Data_analysis_Predictive_modeling_Census_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3 Section a)\n",
    "#### Preprocessing and data analysis\n",
    "Read the data from csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable = pd.read_csv(\"http://facweb.cs.depaul.edu/mobasher/classes/csc478/Data/adult-modified.csv\", na_values=[\"?\"])\n",
    "\n",
    "vstable.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'workclass', 'education', 'marital-status', 'race', 'sex',\n",
       "       'hours-per-week', 'income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Showing the sample of first 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>Public</td>\n",
       "      <td>13</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Self-emp</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>5</td>\n",
       "      <td>Married</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>16</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52.0</td>\n",
       "      <td>Self-emp</td>\n",
       "      <td>9</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>50</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age workclass  education marital-status   race     sex  hours-per-week  \\\n",
       "0  39.0    Public         13         Single  White    Male              40   \n",
       "1  50.0  Self-emp         13        Married  White    Male              13   \n",
       "2  38.0   Private          9         Single  White    Male              40   \n",
       "3  53.0   Private          7        Married  Black    Male              40   \n",
       "4  28.0   Private         13        Married  Black  Female              40   \n",
       "5  37.0   Private         14        Married  White  Female              40   \n",
       "6  49.0   Private          5        Married  Black  Female              16   \n",
       "7  52.0  Self-emp          9        Married  White    Male              45   \n",
       "8  31.0   Private         14         Single  White  Female              50   \n",
       "9  42.0   Private         13        Married  White    Male              40   \n",
       "\n",
       "  income  \n",
       "0  <=50K  \n",
       "1  <=50K  \n",
       "2  <=50K  \n",
       "3  <=50K  \n",
       "4  <=50K  \n",
       "5  <=50K  \n",
       "6  <=50K  \n",
       "7   >50K  \n",
       "8   >50K  \n",
       "9   >50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examine missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below cell shows the general info about the table. We see there are missing values for age and workclass columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             9802 non-null   float64\n",
      " 1   workclass       9412 non-null   object \n",
      " 2   education       10000 non-null  int64  \n",
      " 3   marital-status  10000 non-null  object \n",
      " 4   race            10000 non-null  object \n",
      " 5   sex             10000 non-null  object \n",
      " 6   hours-per-week  10000 non-null  int64  \n",
      " 7   income          10000 non-null  object \n",
      "dtypes: float64(1), int64(2), object(5)\n",
      "memory usage: 625.1+ KB\n"
     ]
    }
   ],
   "source": [
    "vstable.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9802.000000</td>\n",
       "      <td>9412</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000</td>\n",
       "      <td>10000.000000</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5017</td>\n",
       "      <td>8556</td>\n",
       "      <td>6703</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.449806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.076600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.530300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.611949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.548172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.277197</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age workclass     education marital-status   race    sex  \\\n",
       "count   9802.000000      9412  10000.000000          10000  10000  10000   \n",
       "unique          NaN         3           NaN              2      5      2   \n",
       "top             NaN   Private           NaN         Single  White   Male   \n",
       "freq            NaN      6947           NaN           5017   8556   6703   \n",
       "mean      38.449806       NaN     10.076600            NaN    NaN    NaN   \n",
       "std       13.611949       NaN      2.548172            NaN    NaN    NaN   \n",
       "min       17.000000       NaN      1.000000            NaN    NaN    NaN   \n",
       "25%       27.000000       NaN      9.000000            NaN    NaN    NaN   \n",
       "50%       37.000000       NaN     10.000000            NaN    NaN    NaN   \n",
       "75%       47.000000       NaN     12.000000            NaN    NaN    NaN   \n",
       "max       90.000000       NaN     16.000000            NaN    NaN    NaN   \n",
       "\n",
       "        hours-per-week income  \n",
