change in msa                50.942498\n",
       "migration code-change in reg                50.726984\n",
       "migration code-move within reg              50.726984\n",
       "live in this house 1 year ago               50.726984\n",
       "major occupation code                       50.462353\n",
       "major industry code                         50.462353\n",
       "class of worker                             50.242328\n",
       "country of birth father                      3.364524\n",
       "country of birth mother                      3.066814\n",
       "country of birth self                        1.700556\n",
       "hispanic origin                              0.591411"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_nans = nans_count(census_clean)\n",
    "column_nans.sort_values('nan_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.579824Z",
     "start_time": "2020-03-16T09:29:46.412274Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_nans.hist(grid=False)\n",
    "plt.title('Distribution of missing value counts per column')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.625703Z",
     "start_time": "2020-03-16T09:29:46.581819Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['class of worker',\n",
       " 'enroll in edu inst last wk',\n",
       " 'major industry code',\n",
       " 'major occupation code',\n",
       " 'member of a labor union',\n",
       " 'reason for unemployment',\n",
       " 'region of previous residence',\n",
       " 'state of previous residence',\n",
       " 'migration code-change in msa',\n",
       " 'migration code-change in reg',\n",
       " 'migration code-move within reg',\n",
       " 'live in this house 1 year ago',\n",
       " 'migration prev res in sunbelt',\n",
       " 'family members under 18',\n",
       " \"fill inc questionnaire for veteran's admin\"]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_nan_threshold = 50\n",
    "\n",
    "nans_to_drop = column_nans[column_nans['nan_count'] > column_nan_threshold].index.tolist()\n",
    "census_clean.drop(nans_to_drop, axis=1, inplace=True)\n",
    "\n",
    "# droped columns\n",
    "nans_to_drop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.734409Z",
     "start_time": "2020-03-16T09:29:46.626699Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nan_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>country of birth father</th>\n",
       "      <td>3.364524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth mother</th>\n",
       "      <td>3.066814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country of birth self</th>\n",
       "      <td>1.700556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic origin</th>\n",
       "      <td>0.591411</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                         nan_count\n",
       "country of birth father   3.364524\n",
       "country of birth mother   3.066814\n",
       "country of birth self     1.700556\n",
       "hispanic origin           0.591411"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_nans = nans_count(census_clean)\n",
    "column_nans.sort_values('nan_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:46.918917Z",
     "start_time": "2020-03-16T09:29:46.738401Z"
    }
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   "outputs": [
    {
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     "metadata": {},
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "education: ['High school graduate', 'Some college but no degree', '10th grade', 'Children', 'Bachelors degree(BA AB BS)', 'Masters degree(MA MS MEng MEd MSW MBA)', 'Less than 1st grade', 'Associates degree-academic program', '7th and 8th grade', '12th grade no diploma', 'Associates degree-occup /vocational', 'Prof school degree (MD DDS DVM LLB JD)', '5th or 6th grade', '11th grade', 'Doctorate degree(PhD EdD)', '9th grade', '1st 2nd 3rd or 4th grade']\n",
      "marital status: ['Widowed', 'Divorced', 'Never married', 'Married-civilian spouse present', 'Separated', 'Married-spouse absent', 'Married-A F spouse present']\n",
      "race: ['White', 'Asian or Pacific Islander', 'Amer Indian Aleut or Eskimo', 'Black', 'Other']\n",
      "hispanic origin: ['All other', nan, 'Central or South American', 'Mexican (Mexicano)', 'Mexican-American', 'Other Spanish', 'Puerto Rican', 'Cuban', 'Chicano']\n",
      "sex: ['Female', 'Male']\n",
      "full or part time employment stat: ['Not in labor force', 'Children or Armed Forces', 'Full-time schedules', 'Unemployed full-time', 'Unemployed part- time', 'PT for non-econ reasons usually FT', 'PT for econ reasons usually PT', 'PT for econ reasons usually FT']\n",
      "tax filer stat: ['Nonfiler', 'Head of household', 'Joint both under 65', 'Single', 'Joint both 65+', 'Joint one under 65 & one 65+']\n",
      "detailed household and family stat: ['Other Rel 18+ ever marr not in subfamily', 'Householder', 'Child 18+ never marr Not in a subfamily', 'Child <18 never marr not in subfamily', 'Spouse of householder', 'Secondary individual', 'Other Rel 18+ never marr not in subfamily', 'Nonfamily householder', 'Grandchild <18 never marr not in subfamily', 'Grandchild <18 never marr child of subfamily RP', 'Child 18+ ever marr Not in a subfamily', 'Child 18+ never marr RP of subfamily', 'Child 18+ spouse of subfamily RP', 'Other Rel <18 never marr child of subfamily RP', 'Child under 18 of RP of unrel subfamily', 'Grandchild 18+ never marr not in subfamily', 'Child 18+ ever marr RP of subfamily', 'Other Rel 18+ ever marr RP of subfamily', 'RP of unrelated subfamily', 'Other Rel 18+ spouse of subfamily RP', 'Other Rel <18 never marr not in subfamily', 'Other Rel <18 spouse of subfamily RP', 'In group quarters', 'Grandchild 18+ spouse of subfamily RP', 'Other Rel 18+ never marr RP of subfamily', 'Child <18 never marr RP of subfamily', 'Child <18 ever marr not in subfamily', 'Other Rel <18 ever marr RP of subfamily', 'Grandchild 18+ ever marr not in subfamily', 'Child <18 spouse of subfamily RP', 'Spouse of RP of unrelated subfamily', 'Other Rel <18 never married RP of subfamily', 'Grandchild 18+ never marr RP of subfamily', 'Grandchild 18+ ever marr RP of subfamily', 'Child <18 ever marr RP of subfamily', 'Other Rel <18 ever marr not in subfamily', 'Grandchild <18 never marr RP of subfamily', 'Grandchild <18 ever marr not in subfamily']\n",
      "detailed household summary in household: ['Other relative of householder', 'Householder', 'Child 18 or older', 'Child under 18 never married', 'Spouse of householder', 'Nonrelative of householder', 'Group Quarters- Secondary individual', 'Child under 18 ever married']\n",
      "country of birth father: ['United-States', 'Vietnam', 'Philippines', nan, 'Columbia', 'Germany', 'Mexico', 'Japan', 'Peru', 'Dominican-Republic', 'South Korea', 'Cuba', 'El-Salvador', 'Canada', 'Scotland', 'Outlying-U S (Guam USVI etc)', 'Italy', 'Guatemala', 'Ecuador', 'Puerto-Rico', 'Cambodia', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'Hungary', 'Yugoslavia', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Portugal', 'Iran', 'France', 'India', 'Hong Kong', 'Haiti', 'Greece', 'Holand-Netherlands', 'Thailand', 'Laos', 'Panama']\n",
      "country of birth mother: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'El-Salvador', 'Peru', 'Puerto-Rico', 'Cuba', 'Philippines', 'Dominican-Republic', 'Germany', 'England', 'Guatemala', 'Scotland', 'Portugal', 'Italy', 'Ecuador', 'Yugoslavia', 'China', 'Poland', 'Hungary', 'Nicaragua', 'Taiwan', 'Ireland', 'Canada', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'France', 'Cambodia', 'India', 'Hong Kong', 'Haiti', 'Japan', 'Greece', 'Holand-Netherlands', 'Thailand', 'Panama', 'Laos', 'Outlying-U S (Guam USVI etc)']\n",
      "country of birth self: ['United-States', 'Vietnam', nan, 'Columbia', 'Mexico', 'Peru', 'Cuba', 'Philippines', 'Dominican-Republic', 'El-Salvador', 'Canada', 'Scotland', 'Portugal', 'Guatemala', 'Ecuador', 'Germany', 'Outlying-U S (Guam USVI etc)', 'Puerto-Rico', 'Italy', 'China', 'Poland', 'Nicaragua', 'Taiwan', 'England', 'Ireland', 'South Korea', 'Trinadad&Tobago', 'Jamaica', 'Honduras', 'Iran', 'Hungary', 'France', 'Cambodia', 'India', 'Hong Kong', 'Japan', 'Haiti', 'Holand-Netherlands', 'Greece', 'Thailand', 'Panama', 'Yugoslavia', 'Laos']\n",
      "citizenship: ['Native- Born in the United States', 'Foreign born- Not a citizen of U S ', 'Foreign born- U S citizen by naturalization', 'Native- Born abroad of American Parent(s)', 'Native- Born in Puerto Rico or U S Outlying']\n",
      "income class: ['- 50000.', '50000+.']\n",
      "\n"
     ]
    }
   ],
   "source": [
    "categorical = census_clean.select_dtypes(include='object')\n",
    "unique_values(categorical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Re-encode some columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:47.149326Z",
     "start_time": "2020-03-16T09:29:46.920912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe:  25.117076873779297  MB\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "___MEMORY USAGE AFTER COMPLETION:___\n",
      "Memory usage is: 22.453157424926758 MB\n",
      "This is 89.39% of the initial size\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 26 columns):\n",
      " #   Column                                   Non-Null Count   Dtype \n",
      "---  ------                                   --------------   ----- \n",
      " 0   age                                      199523 non-null  uint8 \n",
      " 1   detailed industry recode                 199523 non-null  uint8 \n",
      " 2   detailed occupation recode               199523 non-null  uint8 \n",
      " 3   education                                199523 non-null  object\n",
      " 4   wage per hour                            199523 non-null  uint16\n",
      " 5   marital status                           199523 non-null  object\n",
      " 6   race                                     199523 non-null  object\n",
      " 7   hispanic origin                          198343 non-null  object\n",
      " 8   sex                                      199523 non-null  uint8 \n",
      " 9   full or part time employment stat        199523 non-null  object\n",
      " 10  capital gains                            199523 non-null  uint32\n",
      " 11  capital losses                           199523 non-null  uint16\n",
      " 12  dividends from stocks                    199523 non-null  uint32\n",
      " 13  tax filer stat                           199523 non-null  object\n",
      " 14  detailed household and family stat       199523 non-null  object\n",
      " 15  detailed household summary in household  199523 non-null  object\n",
      " 16  num persons worked for employer          199523 non-null  uint8 \n",
      " 17  country of birth father                  192810 non-null  object\n",
      " 18  country of birth mother                  193404 non-null  object\n",
      " 19  country of birth self                    196130 non-null  object\n",
      " 20  citizenship                              199523 non-null  object\n",
      " 21  own business or self employed            199523 non-null  uint8 \n",