       "count     10000.000000  10000  \n",
       "unique             NaN      2  \n",
       "top                NaN  <=50K  \n",
       "freq               NaN   7621  \n",
       "mean         40.530300    NaN  \n",
       "std          12.277197    NaN  \n",
       "min           1.000000    NaN  \n",
       "25%          40.000000    NaN  \n",
       "50%          40.000000    NaN  \n",
       "75%          45.000000    NaN  \n",
       "max          99.000000    NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cells show the following variables have missing values. age and workclass.\n",
    "\n",
    "age: There are 198 missing values.\n",
    "\n",
    "workclass: There are 588 missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Instances in the data that contain a missing value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Self-emp</td>\n",
       "      <td>14</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Married</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>5</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>43</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9944</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>12</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9965</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Married</td>\n",
       "      <td>Amer-Indian</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9966</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>53</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age workclass  education marital-status         race     sex  \\\n",
       "19    NaN  Self-emp         14         Single        White  Female   \n",
       "27    NaN       NaN         10        Married        Asian    Male   \n",
       "40    NaN   Private          5        Married        White    Male   \n",
       "65    NaN   Private          9        Married        White    Male   \n",
       "80    NaN   Private          9         Single        White    Male   \n",
       "...   ...       ...        ...            ...          ...     ...   \n",
       "9944  NaN   Private         12         Single        White  Female   \n",
       "9965  NaN   Private         10        Married  Amer-Indian  Female   \n",
       "9966  NaN   Private         13        Married        White    Male   \n",
       "9993  NaN   Private          9        Married        White  Female   \n",
       "9998  NaN   Private          2        Married        White    Male   \n",
       "\n",
       "      hours-per-week income  \n",
       "19                45   >50K  \n",
       "27                60   >50K  \n",
       "40                43  <=50K  \n",
       "65                40  <=50K  \n",
       "80                30  <=50K  \n",
       "...              ...    ...  \n",
       "9944              45  <=50K  \n",
       "9965              40  <=50K  \n",
       "9966              50   >50K  \n",
       "9993              15  <=50K  \n",
       "9998              53  <=50K  \n",
       "\n",
       "[198 rows x 8 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable[vstable.age.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Married</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>60</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>32</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9907</th>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>15</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9926</th>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>8</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9927</th>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9938</th>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>588 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  education marital-status   race     sex  hours-per-week  \\\n",
       "27     NaN       NaN         10        Married  Asian    Male              60   \n",
       "61    32.0       NaN          4        Married  White    Male              40   \n",
       "69    25.0       NaN         10         Single  White    Male              40   \n",
       "77    67.0       NaN          6        Married  White    Male               2   \n",
       "106   17.0       NaN          6         Single  White  Female              32   \n",
       "...    ...       ...        ...            ...    ...     ...             ...   \n",
       "9907  68.0       NaN          9        Married  White    Male              15   \n",
       "9926  73.0       NaN          7        Married  White    Male               8   \n",
       "9927  18.0       NaN          7         Single  White  Female              20   \n",
       "9938  36.0       NaN         10         Single  White  Female              25   \n",
       "9987  67.0       NaN          4        Married  White    Male              40   \n",
       "\n",
       "     income  \n",
       "27     >50K  \n",
       "61    <=50K  \n",
       "69    <=50K  \n",
       "77    <=50K  \n",
       "106   <=50K  \n",
       "...     ...  \n",
       "9907  <=50K  \n",
       "9926  <=50K  \n",
       "9927  <=50K  \n",
       "9938  <=50K  \n",
       "9987  <=50K  \n",
       "\n",
       "[588 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable[vstable.workclass.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell shows the records with missing values. Missing records are also elaborated for each attribute \"age\" and \"workclass\".\n",
    "\n",
    "#### Using mean value to fill the missing values for all numeric attributes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_mean = vstable.age.mean()\n",
    "vstable.age.fillna(age_mean, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, workclass, education, marital-status, race, sex, hours-per-week, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable[vstable.age.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dropping all the rows with missing values for categorical attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vstable.drop(vstable[vstable.workclass.isnull()].index, axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
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       "      <th>marital-status</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, workclass, education, marital-status, race, sex, hours-per-week, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "vstable[vstable.workclass.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9412 entries, 0 to 9999\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   age             9412 non-null   float64\n",
      " 1   workclass       9412 non-null   object \n",
      " 2   education       9412 non-null   int64  \n",
      " 3   marital-status  9412 non-null   object \n",
      " 4   race            9412 non-null   object \n",
      " 5   sex             9412 non-null   object \n",
      " 6   hours-per-week  9412 non-null   int64  \n",
      " 7   income          9412 non-null   object \n",
      "dtypes: float64(1), int64(2), object(5)\n",
      "memory usage: 661.8+ KB\n"
     ]
    }
   ],
   "source": [
    "vstable.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above process has filled the missing values in \"Age\" attribute with the mean value of individual attribute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Validation to check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9412.000000</td>\n",
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       "      <th>unique</th>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Private</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4737</td>\n",
       "      <td>8062</td>\n",
       "      <td>6383</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.366342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.125266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.080217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.962039</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.542118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.884590</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age workclass    education marital-status   race   sex  \\\n",
       "count   9412.000000      9412  9412.000000           9412   9412  9412   \n",
       "unique          NaN         3          NaN              2      5     2   \n",
       "top             NaN   Private          NaN        Married  White  Male   \n",
       "freq            NaN      6947          NaN           4737   8062  6383   \n",
       "mean      38.366342       NaN    10.125266            NaN    NaN   NaN   \n",
       "std       12.962039       NaN     2.542118            NaN    NaN   NaN   \n",
       "min       17.000000       NaN     1.000000            NaN    NaN   NaN   \n",
       "25%       28.000000       NaN     9.000000            NaN    NaN   NaN   \n",
       "50%       37.000000       NaN    10.000000            NaN    NaN   NaN   \n",
       "75%       47.000000       NaN    13.000000            NaN    NaN   NaN   \n",
       "max       90.000000       NaN    16.000000            NaN    NaN   NaN   \n",
       "\n",
       "        hours-per-week income  \n",
       "count      9412.000000   9412  \n",
       "unique             NaN      2  \n",
       "top                NaN  <=50K  \n",
       "freq               NaN   7093  \n",
       "mean         41.080217    NaN  \n",
       "std          11.884590    NaN  \n",
       "min           1.000000    NaN  \n",
       "25%          40.000000    NaN  \n",
       "50%          40.000000    NaN  \n",
       "75%          45.000000    NaN  \n",
       "max          99.000000    NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [age, workclass, education, marital-status, race, sex, hours-per-week, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable[vstable.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Characteristics of the attributes\n",
    "#### Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9412.000000</td>\n",
       "      <td>9412.000000</td>\n",
       "      <td>9412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.366342</td>\n",