      " 22  veterans benefits                        199523 non-null  uint8 \n",
      " 23  weeks worked in year                     199523 non-null  uint8 \n",
      " 24  year                                     199523 non-null  uint8 \n",
      " 25  income class                             199523 non-null  uint8 \n",
      "dtypes: object(12), uint16(2), uint32(2), uint8(10)\n",
      "memory usage: 22.5+ MB\n"
     ]
    }
   ],
   "source": [
    "census_clean['sex'] = census_clean['sex'].map({'Female': 0, 'Male': 1})\n",
    "census_clean['income class'] = census_clean['income class'].map({'- 50000.': 0, '50000+.': 1})\n",
    "\n",
    "census_clean, _ = reduce_mem_usage(census_clean)\n",
    "census_clean.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:47.349764Z",
     "start_time": "2020-03-16T09:29:47.150330Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 26 columns):\n",
      " #   Column                                   Non-Null Count   Dtype \n",
      "---  ------                                   --------------   ----- \n",
      " 0   age                                      199523 non-null  uint8 \n",
      " 1   detailed industry recode                 199523 non-null  uint8 \n",
      " 2   detailed occupation recode               199523 non-null  uint8 \n",
      " 3   education                                199523 non-null  object\n",
      " 4   wage per hour                            199523 non-null  uint16\n",
      " 5   marital status                           199523 non-null  object\n",
      " 6   race                                     199523 non-null  object\n",
      " 7   hispanic origin                          199523 non-null  object\n",
      " 8   sex                                      199523 non-null  uint8 \n",
      " 9   full or part time employment stat        199523 non-null  object\n",
      " 10  capital gains                            199523 non-null  uint32\n",
      " 11  capital losses                           199523 non-null  uint16\n",
      " 12  dividends from stocks                    199523 non-null  uint32\n",
      " 13  tax filer stat                           199523 non-null  object\n",
      " 14  detailed household and family stat       199523 non-null  object\n",
      " 15  detailed household summary in household  199523 non-null  object\n",
      " 16  num persons worked for employer          199523 non-null  uint8 \n",
      " 17  country of birth father                  199523 non-null  object\n",
      " 18  country of birth mother                  199523 non-null  object\n",
      " 19  country of birth self                    199523 non-null  object\n",
      " 20  citizenship                              199523 non-null  object\n",
      " 21  own business or self employed            199523 non-null  uint8 \n",
      " 22  veterans benefits                        199523 non-null  uint8 \n",
      " 23  weeks worked in year                     199523 non-null  uint8 \n",
      " 24  year                                     199523 non-null  uint8 \n",
      " 25  income class                             199523 non-null  uint8 \n",
      "dtypes: object(12), uint16(2), uint32(2), uint8(10)\n",
      "memory usage: 22.5+ MB\n"
     ]
    }
   ],
   "source": [
    "census_clean = census_clean.fillna('Unknown')\n",
    "census_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One Hot Encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:47.673898Z",
     "start_time": "2020-03-16T09:29:47.350763Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education_10th grade</th>\n",
       "      <th>education_11th grade</th>\n",
       "      <th>education_12th grade no diploma</th>\n",
       "      <th>education_1st 2nd 3rd or 4th grade</th>\n",
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       "      <th>...</th>\n",
       "      <th>country of birth self_Trinadad&amp;Tobago</th>\n",
       "      <th>country of birth self_United-States</th>\n",
       "      <th>country of birth self_Unknown</th>\n",
       "      <th>country of birth self_Vietnam</th>\n",
       "      <th>country of birth self_Yugoslavia</th>\n",
       "      <th>citizenship_Foreign born- Not a citizen of U S</th>\n",
       "      <th>citizenship_Foreign born- U S citizen by naturalization</th>\n",
       "      <th>citizenship_Native- Born abroad of American Parent(s)</th>\n",
       "      <th>citizenship_Native- Born in Puerto Rico or U S Outlying</th>\n",
       "      <th>citizenship_Native- Born in the United States</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 232 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   education_10th grade  education_11th grade  \\\n",
       "0                     0                     0   \n",
       "1                     0                     0   \n",
       "2                     1                     0   \n",
       "3                     0                     0   \n",
       "4                     0                     0   \n",
       "\n",
       "   education_12th grade no diploma  education_1st 2nd 3rd or 4th grade  \\\n",
       "0                                0                                   0   \n",
       "1                                0                                   0   \n",
       "2                                0                                   0   \n",
       "3                                0                                   0   \n",
       "4                                0                                   0   \n",
       "\n",
       "   education_5th or 6th grade  education_7th and 8th grade  \\\n",
       "0                           0                            0   \n",
       "1                           0                            0   \n",
       "2                           0                            0   \n",
       "3                           0                            0   \n",
       "4                           0                            0   \n",
       "\n",
       "   education_9th grade  education_Associates degree-academic program  \\\n",
       "0                    0                                             0   \n",
       "1                    0                                             0   \n",
       "2                    0                                             0   \n",
       "3                    0                                             0   \n",
       "4                    0                                             0   \n",
       "\n",
       "   education_Associates degree-occup /vocational  \\\n",
       "0                                              0   \n",
       "1                                              0   \n",
       "2                                              0   \n",
       "3                                              0   \n",
       "4                                              0   \n",
       "\n",
       "   education_Bachelors degree(BA AB BS)  ...  \\\n",
       "0                                     0  ...   \n",
       "1                                     0  ...   \n",
       "2                                     0  ...   \n",
       "3                                     0  ...   \n",
       "4                                     0  ...   \n",
       "\n",
       "   country of birth self_Trinadad&Tobago  country of birth self_United-States  \\\n",
       "0                                      0                                    1   \n",
       "1                                      0                                    1   \n",
       "2                                      0                                    0   \n",
       "3                                      0                                    1   \n",
       "4                                      0                                    1   \n",
       "\n",
       "   country of birth self_Unknown  country of birth self_Vietnam  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              1   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   country of birth self_Yugoslavia  \\\n",
       "0                                 0   \n",
       "1                                 0   \n",
       "2                                 0   \n",
       "3                                 0   \n",
       "4                                 0   \n",
       "\n",
       "   citizenship_Foreign born- Not a citizen of U S   \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                1   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   citizenship_Foreign born- U S citizen by naturalization  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "3                                                  0         \n",
       "4                                                  0         \n",
       "\n",
       "   citizenship_Native- Born abroad of American Parent(s)  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "3                                                  0       \n",
       "4                                                  0       \n",
       "\n",
       "   citizenship_Native- Born in Puerto Rico or U S Outlying  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "3                                                  0         \n",
       "4                                                  0         \n",
       "\n",
       "   citizenship_Native- Born in the United States  \n",
       "0                                              1  \n",
       "1                                              1  \n",
       "2                                              0  \n",
       "3                                              1  \n",
       "4                                              1  \n",
       "\n",
       "[5 rows x 232 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "categorical = census_clean.select_dtypes(include='object')\n",
    "encoded = pd.get_dummies(categorical)\n",
    "\n",
    "encoded.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:47.796598Z",
     "start_time": "2020-03-16T09:29:47.674895Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Columns: 246 entries, age to citizenship_Native- Born in the United States\n",
      "dtypes: uint16(2), uint32(2), uint8(242)\n",
      "memory usage: 48.3 MB\n"
     ]
    }
   ],
   "source": [
    "census_clean = census_clean.drop(categorical, axis=1)\n",
    "census_clean = pd.concat([census_clean, encoded], axis=1)\n",
    "\n",
    "census_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:29:47.982100Z",
     "start_time": "2020-03-16T09:29:47.797566Z"
    }
   },
   "outputs": [
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       "<p>5 rows × 246 columns</p>\n",
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       "        age  detailed industry recode  detailed occupation recode  \\\n",
       "0  1.725879                         0                           0   \n",
       "1  1.053560                         4                          34   \n",
       "2 -0.739291                         0                           0   \n",
       "3 -1.142682                         0                           0   \n",
       "4 -1.097861                         0                           0   \n",