       "      <td>10.125266</td>\n",
       "      <td>41.080217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.962039</td>\n",
       "      <td>2.542118</td>\n",
       "      <td>11.884590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age    education  hours-per-week\n",
       "count  9412.000000  9412.000000     9412.000000\n",
       "mean     38.366342    10.125266       41.080217\n",
       "std      12.962039     2.542118       11.884590\n",
       "min      17.000000     1.000000        1.000000\n",
       "25%      28.000000     9.000000       40.000000\n",
       "50%      37.000000    10.000000       40.000000\n",
       "75%      47.000000    13.000000       45.000000\n",
       "max      90.000000    16.000000       99.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above statement describes the 5 point summary of numerical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'workclass' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Private     6947\n",
       "Public      1317\n",
       "Self-emp    1148\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "workclass_cnt = vstable[\"workclass\"].value_counts()\n",
    "print(\"Frequency of value in column 'workclass' :\\n\")\n",
    "workclass_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'marital-status' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Married    4737\n",
       "Single     4675\n",
       "Name: marital-status, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "marital_status_cnt = vstable[\"marital-status\"].value_counts()\n",
    "print(\"Frequency of value in column 'marital-status' :\\n\")\n",
    "marital_status_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'race' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "White          8062\n",
       "Black           892\n",
       "Asian           291\n",
       "Amer-Indian      92\n",
       "Hispanic         75\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "race_cnt = vstable[\"race\"].value_counts()\n",
    "print(\"Frequency of value in column 'race' :\\n\")\n",
    "race_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'sex' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Male      6383\n",
       "Female    3029\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sex_cnt = vstable[\"sex\"].value_counts()\n",
    "print(\"Frequency of value in column 'sex' :\\n\")\n",
    "sex_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frequency of value in column 'income' :\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<=50K    7093\n",
       ">50K     2319\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income_cnt = vstable[\"income\"].value_counts()\n",
    "print(\"Frequency of value in column 'income' :\\n\")\n",
    "income_cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bar charts for Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Income Distribution'}, xlabel='Income', ylabel='Count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FkpaWx63LtpJ0mqRlkm6UtEdTPbPL9kslze5kzBERERF10eketc8A37P9PGA34OfACcBi2zOAxWUZ4EBgRvmZA3wOQNI2wEnAXsCewEmN5C4iIiJiIutYoiZpK+CVwOkAth+2fS9wCLCgbLYAOLQ8PwQ4w5UrgGmStgdeCyyyvcr2PcAi4IBOxR0RERFRF1M6WPfOwG+BL0raDbgWeBfQZ/v2ss0dQF95vgNwa9PrV5Sy4cofR9Icqp44+vr6GBgYaNsbAZg7c21b62vom9r+utv93nvJmjVrJvX774Qc04iI7ulkojYF2AN4h+0rJX2Gdac5AbBtSW7HzmzPA+YBzJo1y/39/e2o9jHHnHB+W+trmDtzLZ9Y0t6P4Zaj+ttaXy8ZGBig3Z/9ZDeZj6mkjYFrgJW2D5a0M3A28FSqL59vsf2wpM2AM4AXA3cDR9i+pdRxInAc8CjwTtsXjv87iYhe1ckxaiuAFbavLMvnUiVud5ZTmpTHu8r6lcBOTa/fsZQNVx4R0Wnvohpb2/CvwKds7wLcQ5WAUR7vKeWfKtshaVfgSOBPqYZs/FdJ/iIiWtKxRM32HcCtkp5bivYDfgYsBBozN2cD55XnC4Gjy+zPvYHV5RTphcD+krYukwj2L2URER0jaUfgdcAXyrKAfam+dMITx9g2xt6eC+xXtj8EONv2Q7Z/DSyjmhQVEdGSTp76BHgH8BVJmwK/Ao6lSg7PkXQcsBw4vGx7AXAQVUP2YNkW26sknQxcXbb7qO1VHY47IuLTwD8CW5blpwL32m4MKm0eL/vYWFrbayWtLtvvAFzRVOeQY2yh8+NsW9Wp8bit6sS43fUxmcdj5rMf6Nq+R9LRRM32DcCsIVbtN8S2Bo4fpp75wPy2BhcRMQxJBwN32b5WUv947LPT42xb1anxuK3qxLjd9TGZx/jms+/v2r5H0r0jEhFRXy8D3iDpIGBz4ClU14WcJmlK6VVrHi/bGEu7QtIUYCuqSQUZYxsRGyS3kIqIGMT2ibZ3tD2dajLAD2wfBVwMHFY2GzzGtjH29rCyvUv5kZI2KzNGZwBXjdPbiIgJID1qERGtex9wtqR/Bq6nXNC7PJ4paRmwiiq5w/ZNks6hmki1Fjje9qPjH3ZE9KokahERI7A9AAyU579iiFmbtv8AvGmY158CnNK5CCNiIsupz4iIiIiaSqIWERERUVNJ1CIiIiJqKolaRERERE0lUYuIiIioqSRqERERETWVRC0iIiKippKoRURERNRUErWIiIiImkqiFhEREVFTSdQiIiIiaiqJWkRERERNJVGLiIiIqKkkahERERE1lUQtIiIioqaSqEVERETUVBK1iIiIiJrqaKIm6RZJSyTdIOmaUraNpEWSlpbHrUu5JJ0maZmkGyXt0VTP7LL9UkmzOxlzRERERF2MR4/aq2zvbntWWT4BWGx7BrC4LAMcCMwoP3OAz0GV2AEnAXsBewInNZK7iIiIiImsG6c+DwEWlOcLgEObys9w5QpgmqTtgdcCi2yvsn0PsAg4YJxjjoiIiBh3Uzpcv4GLJBn4b9vzgD7bt5f1dwB95fkOwK1Nr11RyoYrfxxJc6h64ujr62NgYKCNbwPmzlzb1voa+qa2v+52v/desmbNmkn9/jshxzQions6nai93PZKSU8DFkn6RfNK2y5J3AYrSeA8gFmzZrm/v78d1T7mmBPOb2t9DXNnruUTS9r7MdxyVH9b6+slAwMDtPuzn+xyTCMiuqejpz5tryyPdwHfohpjdmc5pUl5vKtsvhLYqenlO5ay4cojIiIiJrSOJWqSnixpy8ZzYH/gp8BCoDFzczZwXnm+EDi6zP7cG1hdTpFeCOwvaesyiWD/UhYRERExoXXy1Gcf8C1Jjf181fb3JF0NnCPpOGA5cHjZ/gLgIGAZ8CBwLIDtVZJOBq4u233U9qoOxh0RERFRCx1L1Gz/CthtiPK7gf2GKDdw/DB1zQfmtzvGiIiIiDrLnQkiIiIiaiqJWkRERERNJVGLiIiIqKkkahERERE1lUQtIiIioqaSqEVERETUVBK1iIiIiJpKohYRERFRU0nUIiIGkbS5pKsk/UTSTZI+Usp3lnSlpGWSviZp01K+WVleVtZPb6rrxFJ+s6TXduktRUSPSqIWEfFEDwH72t4N2B04oNyD+F+BT9neBbgHOK5sfxxwTyn/VNkOSbsCRwJ/ChwA/JekjcfzjUREb0uiFhExiCtryuIm5cfAvsC5pXwBcGh5fkhZpqzfT9WNjg8Bzrb9kO1fU93LeM/Ov4OImCiSqEVEDEHSxpJuAO4CFgG/BO61vbZssgLYoTzfAbgVoKxfDTy1uXyI10REjKpjN2WPiOhlth8Fdpc0DfgW8LxO7k/SHGAOQF9fHwMDA53c3bDmzlw7+kYd1De1uzF067jXQT77ga7teyRJ1CIiRmD7XkkXAy8FpkmaUnrNdgRWls1WAjsBKyRNAbYC7m4qb2h+zeD9zAPmAcyaNcv9/f0deDejO+aE87uy34a5M9fyiSXd+9d0y1H9Xdt3t+Wz7+/avkeSU58REYNI2q70pCFpKvAa4OfAxcBhZbPZwHnl+cKyTFn/A9su5UeWWaE7AzOAq8blTUTEhJAetYiIJ9oeWFBmaG4EnGP7O5J+Bpwt6Z+B64HTy/anA2dKWgasoprpie2bJJ0D/AxYCxxfTqlGRLQkiVpExCC2bwReNET5rxhi1qbtPwBvGqauU4BT2h1jREwOOfUZERERUVNJ1CIiIiJqKolaRERERE11PFErF428XtJ3ynLulRcRERHRgvHoUXsX1bT2htwrLyIiIqIFLSVqkl7WStkQ2+wIvA74QlkWuVdeRIyjsbZfERF10GqP2n+0WDbYp4F/BP5Ylp9K7pUXEeNrrO1XRETXjXgdNUkvBfYBtpP0nqZVTwFGPP0o6WDgLtvXSurfwDhH1en75HXq/mOduLdZXe9XNh7WrFkzqd9/J/TqMd2Q9isioi5Gu+DtpsAWZbstm8rvY91tVIbzMuANkg4CNqdqHD9Dh+6V1+n75HXqHmiduLdZXe9XNh4GBgbo1j0SJ6oePqYb0n5FRNTCiBmC7UuASyR9yfby9anY9onAiQClR+29to+S9HWqRvJshr5X3uU03StP0kLgq5I+CTyD3CsvIlqwIe1XRERdtNqVs5mkecD05tfY3ncM+3wfuVdeRIyfdrZfERHjqtVE7evA56lmb653kmR7ABgoz3OvvIgYTxvUfkVEdFOridpa25/raCQREZ2R9isielarl+f4tqS/k7S9pG0aPx2NLCKiPdJ+RUTParVHbXZ5/IemMgPPbm84ERFtl/YrInpWS4ma7Z07HUhERCek/YqIXtZSoibp6KHKbZ/R3nAiItor7VdE9LJWT32+pOn55sB+wHVAGrqIqLu0XxHRs1o99fmO5mVJ06guWBsRUWtpvyKil7U663OwB4CM+4iIXpT2KyJ6Rqtj1L5NNUsKqpsZPx84p1NBRUS0S9qviOhlrY5R+/em52uB5bZXdCCeiIh2S/sVET2rpVOf5ebGvwC2BLYGHu5kUBER7ZL2KyJ6WUuJmqTDgauo7sV5OHClpMM6GVhERDuk/YqIXtbqqc8PAC+xfReApO2A7wPndiqwiIg2SfsVET2r1VmfGzUaueLu9XhtREQ3pf2KiJ7Vao/a9yRdCJxVlo8ALuhMSBERbZX2KyJ61oiJmqRdgD7b/yDpz4GXl1WXA1/pdHAREWOV9isiJoLRetQ+DZwIYPubwDcBJM0s617fwdgiIjbEp0n7FRE9brRxGn22lwwuLGXTOxJRRER7pP2KiJ43WqI2bYR1U9sYR0REu00bYV3ar4joCaMlatdI+uvBhZLeClzbmZAiItoi7VdE9LzRxqi9G/iWpKNY17DNAjYF3tjBuCIiNtS7SfsVET1uxETN9p3APpJeBbygFJ9v+wejVSxpc+BSYLOyn3NtnyRpZ+Bs4KlUjedbbD8saTPgDODFVNc5OsL2LaWuE4HjgEeBd9q+cL3faURMKhvSfkVE1EVL11GzfTFw8XrW/RCwr+01kjYBLpP0XeA9wKdsny3p81QJ2OfK4z22d5F0JPCvwBGSdgWOBP4UeAbwfUl/YvvR9YwnIiahMbZfERG10LGrc7uypixuUn4M7Mu6W7csAA4tzw8py5T1+0lSKT/b9kO2fw0sA/bsVNwREZJ2knSxpJ9JuknSu0r5NpIWSVpaHrcu5ZJ0mqRlkm6UtEdTXbPL9kslze7We4qI3tTqnQnGRNLGVKc3dwH+E/glcK/ttWWTFcAO5fkOwK0AttdKWk11enQH4Iqmaptf07yvOcAcgL6+PgYGBtr6XubOXDv6RmPQN7X9dbf7vfeSNWvWTOr33wmT9JiuBebavk7SlsC1khYBxwCLbZ8q6QTgBOB9wIHAjPKzF9VZgr0kbQOcRDU2zqWehbbvGfd3FBE9qaOJWjk9ubukacC3gOd1cF/zgHkAs2bNcn9/f1vrP+aE89taX8PcmWv5xJL2fgy3HNXf1vp6ycDAAO3+7Ce7yXhMbd8O3F6e3y/p51RfEA8B+stmC4ABqkTtEOAM2waukDRN0vZl20W2VwGUZO8A1t3OKiJiRB1N1Bps3yvpYuClwDRJU0qv2o7AyrLZSmAnYIWkKcBWVJMKGuUNza+JiOgoSdOBFwFXUl1E9/ay6g6grzx/7IxA0ej5H658qP109KxAqzp19qBVnTjLsD4mYe/xY/LZD3Rt3yPpWKImaTvgkZKkTQVeQzVB4GLgMKqZn7OB88pLFpbly8v6H9i2pIXAVyV9kmoywQzgqk7FHRHRIGkL4BvAu23fVw2brZT2ye3aV6fPCrSqU2cPWtWJswzrYzKfkchn39+1fY+kk0dke2BBGae2EXCO7e9I+hlwtqR/Bq4HTi/bnw6cKWkZsIpqpie2b5J0DvAzqnEjx2fGZ0R0Wpmt/g3gK+VeoQB3Stre9u3l1OZdpXy4nv+VrDtV2igf6GTcETGxdCxRs30j1emCweW/YohZm7b/ALxpmLpOAU5pd4wREUMpM85PB35u+5NNqxo9/6fyxDMCb5d0NtVkgtUlmbsQ+FhjdiiwP+VG8RERreheH2NERH29DHgLsETSDaXs/VQJ2jmSjgOWA4eXdRcAB1FdPuhB4FgA26sknQxcXbb7aGNiQUREK5KoRUQMYvsyQMOs3m+I7Q0cP0xd84H57YsuIiaTjl3wNiIiIiI2TBK1iIiIiJpKohYRERFRU0nUIiIiImoqiVpERERETSVRi4iIiKipJGoRERERNZVELSIiIqKmkqhFRERE1FQStYiIiIiaSqIWERERUVNJ1CIiIiJqKolaRERERE0lUYuIiIioqSRqERERETWVRC0iIiKippKoRURERNRUErWIiIiImkqiFhEREVFTHUvUJO0k6WJJP5N0k6R3lfJtJC2StLQ8bl3KJek0Scsk3Shpj6a6Zpftl0qa3amYIyIiIuqkkz1qa4G5tncF9gaOl7QrcAKw2PYMYHFZBjgQmFF+5gCfgyqxA04C9gL2BE5qJHcRERERE1nHEjXbt9u+rjy/H/g5sANwCLCgbLYAOLQ8PwQ4w5UrgGmStgdeCyyyvcr2PcAi4IBOxR0RERFRF1PGYyeSpgMvAq4E+mzfXlbdAfSV5zsAtza9bEUpG6588D7mUPXE0dfXx8DAQPveADB35tq21tfQN7X9dbf7vfeSNWvWTOr33wk5phER3dPxRE3SFsA3gHfbvk/SY+tsW5LbsR/b84B5ALNmzXJ/f387qn3MMSec39b6GubOXMsnlrT3Y7jlqP621tdLBgYGaPdnP9nlmEZEdE9HZ31K2oQqSfuK7W+W4jvLKU3K412lfCWwU9PLdyxlw5VHRERETGidnPUp4HTg57Y/2bRqIdCYuTkbOK+p/Ogy+3NvYHU5RXohsL+krcskgv1LWURERMSE1slTny8D3gIskXRDKXs/cCpwjqTjgOXA4WXdBcBBwDLgQeBYANurJJ0MXF22+6jtVR2MOyIiIqIWOpao2b4M0DCr9xtiewPHD1PXfGB++6KLiIiIqL/cmSAiIiKippKoRURERNRUErWIiIiImkqiFhEREVFTSdQiIiIiaiqJWkRERERNJVGLiIiIqKkkahERQ5A0X9Jdkn7aVLaNpEWSlpbHrUu5JJ0maZmkGyXt0fSa2WX7pZJmD7WviIjhJFGLiBjal4ADBpWdACy2PQNYXJYBDgRmlJ85wOegSuyAk4C9gD2BkxrJXUREK5KoRUQMwfalwODb1R0CLCjPFwCHNpWf4coVwDRJ2wOvBRbZXmX7HmART0z+IiKG1cl7fUZETDR9tm8vz+8A+srzHYBbm7ZbUcqGK38CSXOoeuPo6+tjYGCgfVGvh7kz13Zlvw19U7sbQ7eOex3ksx/o2r5HkkQtImIMbFuS21jfPGAewKxZs9zf39+uqtfLMSec35X9NsyduZZPLOnev6Zbjurv2r67LZ99f9f2PZKc+oyIaN2d5ZQm5fGuUr4S2Klpux1L2XDlEREtSaIWEdG6hUBj5uZs4Lym8qPL7M+9gdXlFOmFwP6Sti6TCPYvZRERLcmpz4iIIUg6C+gHtpW0gmr25qnAOZKOA5YDh5fNLwAOApYBDwLHAtheJelk4Oqy3UdtD56gEBExrCRq0TXTOzAeYu7MtW0fZ3HLqa9ra33RG2y/eZhV+w2xrYHjh6lnPjC/jaFFxCSSU58RERERNZVELSIiIqKmkqhFRERE1FQStYiIiIiaymSCiAkkEzQiIiaWjvWoSZov6S5JP20q20bSIklLy+PWpVySTpO0TNKNkvZoes3ssv1SSbOH2ldERETERNTJU59f4ok3Hz4BWGx7BrC4LAMcCMwoP3OAz0GV2FFdu2gvYE/gpEZyFxERETHRdSxRs30pMPjCjocAC8rzBcChTeVnuHIFMK3cnuW1wCLbq2zfAyziiclfRERExIQ03mPU+sptVQDuAPrK8x2AW5u2W1HKhit/AklzqHrj6OvrY2BgoH1RU43T6YS+qe2vu93vvVM6cUwn8/GEHNOIiImma5MJbFuS21jfPGAewKxZs9zf39+uqgHaPpi6Ye7MtXxiSXs/hluO6m9rfZ3SiWM6mY8n5JhGREw04315jjvLKU3K412lfCWwU9N2O5ay4cojIiIiJrzxTtQWAo2Zm7OB85rKjy6zP/cGVpdTpBcC+0vaukwi2L+URUREREx4HTv1KeksoB/YVtIKqtmbpwLnSDoOWA4cXja/ADgIWAY8CBwLYHuVpJOBq8t2H7U9eIJCRERExITUsUTN9puHWbXfENsaOH6YeuYD89sYWkRERERPyC2kIiIiImoqiVpERERETSVRi4iIiKipJGoRERERNZVELSIiIqKmkqhFRERE1FQStYiIiIiaSqIWERERUVNJ1CIiIiJqKolaRERERE0lUYuIiIioqSRqERERETWVRC0iIiKippKoRURERNRUErWIiIiImkqiFhEREVFTSdQiIiIiaiqJWkRERERNJVGLiIiIqKkkahERERE1lUQtIiIioqZ6JlGTdICkmyUtk3RCt+OJiGhV2q+IGKueSNQkbQz8J3AgsCvwZkm7djeqiIjRpf2KiA3RE4kasCewzPavbD8MnA0c0uWYIiJakfYrIsZMtrsdw6gkHQYcYPutZfktwF623960zRxgTll8LnDzuAc6NtsCv+t2EBNIjmf79coxfZbt7bodxGCttF+lvFfbsHbrld+3aL/J/NkP235NGe9IOsX2PGBet+NYX5KusT2r23FMFDme7ZdjOj56tQ1rt/y+TV757IfWK6c+VwI7NS3vWMoiIuou7VdEjFmvJGpXAzMk7SxpU+BIYGGXY4qIaEXar4gYs5449Wl7raS3AxcCGwPzbd/U5bDaZdKf6mizHM/2yzHdABO8/eqE/L5NXvnsh9ATkwkiIiIiJqNeOfUZERERMekkUYuIiIioqSRqERERETWVRC16nqQnS9qoaXkjSU/qZkwRERHt0BOzPiciSc8CZtj+vqSpwBTb93c7rh61GHg1sKYsPwm4CNinaxH1OEl7AC8HDPzI9nVdDikmIEl/PtJ6298cr1iie/L/cGRJ1LpA0l9T3SpmG+A5VBfA/DywXzfj6mGb224kadhekx61sZP0IeBNQOOf5Bclfd32P3cxrJiYXl8en0b1xeoHZflVwI9Z9zsYE1T+H44uiVp3HE91o+YrAWwvlfS07obU0x6QtEej10fSi4HfdzmmXnYUsJvtPwBIOhW4AUiiFm1l+1gASRcBu9q+vSxvD3ypi6HF+Mn/w1EkUeuOh2w/LAkASVOoTjHF2Lwb+Lqk2wABTweO6GpEve02YHPgD2V5M3LLo+isnRpJWnEn8MxuBRPjKv8PR5FErTsukfR+YKqk1wB/B3y7yzH1LNtXS3oe8NxSdLPtR7oZU49bDdwkaRFVg/ka4CpJpwHYfmc3g4sJabGkC4GzyvIRwPe7GE+Mn/w/HEXuTNAFZYbiccD+VD1AF9r+n+5G1Xsk7Wv7B8MNSM5A5LGRNHuk9bYXjFcsMXlIeiPwyrJ4qe1vdTOeGB9D/T8EvuAkJ49JotYFkt5l+zOjlcXIJH3E9kmSvjjEatv+q3EPKiLGZNDMvycBG2fmX0QSta6QdJ3tPQaVXW/7Rd2KKaJB0sHAycCzqIZHiCrxfUpXA4sJq3nmn+3nSJoBfN52Zv5NUJKWMMJYNNsvHMdwai1j1MaRpDcDfwnsLGlh06otgVXdiap3SXrPSOttf3K8YplgPg38ObAkpx9inGTm3+RzcLcD6BVJ1MbXj4HbgW2BTzSV3w/c2JWIetuW3Q5ggroV+GmStBhHmfk3ydhe3u0YekVOfUbE40h6CdWpz0uAhxrl6aGMTpH0ceBe4GjgHVQz/35m+wPdjCs6T9L9PDEpXw1cA8y1/avxj6pecq/PLpC0t6SrJa2R9LCkRyXd1+24epWkZ0v6tqTfSrpL0nmSnt3tuHrYKcCDVNdS27LpJ6JTTgB+CywB/ga4APhgVyOK8fJp4B+AHajuSvBe4KvA2cD87oVVH+lR6wJJ1wBHAl8HZlF9i/wT2yd2NbAeJekK4D9Zdw2mI4F32N6re1H1Lkk/tf2CbscREROfpJ/Y3m1Q2Q22dx9q3WSUHrUusb2Mavr5o7a/CBzQ7Zh62JNsn2l7bfn5MlVvUIzNBZL273YQMfFJOqc8LpF04+CfbscX4+JBSYdL2qj8HM66u6KkJ4n0qHWFpEuBVwNfAO6gmmBwTL45rB9J25Sn7wPuoeoqN9VVzbdOD+XYlDEjTwYeLj+5PEd0hKTtbd9erqH2BBlwPvGVYSqfAV5K1X5fAfw91W3rXmz7si6GVwtJ1LqgNEp3AptS/UJuBfxX6WWLFkn6NdUftoZYbdsZpxZRc5I2Br5v+1XdjiWijpKodUG55dH5th8adeOIcabqGglHATvbPlnSTsD2tq/qcmgxQUlaDPy57dXdjiXGl6TtgL8GptN0ybDcWWadJGpdUG55tC9wKfA14Hu213Y3qt4l6eihym2fMd6xTASSPgf8EdjX9vMlbQ1cZPslXQ4tJihJ5wEvAhYBDzTKbb+za0HFuJD0Y+CHwLXAo41y29/oWlA1kwvedoHtYyVtAhwIvBn4T0mLbL+1y6H1quYEYnNgP+A6IIna2Oxlew9J1wPYvkfSpt0OKia0b5afmHyeZPt93Q6izpKodYntRyR9l2qM1VTgUCCJ2hjYfkfzsqRpVBMLYmweKeOGDI+dmvhjd0OKiar8rh2TMWqT1nckHWT7gm4HUle5PEcXSDpQ0peApcBfUM3+fHpXg5pYHgB27nYQPew04FvA0ySdAlwGfKy7IcVEZftR4I+Stup2LNEV76JK1n4v6T5J9+cC8I+XHrXuOJpqbNrfZELBhpP0bdZdb2cjYFfgnO5F1Ntsf0XStVSnkAUcavvnXQ4rJrY1wBJJGaM2ydjOXU9GkckE0fMk/VnT4lpgue0V3YpnIpE0x/a8bscRE5uk2UOV214w3rHE+JD0PNu/kLTHUOttXzfeMdVVErVxJOky2y8f4ia0uaDoGEjaHHgbsAvVPQJPz+zZ9pJ0ne0hG9KIiLGSNM/2HEkXNxU/9n/R9r5dCKuWkqhFz5L0NeARqqndB1L1pL2ru1H1LkmbDT4VL+l62y/qVkwxOUiaAfwL1bCFx27/lotWT1yS9gR+Y/uOsjybasz2LcCHba/qYni1kskE40zSxpJ+0e04Johdbf8/2/8NHAa8otsB9bjLASSd2VT2+i7FEpPLF4HPUQ1deBXVpXW+3NWIotM+T3WLOiS9kipRXwCsBjLcokkmE4wz249KulnSM23/ptvx9LhHGk9sr60uqB8bYFNJfwnsU+6eATz2zRfbuc5VdMpU24slqdzf88NlQsuHuh1YdMzGTb1mRwDzykVuvyHphu6FVT9J1Lpja+AmSVfx+BlOb+heSD1pt6Zp3AKmluWM+Rubt1HdOmoaT+xJM7kgaXTOQ5I2ApZKejvVDbm36HJM0VkbS5pSxhXvB8xpWpfcpEkORnf8U7cDmAhsb9ztGCYS25cBl0m6xvbp3Y4nJpV3AU8C3gmcTHWLvSFngsaEcRZwiaTfAb+nGmuMpF2oTn9GkckE4yizFKPOmk93DiWnPiOinSTtDWxPdS/hB0rZnwBb5PIc6yRRG0eZpRh1JumLI6y27b8at2BiUpC0cKT1GQ4SkURtXElaYntmeT4FuCrXqIqIyUrSb4FbqU6DXUk1vvQxti/pRlwRdZLLc4yvx81S7GYgEcOR1CfpdEnfLcu7Sjqu23HFhPR04P3AC4DPAK8Bfmf7kiRpEZX0qI0jSY+ybpangKnAg2SWYtRISdC+CHzA9m6l9/f6Rm9wRCdI2gx4M/BvwEdsf7bLIUXUQmZ9jqPMUowesa3tcySdCI9do+7RbgcVE1NJ0F5HlaRNB04DvtXNmCLqJIlaRAz2gKSnUu67V2ZmZbp8tJ2kM6hOe15A1Yv20y6HFFE7OfUZEY8jaQ/gP6j+gf4U2A44zPaNXQ0sJhxJf2TdcJDmf0YZDhJRpEctIgCQ9BLgVtvXSfoz4G+obpJ8EbCiq8HFhGQ7E9oiRpE/koho+G/KTZKBfYAPAP8J3ENukhwR0RXpUYuIhtwkOSKiZtKjFhENG5dLcUB1k+QfNK3Ll7qY0CRZ0peblqdI+q2k76xnPc+QdG55vrukg1p4Tf9Q+5H0JElfkbRE0k8lXSZpC0nTJP1dC/W2tF3UWxK1iGho3CT5PHKT5Jh8HgBeIGlqWX4NsHJ9KpA0xfZttg8rRbsDoyZqI3gXcKftmbZfABxHdeH0aUArCVir20WNJVGLCABsnwLMBb4EvNzrpoRvBLyjW3FFjKMLqK7pBtV13c5qrJC0p6TLJV0v6ceSnlvKj5G0UNIPgMWSppfer02BjwJHSLpB0hHD1TGC7WlKFm3fbPsh4FTgOaXefyu9bIslXVd63w4pLxm83eN67iR9VtIx5fmpkn4m6UZJ/74BxzDaLKczIuIxtq8Youz/uhFLRBecDXyoJDMvBOYDryjrfgG8olwA+tXAx6hmRQPsAbzQ9ipJ0wFsPyzpQ8As228HkPSUEeoYynzgIkmHAYuBBbaXAicAL7C9e6l3CvBG2/dJ2ha4otzwfvB2/UPtpFw38Y3A82xb0rRWD1h0XhK1iIgIwPaNJdF6M1XvWrOtgAWSZlBd822TpnWLmibijGSkOoaK5wZJzwb2B14NXC3ppVRDE5oJ+JikVwJ/BHYA+lqIp2E18Afg9JKkrte4vOisnPqMiIhYZyHw7zSd9ixOBi4uY8VeD2zetO4BWjNSHUOyvcb2N23/HfBlhh7zdhTVhalfXHrP7hym7rU8/v/+5mUfa4E9gXOBg4Hvtfh+YhwkUYtxI+lTkt7dtHyhpC80LX9C0ntarGtA0qwWtz1GUm7wHBGtmE91O6slg8q3Yt14sWNarOt+YMux1iHpZZK2Ls83BXYFlg9T7122H5H0KuBZw+x/ObCrpM3K6c39St1bAFvZvgD4e2C3Ft9fjIMkajGefkR1IVUkbQRsC/xp0/p9gB+PVomk3Nw+IjrC9grbpw2x6uPAv0i6ntaHDV1MlRjdIOmIMdTxHKqZ2EuA64FrgG/Yvhv4UZm08G/AV4BZZbujqcbTMXg727cC51DdGu6cUidUydx3JN0IXAa09IU5xkfu9RnjRtIzgCtt7yRpJvBeqllNRwAPUnXXH0E1U2kKcDXwt7YfknQL8DWqKfMfB95WXn8d1TfgFbY/KOkAqgG6GwO/s71fmdU0y/bbJb0e+CCwKXA3cJTtO8stkz5TQjXwSmCLss+nlHj+1vYPO3aAIiIiBkmPWowb27cBayU9k6r37HLgSuClwCxgKfAF4AjbMynJUVMVd9vew/bZZXkK1TfJpSVJ2w74H+AvbO8GvGmIMC4D9rb9IqoZXv9Yyt8LHF/Gd7yCarDuXwIXlrLdgBs2+CBERESshyRqMd5+TJWkNRK1y5uWVwC/brocxAKqnq2Grw2q67+Bn5brfwHsDVxq+9cAw8zC2hG4sJwi+AfWnXr9EfBJSe8EppXBtVcDx0r6MDDT9v1je8sRERFjk0QtxltjnNpMqnESV1D1qO0DDIzy2sEzq34MvErSqDOnmvwH8NnSY/c3rJv1dCrwVmAq1ZiO59m+lCpRXAl8SdLR67GfiIiIDZZELcbbj6mmf6+y/Wjp9ZpGlax9A5heblkE8BbgkhHqOp3qWkfnlAs+XgG8UtLOAJK2GeI1zbOuZjcKJT3H9hLb/0rVk/Y8Sc+iun3L/1Cdkt1jLG84IiJirJKoxXhbQjXb84pBZattrwCOBb5eTk3+Efj8SJXZ/iTVzKUzqSYHzAG+KeknPPFUKcCHS/3XAr9rKn93mRl1I9W99L4L9AM/KTO0jmDdZIOIiIhxkVmfERERETWVHrWIiIiImkqiFhEREVFTSdQiIiIiaiqJWkRERERNJVGLiIiIqKkkahERERE1lUQtIiIioqb+PwYm+4izViEZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 338.4x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp1 = vstable[\"workclass\"].value_counts()\n",
    "temp2 = vstable[\"marital-status\"].value_counts()\n",
    "temp3 = vstable[\"race\"].value_counts()\n",
    "temp4 = vstable[\"sex\"].value_counts()\n",
    "temp5 = vstable[\"income\"].value_counts()\n",
    "\n",
    "fig = plt.figure(figsize=(10,4))\n",
    "ax1 = fig.add_subplot(121)\n",
    "ax1.set_xlabel('Workclass')\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title(\"Workclass Distribution\")\n",
    "temp1.plot(kind='bar', grid = True)\n",
    "\n",
    "ax1 = fig.add_subplot(122)\n",
    "ax1.set_xlabel('Marital Status')\n",
    "ax1.set_ylabel('Count')\n",
    "ax1.set_title(\"Marital Status Distribution\")\n",
    "temp2.plot(kind='bar', grid = True)\n",
    "\n",
    "fig2 = plt.figure(figsize=(10,4))\n",
    "ax2 = fig2.add_subplot(121)\n",
    "ax2.set_xlabel('Race')\n",
    "ax2.set_ylabel('Count')\n",
    "ax2.set_title(\"Race Distribution\")\n",
    "temp3.plot(kind='bar', grid = True)\n",
    "\n",
    "ax2 = fig2.add_subplot(122)\n",
    "ax2.set_xlabel('Sex')\n",
    "ax2.set_ylabel('Count')\n",
    "ax2.set_title(\"Sex Distribution\")\n",
    "temp4.plot(kind='bar', grid = True)\n",
    "\n",
    "fig3 = plt.figure(figsize=(4.7,4))\n",
    "ax3 = fig3.add_subplot(111)\n",
    "ax3.set_xlabel('Income')\n",
    "ax3.set_ylabel('Count')\n",
    "ax3.set_title(\"Income Distribution\")\n",
    "temp5.plot(kind='bar', grid = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above bar charts shows the distirbution of records for the following variables. \n",
    "\n",
    "Workclass: We see the distribution of data in the following order \"Private\" > \"Public\" > \"Self Emp\". \n",
    "\n",
    "Martial Status: The distribution of Married people are higher than Unmarried. \n",
    "\n",
    "Gender: The distribution of Male are higher than Female. \n",
    "\n",
    "Race: We see the distribution of data in the following order \"White\" > \"Black\" > \"Asian\" > \"Amer-Indian\" > \"Hispanic\".  \n",
    "\n",
    "Income: Majority of the people have income less than 50k. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Age')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vstable[\"age\"].plot(kind=\"hist\", bins=9,title = \"Histogram of Age\")\n",
    "plt.xlabel(\"Age\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the histogram of the age variable in 9 bins. We see the maximum number of people are below 40."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Education')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vstable[\"education\"].plot(kind=\"hist\", bins=7,title = \"Histogram of Education\")\n",
    "plt.xlabel(\"Education\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the histogram of the education variable in 7 bins. We see the maximum number of people are between 8 and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hours per week')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vstable[\"hours-per-week\"].plot(kind=\"hist\", bins=5,title = \"Histogram of Hours per week\")\n",
    "plt.xlabel(\"Hours per week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot shows the histogram of the hours per week variable in 5 bins. We see the maximum number of people are working between 20 and 40 hours per week."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-tabulation \n",
    "Cross-tabulations of the education attribute with the race attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Amer-Indian</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>49</td>\n",
       "      <td>4</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>35</td>\n",
       "      <td>67</td>\n",
       "      <td>350</td>\n",
       "      <td>23</td>\n",
       "      <td>2590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>26</td>\n",
       "      <td>64</td>\n",
       "      <td>206</td>\n",
       "      <td>11</td>\n",