       "\n",
       "   wage per hour  sex  capital gains  capital losses  dividends from stocks  \\\n",
       "0      -0.201629    0      -0.092542       -0.137236              -0.099553   \n",
       "1      -0.201629    1      -0.092542       -0.137236              -0.099553   \n",
       "2      -0.201629    0      -0.092542       -0.137236              -0.099553   \n",
       "3      -0.201629    0      -0.092542       -0.137236              -0.099553   \n",
       "4      -0.201629    0      -0.092542       -0.137236              -0.099553   \n",
       "\n",
       "   num persons worked for employer  own business or self employed  ...  \\\n",
       "0                                0                              0  ...   \n",
       "1                                1                              0  ...   \n",
       "2                                0                              0  ...   \n",
       "3                                0                              0  ...   \n",
       "4                                0                              0  ...   \n",
       "\n",
       "   country of birth self_Trinadad&Tobago  country of birth self_United-States  \\\n",
       "0                                      0                                    1   \n",
       "1                                      0                                    1   \n",
       "2                                      0                                    0   \n",
       "3                                      0                                    1   \n",
       "4                                      0                                    1   \n",
       "\n",
       "   country of birth self_Unknown  country of birth self_Vietnam  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "2                              0                              1   \n",
       "3                              0                              0   \n",
       "4                              0                              0   \n",
       "\n",
       "   country of birth self_Yugoslavia  \\\n",
       "0                                 0   \n",
       "1                                 0   \n",
       "2                                 0   \n",
       "3                                 0   \n",
       "4                                 0   \n",
       "\n",
       "   citizenship_Foreign born- Not a citizen of U S   \\\n",
       "0                                                0   \n",
       "1                                                0   \n",
       "2                                                1   \n",
       "3                                                0   \n",
       "4                                                0   \n",
       "\n",
       "   citizenship_Foreign born- U S citizen by naturalization  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "3                                                  0         \n",
       "4                                                  0         \n",
       "\n",
       "   citizenship_Native- Born abroad of American Parent(s)  \\\n",
       "0                                                  0       \n",
       "1                                                  0       \n",
       "2                                                  0       \n",
       "3                                                  0       \n",
       "4                                                  0       \n",
       "\n",
       "   citizenship_Native- Born in Puerto Rico or U S Outlying  \\\n",
       "0                                                  0         \n",
       "1                                                  0         \n",
       "2                                                  0         \n",
       "3                                                  0         \n",
       "4                                                  0         \n",
       "\n",
       "   citizenship_Native- Born in the United States  \n",
       "0                                              1  \n",
       "1                                              1  \n",
       "2                                              0  \n",
       "3                                              1  \n",
       "4                                              1  \n",
       "\n",
       "[5 rows x 246 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical = ['age', 'wage per hour', 'capital gains', 'capital losses', 'dividends from stocks']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "census_clean[numerical] = scaler.fit_transform(census_clean[numerical])\n",
    "census_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensionality Reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:12.850558Z",
     "start_time": "2020-03-16T09:29:47.983107Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>0.012432</td>\n",
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       "      <td>0.114889</td>\n",
       "      <td>-0.006549</td>\n",
       "      <td>0.021387</td>\n",
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       "      <td>0.066017</td>\n",
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       "      <td>-0.002048</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>0.036342</td>\n",
       "      <td>0.014168</td>\n",
       "      <td>-0.003058</td>\n",
       "      <td>-0.025863</td>\n",
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       "    <tr>\n",
       "      <th>detailed occupation recode</th>\n",
       "      <td>0.117414</td>\n",
       "      <td>0.563493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194498</td>\n",
       "      <td>0.138841</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.043680</td>\n",
       "      <td>-0.026726</td>\n",
       "      <td>0.515554</td>\n",
       "      <td>0.228952</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004534</td>\n",
       "      <td>-0.073951</td>\n",
       "      <td>0.004907</td>\n",
       "      <td>0.006822</td>\n",
       "      <td>-0.000252</td>\n",
       "      <td>0.073811</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>0.006073</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>-0.073924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wage per hour</th>\n",
       "      <td>0.036938</td>\n",
       "      <td>0.165980</td>\n",
       "      <td>0.194498</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.023230</td>\n",
       "      <td>-0.001082</td>\n",
       "      <td>0.010993</td>\n",
       "      <td>-0.005731</td>\n",
       "      <td>0.191543</td>\n",
       "      <td>0.054051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002167</td>\n",
       "      <td>0.008415</td>\n",
       "      <td>0.002299</td>\n",
       "      <td>-0.002486</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>-0.004456</td>\n",
       "      <td>0.008406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.052560</td>\n",
       "      <td>-0.025911</td>\n",
       "      <td>0.138841</td>\n",
       "      <td>0.023230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054963</td>\n",
       "      <td>0.065799</td>\n",
       "      <td>0.011132</td>\n",
       "      <td>0.053625</td>\n",
       "      <td>0.036327</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001436</td>\n",
       "      <td>0.004283</td>\n",
       "      <td>0.003981</td>\n",
       "      <td>0.001537</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.002048</td>\n",
       "      <td>-0.010319</td>\n",
       "      <td>0.006892</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.004301</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citizenship_Foreign born- Not a citizen of U S</th>\n",
       "      <td>-0.001346</td>\n",
       "      <td>0.003974</td>\n",
       "      <td>0.073811</td>\n",
       "      <td>-0.014599</td>\n",
       "      <td>0.002048</td>\n",
       "      <td>-0.010700</td>\n",
       "      <td>-0.009319</td>\n",
       "      <td>-0.019078</td>\n",
       "      <td>-0.006627</td>\n",
       "      <td>-0.024629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041365</td>\n",
       "      <td>-0.752010</td>\n",
       "      <td>0.270107</td>\n",
       "      <td>0.102205</td>\n",
       "      <td>0.018242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.046656</td>\n",
       "      <td>-0.025285</td>\n",
       "      <td>-0.023502</td>\n",
       "      <td>-0.752067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citizenship_Foreign born- U S citizen by naturalization</th>\n",
       "      <td>0.114889</td>\n",
       "      <td>0.036342</td>\n",
       "      <td>0.021680</td>\n",
       "      <td>0.009011</td>\n",
       "      <td>-0.010319</td>\n",
       "      <td>0.013232</td>\n",
       "      <td>0.006070</td>\n",
       "      <td>0.002143</td>\n",
       "      <td>0.030594</td>\n",
       "      <td>-0.015403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.032765</td>\n",
       "      <td>-0.487291</td>\n",
       "      <td>0.217383</td>\n",
       "      <td>0.071532</td>\n",
       "      <td>0.067059</td>\n",
       "      <td>-0.046656</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.016384</td>\n",
       "      <td>-0.015229</td>\n",
       "      <td>-0.487328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citizenship_Native- Born abroad of American Parent(s)</th>\n",
       "      <td>-0.006549</td>\n",
       "      <td>0.014168</td>\n",
       "      <td>0.006073</td>\n",
       "      <td>-0.001495</td>\n",
       "      <td>0.006892</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>0.001879</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>0.012954</td>\n",
       "      <td>0.009394</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004188</td>\n",
       "      <td>-0.264082</td>\n",
       "      <td>0.143246</td>\n",
       "      <td>0.021305</td>\n",
       "      <td>0.004188</td>\n",
       "      <td>-0.025285</td>\n",
       "      <td>-0.016384</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008253</td>\n",
       "      <td>-0.264102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citizenship_Native- Born in Puerto Rico or U S Outlying</th>\n",
       "      <td>0.021387</td>\n",
       "      <td>-0.003058</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>-0.004456</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>-0.002783</td>\n",
       "      <td>-0.004225</td>\n",
       "      <td>-0.006517</td>\n",
       "      <td>-0.001595</td>\n",
       "      <td>-0.011090</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001593</td>\n",
       "      <td>-0.245469</td>\n",
       "      <td>-0.011520</td>\n",
       "      <td>-0.003881</td>\n",
       "      <td>-0.001593</td>\n",
       "      <td>-0.023502</td>\n",
       "      <td>-0.015229</td>\n",
       "      <td>-0.008253</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.245487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citizenship_Native- Born in the United States</th>\n",
       "      <td>-0.064141</td>\n",
       "      <td>-0.025863</td>\n",
       "      <td>-0.073924</td>\n",
       "      <td>0.008406</td>\n",
       "      <td>0.004301</td>\n",
       "      <td>0.002041</td>\n",
       "      <td>0.004739</td>\n",
       "      <td>0.015496</td>\n",
       "      <td>-0.014457</td>\n",
       "      <td>0.027965</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050984</td>\n",
       "      <td>0.999925</td>\n",
       "      <td>-0.368643</td>\n",
       "      <td>-0.124195</td>\n",
       "      <td>-0.050984</td>\n",
       "      <td>-0.752067</td>\n",
       "      <td>-0.487328</td>\n",
       "      <td>-0.264102</td>\n",
       "      <td>-0.245487</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>246 rows × 246 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         age  \\\n",
       "age                                                 1.000000   \n",
       "detailed industry recode                            0.157822   \n",
       "detailed occupation recode                          0.117414   \n",
       "wage per hour                                       0.036938   \n",
       "sex                                                -0.052560   \n",
       "...                                                      ...   \n",
       "citizenship_Foreign born- Not a citizen of U S     -0.001346   \n",
       "citizenship_Foreign born- U S citizen by natura...  0.114889   \n",
       "citizenship_Native- Born abroad of American Par... -0.006549   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...  0.021387   \n",
       "citizenship_Native- Born in the United States      -0.064141   \n",
       "\n",
       "                                                    detailed industry recode  \\\n",
       "age                                                                 0.157822   \n",
       "detailed industry recode                                            1.000000   \n",
       "detailed occupation recode                                          0.563493   \n",
       "wage per hour                                                       0.165980   \n",
       "sex                                                                -0.025911   \n",
       "...                                                                      ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                      0.003974   \n",
       "citizenship_Foreign born- U S citizen by natura...                  0.036342   \n",
       "citizenship_Native- Born abroad of American Par...                  0.014168   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                 -0.003058   \n",
       "citizenship_Native- Born in the United States                      -0.025863   \n",
       "\n",
       "                                                    detailed occupation recode  \\\n",
       "age                                                                   0.117414   \n",
       "detailed industry recode                                              0.563493   \n",
       "detailed occupation recode                                            1.000000   \n",
       "wage per hour                                                         0.194498   \n",
       "sex                                                                   0.138841   \n",
       "...                                                                        ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                        0.073811   \n",
       "citizenship_Foreign born- U S citizen by natura...                    0.021680   \n",
       "citizenship_Native- Born abroad of American Par...                    0.006073   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                    0.008000   \n",
       "citizenship_Native- Born in the United States                        -0.073924   \n",
       "\n",
       "                                                    wage per hour       sex  \\\n",
       "age                                                      0.036938 -0.052560   \n",
       "detailed industry recode                                 0.165980 -0.025911   \n",
       "detailed occupation recode                               0.194498  0.138841   \n",
       "wage per hour                                            1.000000  0.023230   \n",
       "sex                                                      0.023230  1.000000   \n",
       "...                                                           ...       ...   \n",
       "citizenship_Foreign born- Not a citizen of U S          -0.014599  0.002048   \n",
       "citizenship_Foreign born- U S citizen by natura...       0.009011 -0.010319   \n",
       "citizenship_Native- Born abroad of American Par...      -0.001495  0.006892   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...      -0.004456 -0.008929   \n",
       "citizenship_Native- Born in the United States            0.008406  0.004301   \n",
       "\n",
       "                                                    capital gains  \\\n",
       "age                                                      0.053590   \n",
       "detailed industry recode                                 0.066017   \n",
       "detailed occupation recode                               0.002534   \n",
       "wage per hour                                           -0.001082   \n",
       "sex                                                      0.054963   \n",
       "...                                                           ...   \n",
       "citizenship_Foreign born- Not a citizen of U S          -0.010700   \n",
       "citizenship_Foreign born- U S citizen by natura...       0.013232   \n",
       "citizenship_Native- Born abroad of American Par...       0.000439   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...      -0.002783   \n",
       "citizenship_Native- Born in the United States            0.002041   \n",
       "\n",
       "                                                    capital losses  \\\n",
       "age                                                       0.063351   \n",
       "detailed industry recode                                  0.072586   \n",
       "detailed occupation recode                                0.043680   \n",
       "wage per hour                                             0.010993   \n",
       "sex                                                       0.065799   \n",
       "...                                                            ...   \n",
       "citizenship_Foreign born- Not a citizen of U S           -0.009319   \n",
       "citizenship_Foreign born- U S citizen by natura...        0.006070   \n",
       "citizenship_Native- Born abroad of American Par...        0.001879   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...       -0.004225   \n",
       "citizenship_Native- Born in the United States             0.004739   \n",
       "\n",
       "                                                    dividends from stocks  \\\n",
       "age                                                              0.104976   \n",
       "detailed industry recode                                         0.012774   \n",
       "detailed occupation recode                                      -0.026726   \n",
       "wage per hour                                                   -0.005731   \n",
       "sex                                                              0.011132   \n",
       "...                                                                   ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                  -0.019078   \n",
       "citizenship_Foreign born- U S citizen by natura...               0.002143   \n",
       "citizenship_Native- Born abroad of American Par...               0.000812   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...              -0.006517   \n",
       "citizenship_Native- Born in the United States                    0.015496   \n",
       "\n",
       "                                                    num persons worked for employer  \\\n",
       "age                                                                        0.140887   \n",
       "detailed industry recode                                                   0.642096   \n",
       "detailed occupation recode                                                 0.515554   \n",
       "wage per hour                                                              0.191543   \n",
       "sex                                                                        0.053625   \n",
       "...                                                                             ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                            -0.006627   \n",
       "citizenship_Foreign born- U S citizen by natura...                         0.030594   \n",
       "citizenship_Native- Born abroad of American Par...                         0.012954   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                        -0.001595   \n",
       "citizenship_Native- Born in the United States                             -0.014457   \n",
       "\n",
       "                                                    own business or self employed  \\\n",
       "age                                                                     -0.002265   \n",
       "detailed industry recode                                                 0.252694   \n",
       "detailed occupation recode                                               0.228952   \n",
       "wage per hour                                                            0.054051   \n",
       "sex                                                                      0.036327   \n",
       "...                                                                           ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                          -0.024629   \n",
       "citizenship_Foreign born- U S citizen by natura...                      -0.015403   \n",
       "citizenship_Native- Born abroad of American Par...                       0.009394   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                      -0.011090   \n",
       "citizenship_Native- Born in the United States                            0.027965   \n",
       "\n",
       "                                                    ...  \\\n",
       "age                                                 ...   \n",
       "detailed industry recode                            ...   \n",
       "detailed occupation recode                          ...   \n",
       "wage per hour                                       ...   \n",
       "sex                                                 ...   \n",
       "...                                                 ...   \n",
       "citizenship_Foreign born- Not a citizen of U S      ...   \n",
       "citizenship_Foreign born- U S citizen by natura...  ...   \n",
       "citizenship_Native- Born abroad of American Par...  ...   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...  ...   \n",
       "citizenship_Native- Born in the United States       ...   \n",
       "\n",
       "                                                    country of birth self_Trinadad&Tobago  \\\n",
       "age                                                                              0.004563   \n",
       "detailed industry recode                                                         0.003215   \n",
       "detailed occupation recode                                                       0.004534   \n",
       "wage per hour                                                                    0.002167   \n",
       "sex                                                                             -0.001436   \n",
       "...                                                                                   ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                                   0.041365   \n",
       "citizenship_Foreign born- U S citizen by natura...                               0.032765   \n",
       "citizenship_Native- Born abroad of American Par...                               0.004188   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                              -0.001593   \n",
       "citizenship_Native- Born in the United States                                   -0.050984   \n",
       "\n",
       "                                                    country of birth self_United-States  \\\n",
       "age                                                                           -0.064116   \n",
       "detailed industry recode                                                      -0.025854   \n",
       "detailed occupation recode                                                    -0.073951   \n",
       "wage per hour                                                                  0.008415   \n",
       "sex                                                                            0.004283   \n",
       "...                                                                                 ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                                -0.752010   \n",
       "citizenship_Foreign born- U S citizen by natura...                            -0.487291   \n",
       "citizenship_Native- Born abroad of American Par...                            -0.264082   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                            -0.245469   \n",
       "citizenship_Native- Born in the United States                                  0.999925   \n",
       "\n",
       "                                                    country of birth self_Unknown  \\\n",
       "age                                                                      0.032290   \n",
       "detailed industry recode                                                 0.019526   \n",
       "detailed occupation recode                                               0.004907   \n",
       "wage per hour                                                            0.002299   \n",
       "sex                                                                      0.003981   \n",
       "...                                                                           ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                           0.270107   \n",
       "citizenship_Foreign born- U S citizen by natura...                       0.217383   \n",
       "citizenship_Native- Born abroad of American Par...                       0.143246   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                      -0.011520   \n",
       "citizenship_Native- Born in the United States                           -0.368643   \n",
       "\n",
       "                                                    country of birth self_Vietnam  \\\n",
       "age                                                                     -0.004354   \n",
       "detailed industry recode                                                -0.003612   \n",
       "detailed occupation recode                                               0.006822   \n",
       "wage per hour                                                           -0.002486   \n",
       "sex                                                                      0.001537   \n",
       "...                                                                           ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                           0.102205   \n",
       "citizenship_Foreign born- U S citizen by natura...                       0.071532   \n",
       "citizenship_Native- Born abroad of American Par...                       0.021305   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                      -0.003881   \n",
       "citizenship_Native- Born in the United States                           -0.124195   \n",
       "\n",
       "                                                    country of birth self_Yugoslavia  \\\n",
       "age                                                                         0.012432   \n",
       "detailed industry recode                                                   -0.002048   \n",
       "detailed occupation recode                                                 -0.000252   \n",
       "wage per hour                                                               0.002593   \n",
       "sex                                                                         0.004633   \n",
       "...                                                                              ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                              0.018242   \n",
       "citizenship_Foreign born- U S citizen by natura...                          0.067059   \n",
       "citizenship_Native- Born abroad of American Par...                          0.004188   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                         -0.001593   \n",
       "citizenship_Native- Born in the United States                              -0.050984   \n",
       "\n",
       "                                                    citizenship_Foreign born- Not a citizen of U S   \\\n",
       "age                                                                                       -0.001346   \n",
       "detailed industry recode                                                                   0.003974   \n",
       "detailed occupation recode                                                                 0.073811   \n",
       "wage per hour                                                                             -0.014599   \n",
       "sex                                                                                        0.002048   \n",
       "...                                                                                             ...   \n",
       "citizenship_Foreign born- Not a citizen of U S                                             1.000000   \n",
       "citizenship_Foreign born- U S citizen by natura...                                        -0.046656   \n",
       "citizenship_Native- Born abroad of American Par...                                        -0.025285   \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                                        -0.023502   \n",
       "citizenship_Native- Born in the United States                                             -0.752067   \n",
       "\n",
       "                                                    citizenship_Foreign born- U S citizen by naturalization  \\\n",
       "age                                                                                          0.114889         \n",
       "detailed industry recode                                                                     0.036342         \n",
       "detailed occupation recode                                                                   0.021680         \n",
       "wage per hour                                                                                0.009011         \n",
       "sex                                                                                         -0.010319         \n",
       "...                                                                                               ...         \n",
       "citizenship_Foreign born- Not a citizen of U S                                              -0.046656         \n",
       "citizenship_Foreign born- U S citizen by natura...                                           1.000000         \n",
       "citizenship_Native- Born abroad of American Par...                                          -0.016384         \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                                          -0.015229         \n",
       "citizenship_Native- Born in the United States                                               -0.487328         \n",
       "\n",
       "                                                    citizenship_Native- Born abroad of American Parent(s)  \\\n",
       "age                                                                                         -0.006549       \n",
       "detailed industry recode                                                                     0.014168       \n",
       "detailed occupation recode                                                                   0.006073       \n",
       "wage per hour                                                                               -0.001495       \n",
       "sex                                                                                          0.006892       \n",
       "...                                                                                               ...       \n",
       "citizenship_Foreign born- Not a citizen of U S                                              -0.025285       \n",
       "citizenship_Foreign born- U S citizen by natura...                                          -0.016384       \n",
       "citizenship_Native- Born abroad of American Par...                                           1.000000       \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                                          -0.008253       \n",
       "citizenship_Native- Born in the United States                                               -0.264102       \n",
       "\n",
       "                                                    citizenship_Native- Born in Puerto Rico or U S Outlying  \\\n",
       "age                                                                                          0.021387         \n",
       "detailed industry recode                                                                    -0.003058         \n",
       "detailed occupation recode                                                                   0.008000         \n",
       "wage per hour                                                                               -0.004456         \n",
       "sex                                                                                         -0.008929         \n",
       "...                                                                                               ...         \n",
       "citizenship_Foreign born- Not a citizen of U S                                              -0.023502         \n",
       "citizenship_Foreign born- U S citizen by natura...                                          -0.015229         \n",
       "citizenship_Native- Born abroad of American Par...                                          -0.008253         \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                                           1.000000         \n",
       "citizenship_Native- Born in the United States                                               -0.245487         \n",
       "\n",
       "                                                    citizenship_Native- Born in the United States  \n",
       "age                                                                                     -0.064141  \n",
       "detailed industry recode                                                                -0.025863  \n",
       "detailed occupation recode                                                              -0.073924  \n",
       "wage per hour                                                                            0.008406  \n",
       "sex                                                                                      0.004301  \n",
       "...                                                                                           ...  \n",
       "citizenship_Foreign born- Not a citizen of U S                                          -0.752067  \n",
       "citizenship_Foreign born- U S citizen by natura...                                      -0.487328  \n",
       "citizenship_Native- Born abroad of American Par...                                      -0.264102  \n",
       "citizenship_Native- Born in Puerto Rico or U S ...                                      -0.245487  \n",
       "citizenship_Native- Born in the United States                                            1.000000  \n",
       "\n",
       "[246 rows x 246 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print out the column correlations of the dataset\n",
    "corr_matrix = census_clean.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:12.881505Z",
     "start_time": "2020-03-16T09:30:12.851556Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country of birth self_United-States                                   citizenship_Native- Born in the United States                               0.999925\n",
       "citizenship_Native- Born in the United States                         country of birth self_United-States                                         0.999925\n",
       "detailed household and family stat_Spouse of householder              detailed household summary in household_Spouse of householder               0.999788\n",
       "detailed household summary in household_Spouse of householder         detailed household and family stat_Spouse of householder                    0.999788\n",
       "detailed household summary in household_Child under 18 never married  detailed household and family stat_Child <18 never marr not in subfamily    0.998673\n",
       "                                                                                                                                                    ...   \n",
       "country of birth self_Cuba                                            country of birth mother_Cuba                                                0.804900\n",
       "country of birth self_Vietnam                                         country of birth father_Vietnam                                             0.801546\n",
       "country of birth father_Vietnam                                       country of birth self_Vietnam                                               0.801546\n",
       "country of birth self_Columbia                                        country of birth father_Columbia                                            0.801512\n",
       "country of birth father_Columbia                                      country of birth self_Columbia                                              0.801512\n",
       "Length: 110, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrs = corr_matrix.abs().unstack().sort_values(kind='quicksort', ascending=False)\n",
    "corrs[(corrs>0.80) & (corrs<1.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:12.942312Z",
     "start_time": "2020-03-16T09:30:12.882495Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set up PCA and the X vector for diminsionality reduction\n",
    "pca = PCA(random_state=666)\n",
    "census_X = census_clean.drop(\"income class\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:15.933313Z",
     "start_time": "2020-03-16T09:30:12.943310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=666,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(census_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:16.118816Z",
     "start_time": "2020-03-16T09:30:15.934333Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Explained Cumulative Variance</th>\n",
       "      <th>Explained Variance Ratio</th>\n",
       "      <th>age</th>\n",
       "      <th>detailed industry recode</th>\n",
       "      <th>detailed occupation recode</th>\n",
       "      <th>wage per hour</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital gains</th>\n",
       "      <th>capital losses</th>\n",
       "      <th>dividends from stocks</th>\n",
       "      <th>...</th>\n",
       "      <th>country of birth self_Trinadad&amp;Tobago</th>\n",
       "      <th>country of birth self_United-States</th>\n",
       "      <th>country of birth self_Unknown</th>\n",
       "      <th>country of birth self_Vietnam</th>\n",
       "      <th>country of birth self_Yugoslavia</th>\n",
       "      <th>citizenship_Foreign born- Not a citizen of U S</th>\n",
       "      <th>citizenship_Foreign born- U S citizen by naturalization</th>\n",
       "      <th>citizenship_Native- Born abroad of American Parent(s)</th>\n",
       "      <th>citizenship_Native- Born in Puerto Rico or U S Outlying</th>\n",
       "      <th>citizenship_Native- Born in the United States</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dimension 1</th>\n",
       "      <td>0.8084</td>\n",
       "      <td>0.8084</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.5181</td>\n",
       "      <td>0.3565</td>\n",
       "      <td>0.0068</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0024</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0005</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 2</th>\n",
       "      <td>0.9041</td>\n",
       "      <td>0.0957</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.6955</td>\n",
       "      <td>0.7042</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>-0.0047</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0016</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 3</th>\n",
       "      <td>0.9879</td>\n",
       "      <td>0.0838</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>0.4970</td>\n",
       "      <td>0.6136</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>-0.0034</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>-0.0050</td>\n",
       "      <td>-0.0035</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 4</th>\n",
       "      <td>0.9900</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0551</td>\n",
       "      <td>-0.0244</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>0.0641</td>\n",
       "      <td>-0.0102</td>\n",
       "      <td>-0.0033</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0042</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 5</th>\n",
       "      <td>0.9917</td>\n",
       "      <td>0.0017</td>\n",
       "      <td>0.6438</td>\n",
       "      <td>-0.0025</td>\n",
       "      <td>-0.0020</td>\n",
       "      <td>-0.0160</td>\n",
       "      <td>-0.0348</td>\n",
       "      <td>0.0637</td>\n",
       "      <td>0.0554</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0256</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 6</th>\n",
       "      <td>0.9927</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>-0.0735</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>-0.0601</td>\n",
       "      <td>0.0594</td>\n",
       "      <td>0.6949</td>\n",
       "      <td>0.1203</td>\n",
       "      <td>0.6763</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0100</td>\n",
       "      <td>-0.0037</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 7</th>\n",
       "      <td>0.9936</td>\n",
       "      <td>0.0009</td>\n",
       "      <td>-0.0102</td>\n",
       "      <td>0.0021</td>\n",
       "      <td>0.0013</td>\n",
       "      <td>-0.0755</td>\n",
       "      <td>0.0509</td>\n",
       "      <td>-0.2689</td>\n",
       "      <td>0.9435</td>\n",
       "      <td>0.0618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0074</td>\n",
       "      <td>-0.0009</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0049</td>\n",
       "      <td>-0.0021</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>0.0074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 8</th>\n",
       "      <td>0.9944</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>-0.0067</td>\n",
       "      <td>0.9886</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>-0.0283</td>\n",
       "      <td>0.0617</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0182</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0139</td>\n",
       "      <td>-0.0023</td>\n",
       "      <td>-0.0008</td>\n",
       "      <td>-0.0012</td>\n",
       "      <td>0.0182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 9</th>\n",
       "      <td>0.9952</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>-0.0341</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0782</td>\n",
       "      <td>-0.0537</td>\n",
       "      <td>-0.6423</td>\n",
       "      <td>-0.2119</td>\n",
       "      <td>0.7094</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0103</td>\n",
       "      <td>-0.0026</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0051</td>\n",
       "      <td>-0.0040</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>-0.0011</td>\n",
       "      <td>0.0103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimension 10</th>\n",
       "      <td>0.9957</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.1563</td>\n",
       "      <td>-0.0032</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>-0.0048</td>\n",
       "      <td>-0.1478</td>\n",
       "      <td>0.1097</td>\n",
       "      <td>0.1786</td>\n",
       "      <td>-0.0190</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>-0.0147</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>-0.0000</td>\n",
       "      <td>0.0131</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>-0.0001</td>\n",
       "      <td>-0.0014</td>\n",
       "      <td>-0.0147</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 247 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Explained Cumulative Variance  Explained Variance Ratio     age  \\\n",
       "Dimension 1                          0.8084                    0.8084  0.0065   \n",
       "Dimension 2                          0.9041                    0.0957 -0.0007   \n",
       "Dimension 3                          0.9879                    0.0838 -0.0067   \n",
       "Dimension 4                          0.9900                    0.0021  0.0551   \n",
       "Dimension 5                          0.9917                    0.0017  0.6438   \n",
       "Dimension 6                          0.9927                    0.0010 -0.0735   \n",
       "Dimension 7                          0.9936                    0.0009 -0.0102   \n",
       "Dimension 8                          0.9944                    0.0008  0.0054   \n",
       "Dimension 9                          0.9952                    0.0007 -0.0341   \n",
       "Dimension 10                         0.9957                    0.0005 -0.1563   \n",
       "\n",
       "              detailed industry recode  detailed occupation recode  \\\n",
       "Dimension 1                     0.5181                      0.3565   \n",
       "Dimension 2                    -0.6955                      0.7042   \n",
       "Dimension 3                     0.4970                      0.6136   \n",
       "Dimension 4                    -0.0244                     -0.0051   \n",
       "Dimension 5                    -0.0025                     -0.0020   \n",
       "Dimension 6                     0.0001                      0.0090   \n",
       "Dimension 7                     0.0021                      0.0013   \n",
       "Dimension 8                     0.0003                     -0.0067   \n",
       "Dimension 9                    -0.0011                      0.0001   \n",
       "Dimension 10                   -0.0032                      0.0067   \n",
       "\n",
       "              wage per hour     sex  capital gains  capital losses  \\\n",
       "Dimension 1          0.0068  0.0014         0.0024          0.0030   \n",
       "Dimension 2          0.0052  0.0096        -0.0047         -0.0011   \n",
       "Dimension 3          0.0029 -0.0034        -0.0067         -0.0050   \n",
       "Dimension 4          0.0641 -0.0102        -0.0033          0.0214   \n",
       "Dimension 5         -0.0160 -0.0348         0.0637          0.0554   \n",
       "Dimension 6         -0.0601  0.0594         0.6949          0.1203   \n",
       "Dimension 7         -0.0755  0.0509        -0.2689          0.9435   \n",
       "Dimension 8          0.9886  0.0035        -0.0283          0.0617   \n",
       "Dimension 9         -0.0782 -0.0537        -0.6423         -0.2119   \n",
       "Dimension 10        -0.0048 -0.1478         0.1097          0.1786   \n",
       "\n",
       "              dividends from stocks  ...  \\\n",
       "Dimension 1                  0.0003  ...   \n",
       "Dimension 2                 -0.0035  ...   \n",
       "Dimension 3                 -0.0035  ...   \n",
       "Dimension 4                  0.0075  ...   \n",
       "Dimension 5                  0.1426  ...   \n",
       "Dimension 6                  0.6763  ...   \n",
       "Dimension 7                  0.0618  ...   \n",
       "Dimension 8                  0.1018  ...   \n",
       "Dimension 9                  0.7094  ...   \n",
       "Dimension 10                -0.0190  ...   \n",
       "\n",
       "              country of birth self_Trinadad&Tobago  \\\n",
       "Dimension 1                                  0.0000   \n",
       "Dimension 2                                  0.0000   \n",
       "Dimension 3                                 -0.0000   \n",
       "Dimension 4                                 -0.0000   \n",
       "Dimension 5                                  0.0001   \n",
       "Dimension 6                                 -0.0001   \n",
       "Dimension 7                                  0.0001   \n",
       "Dimension 8                                 -0.0000   \n",
       "Dimension 9                                 -0.0000   \n",
       "Dimension 10                                -0.0000   \n",
       "\n",
       "              country of birth self_United-States  \\\n",
       "Dimension 1                               -0.0005   \n",
       "Dimension 2                               -0.0016   \n",
       "Dimension 3                               -0.0011   \n",
       "Dimension 4                                0.0030   \n",
       "Dimension 5                               -0.0256   \n",
       "Dimension 6                                0.0150   \n",
       "Dimension 7                                0.0074   \n",
       "Dimension 8                                0.0182   \n",
       "Dimension 9                                0.0103   \n",
       "Dimension 10                              -0.0147   \n",
       "\n",
       "              country of birth self_Unknown  country of birth self_Vietnam  \\\n",
       "Dimension 1                          0.0001                         0.0000   \n",
       "Dimension 2                         -0.0002                         0.0000   \n",
       "Dimension 3                          0.0000                         0.0000   \n",
       "Dimension 4                         -0.0004                         0.0001   \n",
       "Dimension 5                          0.0046                         0.0002   \n",
       "Dimension 6                         -0.0011                        -0.0003   \n",
       "Dimension 7                         -0.0009                        -0.0000   \n",
       "Dimension 8                         -0.0014                        -0.0003   \n",
       "Dimension 9                         -0.0026                        -0.0001   \n",
       "Dimension 10                         0.0001                         0.0000   \n",
       "\n",
       "              country of birth self_Yugoslavia  \\\n",
       "Dimension 1                            -0.0000   \n",
       "Dimension 2                             0.0000   \n",
       "Dimension 3                            -0.0000   \n",
       "Dimension 4                            -0.0000   \n",
       "Dimension 5                             0.0002   \n",
       "Dimension 6                            -0.0001   \n",
       "Dimension 7                             0.0000   \n",
       "Dimension 8                             0.0000   \n",
       "Dimension 9                            -0.0001   \n",
       "Dimension 10                           -0.0000   \n",
       "\n",
       "              citizenship_Foreign born- Not a citizen of U S   \\\n",
       "Dimension 1                                            0.0002   \n",
       "Dimension 2                                            0.0017   \n",
       "Dimension 3                                            0.0012   \n",
       "Dimension 4                                           -0.0042   \n",
       "Dimension 5                                            0.0101   \n",
       "Dimension 6                                           -0.0100   \n",
       "Dimension 7                                           -0.0049   \n",
       "Dimension 8                                           -0.0139   \n",
       "Dimension 9                                           -0.0051   \n",
       "Dimension 10                                           0.0131   \n",
       "\n",
       "              citizenship_Foreign born- U S citizen by naturalization  \\\n",
       "Dimension 1                                              0.0002         \n",
       "Dimension 2                                             -0.0001         \n",
       "Dimension 3                                             -0.0002         \n",
       "Dimension 4                                              0.0007         \n",
       "Dimension 5                                              0.0138         \n",
       "Dimension 6                                             -0.0037         \n",
       "Dimension 7                                             -0.0021         \n",
       "Dimension 8                                             -0.0023         \n",
       "Dimension 9                                             -0.0040         \n",
       "Dimension 10                                             0.0031         \n",
       "\n",
       "              citizenship_Native- Born abroad of American Parent(s)  \\\n",
       "Dimension 1                                              0.0000       \n",
       "Dimension 2                                             -0.0001       \n",
       "Dimension 3                                              0.0000       \n",
       "Dimension 4                                              0.0002       \n",
       "Dimension 5                                             -0.0001       \n",
       "Dimension 6                                             -0.0001       \n",
       "Dimension 7                                              0.0001       \n",
       "Dimension 8                                             -0.0008       \n",
       "Dimension 9                                             -0.0002       \n",
       "Dimension 10                                            -0.0001       \n",
       "\n",
       "              citizenship_Native- Born in Puerto Rico or U S Outlying  \\\n",
       "Dimension 1                                             -0.0000         \n",
       "Dimension 2                                              0.0001         \n",
       "Dimension 3                                              0.0001         \n",
       "Dimension 4                                              0.0003         \n",
       "Dimension 5                                              0.0019         \n",
       "Dimension 6                                             -0.0012         \n",
       "Dimension 7                                             -0.0004         \n",
       "Dimension 8                                             -0.0012         \n",
       "Dimension 9                                             -0.0011         \n",
       "Dimension 10                                            -0.0014         \n",
       "\n",
       "              citizenship_Native- Born in the United States  \n",
       "Dimension 1                                         -0.0005  \n",
       "Dimension 2                                         -0.0016  \n",
       "Dimension 3                                         -0.0011  \n",
       "Dimension 4                                          0.0030  \n",
       "Dimension 5                                         -0.0257  \n",
       "Dimension 6                                          0.0150  \n",
       "Dimension 7                                          0.0074  \n",
       "Dimension 8                                          0.0182  \n",
       "Dimension 9                                          0.0103  \n",
       "Dimension 10                                        -0.0147  \n",
       "\n",
       "[10 rows x 247 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pca_results(census_X, pca)\n",
    "results.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:16.133776Z",
     "start_time": "2020-03-16T09:30:16.119814Z"
    }
   },
   "outputs": [],
   "source": [
    "n_components = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:16.149733Z",
     "start_time": "2020-03-16T09:30:16.134775Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9962"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained_variance = results.iloc[n_components, 0]\n",
    "explained_variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.139440Z",
     "start_time": "2020-03-16T09:30:16.150734Z"
    }
   },
   "outputs": [],
   "source": [
    "pca = PCA(n_components=n_components, random_state=666)\n",
    "census_pca = pca.fit_transform(census_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.155371Z",
     "start_time": "2020-03-16T09:30:18.140442Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights for PC1\n",
      "Top 5 weights\n",
      "weeks worked in year               0.7746\n",
      "detailed industry recode           0.5181\n",
      "detailed occupation recode         0.3565\n",
      "num persons worked for employer    0.0584\n",
      "veterans benefits                  0.0155\n",
      "Name: 0, dtype: float64\n",
      "\n",
      "\n",
      "Bottom 5 weights\n",
      "marital status_Never married                                               -0.0061\n",
      "detailed household summary in household_Child under 18 never married       -0.0076\n",
      "detailed household and family stat_Child <18 never marr not in subfamily   -0.0076\n",
      "education_Children                                                         -0.0077\n",
      "tax filer stat_Nonfiler                                                    -0.0113\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print_pcs(census_X, pca, comp=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.170330Z",
     "start_time": "2020-03-16T09:30:18.157366Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights for PC2\n",
      "Top 5 weights\n",
      "detailed occupation recode        0.7042\n",
      "weeks worked in year              0.1413\n",
      "sex                               0.0096\n",
      "education_High school graduate    0.0077\n",
      "wage per hour                     0.0052\n",
      "Name: 1, dtype: float64\n",
      "\n",
      "\n",
      "Bottom 5 weights\n",
      "detailed household and family stat_Spouse of householder   -0.0039\n",
      "capital gains                                              -0.0047\n",
      "num persons worked for employer                            -0.0054\n",
      "education_Bachelors degree(BA AB BS)                       -0.0060\n",
      "detailed industry recode                                   -0.6955\n",
      "Name: 1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print_pcs(census_X, pca, comp=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.186320Z",
     "start_time": "2020-03-16T09:30:18.171351Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weights for PC3\n",
      "Top 5 weights\n",
      "detailed occupation recode                                   0.6136\n",
      "detailed industry recode                                     0.4970\n",
      "marital status_Never married                                 0.0055\n",
      "tax filer stat_Nonfiler                                      0.0043\n",
      "detailed household summary in household_Child 18 or older    0.0034\n",
      "Name: 2, dtype: float64\n",
      "\n",
      "\n",
      "Bottom 5 weights\n",
      "marital status_Married-civilian spouse present   -0.0059\n",
      "age                                              -0.0067\n",
      "capital gains                                    -0.0067\n",
      "num persons worked for employer                  -0.0221\n",
      "weeks worked in year                             -0.6129\n",
      "Name: 2, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print_pcs(census_X, pca, comp=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.201249Z",
     "start_time": "2020-03-16T09:30:18.187314Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comp_1</th>\n",
       "      <th>comp_2</th>\n",
       "      <th>comp_3</th>\n",
       "      <th>comp_4</th>\n",
       "      <th>comp_5</th>\n",
       "      <th>comp_6</th>\n",
       "      <th>comp_7</th>\n",
       "      <th>comp_8</th>\n",
       "      <th>comp_9</th>\n",
       "      <th>comp_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-30.047131</td>\n",
       "      <td>-0.549089</td>\n",
       "      <td>-0.323147</td>\n",
       "      <td>0.094969</td>\n",
       "      <td>2.231062</td>\n",
       "      <td>-0.462873</td>\n",
       "      <td>-0.139125</td>\n",
       "      <td>0.006473</td>\n",
       "      <td>-0.140666</td>\n",
       "      <td>-0.626465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24.507579</td>\n",
       "      <td>27.964472</td>\n",
       "      <td>-9.379467</td>\n",
       "      <td>-2.423512</td>\n",
       "      <td>0.749888</td>\n",
       "      <td>-0.076934</td>\n",
       "      <td>-0.010078</td>\n",
       "      <td>-0.303489</td>\n",
       "      <td>-0.036961</td>\n",
       "      <td>-0.881406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-30.070048</td>\n",
       "      <td>-0.546903</td>\n",
       "      <td>-0.289329</td>\n",
       "      <td>-0.060988</td>\n",
       "      <td>0.311240</td>\n",
       "      <td>-0.271709</td>\n",
       "      <td>-0.100106</td>\n",
       "      <td>-0.128212</td>\n",
       "      <td>-0.095432</td>\n",
       "      <td>-0.457406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-30.126841</td>\n",
       "      <td>-0.563604</td>\n",
       "      <td>-0.300821</td>\n",
       "      <td>-0.479269</td>\n",
       "      <td>-1.733652</td>\n",
       "      <td>0.174680</td>\n",
       "      <td>0.020914</td>\n",
       "      <td>0.052019</td>\n",
       "      <td>0.086779</td>\n",
       "      <td>0.237921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-30.126550</td>\n",
       "      <td>-0.563634</td>\n",
       "      <td>-0.301122</td>\n",
       "      <td>-0.476799</td>\n",
       "      <td>-1.704794</td>\n",
       "      <td>0.171387</td>\n",
       "      <td>0.020459</td>\n",
       "      <td>0.052262</td>\n",
       "      <td>0.085250</td>\n",
       "      <td>0.230916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comp_1     comp_2    comp_3    comp_4    comp_5    comp_6    comp_7  \\\n",
       "0 -30.047131  -0.549089 -0.323147  0.094969  2.231062 -0.462873 -0.139125   \n",
       "1  24.507579  27.964472 -9.379467 -2.423512  0.749888 -0.076934 -0.010078   \n",
       "2 -30.070048  -0.546903 -0.289329 -0.060988  0.311240 -0.271709 -0.100106   \n",
       "3 -30.126841  -0.563604 -0.300821 -0.479269 -1.733652  0.174680  0.020914   \n",
       "4 -30.126550  -0.563634 -0.301122 -0.476799 -1.704794  0.171387  0.020459   \n",
       "\n",
       "     comp_8    comp_9   comp_10  \n",
       "0  0.006473 -0.140666 -0.626465  \n",
       "1 -0.303489 -0.036961 -0.881406  \n",
       "2 -0.128212 -0.095432 -0.457406  \n",
       "3  0.052019  0.086779  0.237921  \n",
       "4  0.052262  0.085250  0.230916  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.DataFrame(census_pca, columns=[f'comp_{c+1}' for c in range(census_pca.shape[1])])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.217229Z",
     "start_time": "2020-03-16T09:30:18.203244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: income class, dtype: uint8"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = census_clean['income class']\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:18.339908Z",
     "start_time": "2020-03-16T09:30:18.218202Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_distribution(census_clean[['income class']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:19.406024Z",
     "start_time": "2020-03-16T09:30:18.340903Z"
    }
   },
   "outputs": [],
   "source": [
    "selector = SelectKBest()\n",
    "X_new = selector.fit_transform(census_X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:19.421981Z",
     "start_time": "2020-03-16T09:30:19.407022Z"
    }
   },
   "outputs": [],
   "source": [
    "# Get columns to keep and create new dataframe with those only\n",
    "cols = selector.get_support(indices=True)\n",
    "features_df_new = census_X.iloc[:,cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:19.436942Z",
     "start_time": "2020-03-16T09:30:19.422979Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 199523 entries, 0 to 199522\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                               Non-Null Count   Dtype  \n",
      "---  ------                                               --------------   -----  \n",
      " 0   detailed industry recode                             199523 non-null  uint8  \n",
      " 1   capital gains                                        199523 non-null  float64\n",
      " 2   num persons worked for employer                      199523 non-null  uint8  \n",
      " 3   weeks worked in year                                 199523 non-null  uint8  \n",
      " 4   education_Masters degree(MA MS MEng MEd MSW MBA)     199523 non-null  uint8  \n",
      " 5   education_Prof school degree (MD DDS DVM LLB JD)     199523 non-null  uint8  \n",
      " 6   tax filer stat_Joint both under 65                   199523 non-null  uint8  \n",
      " 7   tax filer stat_Nonfiler                              199523 non-null  uint8  \n",
      " 8   detailed household and family stat_Householder       199523 non-null  uint8  \n",
      " 9   detailed household summary in household_Householder  199523 non-null  uint8  \n",
      "dtypes: float64(1), uint8(9)\n",
      "memory usage: 3.2 MB\n"
     ]
    }
   ],
   "source": [
    "features_df_new.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:19.545683Z",
     "start_time": "2020-03-16T09:30:19.437941Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.3, stratify=y, random_state = 666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:21.196236Z",
     "start_time": "2020-03-16T09:30:19.546680Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score for training: 0.7578790715889477\n"
     ]
    }
   ],
   "source": [
    "clf_pca = SGDClassifier(random_state=666, loss='log', penalty='elasticnet', l1_ratio=0.7, class_weight={0:0.01, 1:1})\n",
    "\n",
    "clf_pca.fit(X_train, y_train)\n",
    "y_pred = clf_pca.predict(X_val)\n",
    "\n",
    "print(f'ROC AUC score for training: {roc_auc_score(y_val, y_pred)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  KBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:21.319905Z",
     "start_time": "2020-03-16T09:30:21.198231Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X_new, y, test_size = 0.3, stratify=y, random_state = 666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-16T09:30:22.890705Z",
     "start_time": "2020-03-16T09:30:21.320904Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC score for training: 0.7578790715889477\n"
     ]
    }
   ],
   "source": [
    "clf_kbest = SGDClassifier(random_state=666, loss='log', penalty='elasticnet', l1_ratio=0.7, class_weight={0:0.01, 1:1})\n",
    "\n",
    "clf_kbest.fit(X_train, y_train)\n",
    "y_pred_new = clf_kbest.predict(X_val)\n",
    "\n",
    "print(f'ROC AUC score for training: {roc_auc_score(y_val, y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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