       "      <td>1818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>75</td>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>1387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race       Amer-Indian  Asian  Black  Hispanic  White\n",
       "education                                            \n",
       "1                    0      0      1         0     11\n",
       "2                    0      1      4         3     38\n",
       "3                    0      4      5         1     71\n",
       "4                    5      5     14         6    150\n",
       "5                    0      3     19         2    118\n",
       "6                    8      3     30         4    223\n",
       "7                    4      6     49         4    261\n",
       "8                    0      2     17         3     78\n",
       "9                   35     67    350        23   2590\n",
       "10                  26     64    206        11   1818\n",
       "11                   5     10     33         4    337\n",
       "12                   4      5     33         3    259\n",
       "13                   5     75    102         8   1387\n",
       "14                   0     27     20         1    467\n",
       "15                   0     11      5         2    153\n",
       "16                   0      8      4         0    101"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab = pd.crosstab(vstable[\"education\"], vstable[\"race\"])\n",
    "ctab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(ctab.plot(kind=\"bar\",title = \"Count based on Education & Race\", ylabel=\"Count\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart and count shows the count of records based on \"Education\" and \"Race\". \n",
    "\n",
    "The chart clearly shows the education is maximum in 9 and white race is the majority. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>5443</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public</th>\n",
       "      <td>925</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-emp</th>\n",
       "      <td>725</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income     <=50K  >50K\n",
       "workclass             \n",
       "Private     5443  1504\n",
       "Public       925   392\n",
       "Self-emp     725   423"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab1 = pd.crosstab(vstable[\"workclass\"], vstable[\"income\"])\n",
    "ctab1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(ctab1.plot(kind=\"bar\",title = \"Count based on Workclass & Income\", ylabel=\"Count\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart and count shows the count of records based on \"Workclass\" and \"Income\". \n",
    "\n",
    "The chart clearly shows the majority of the people have income below 50k and belong to Private workclass. The difference in the income group within the Private workclass is large compared to Public and Sell Employed workclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Amer-Indian</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>65</td>\n",
       "      <td>204</td>\n",
       "      <td>664</td>\n",
       "      <td>64</td>\n",
       "      <td>5950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public</th>\n",
       "      <td>20</td>\n",
       "      <td>48</td>\n",
       "      <td>192</td>\n",
       "      <td>5</td>\n",
       "      <td>1052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-emp</th>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "      <td>36</td>\n",
       "      <td>6</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race       Amer-Indian  Asian  Black  Hispanic  White\n",
       "workclass                                            \n",
       "Private             65    204    664        64   5950\n",
       "Public              20     48    192         5   1052\n",
       "Self-emp             7     39     36         6   1060"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab2 = pd.crosstab(vstable[\"workclass\"], vstable[\"race\"])\n",
    "ctab2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(ctab2.plot(kind=\"bar\",title = \"Count based on Workclass & Race\", ylabel=\"Count\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart and count shows the count of records based on \"Workclass\" and \"Race\". \n",
    "\n",
    "The chart clearly shows the majority of the people belong to White race and fall under Private workclass. \n",
    "Black and Asian are next most popular race. Asians are slightly higher in the self employed workclass than black."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amer-Indian</th>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>224</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>773</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>5944</td>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income       <=50K  >50K\n",
       "race                    \n",
       "Amer-Indian     83     9\n",
       "Asian          224    67\n",
       "Black          773   119\n",
       "Hispanic        69     6\n",
       "White         5944  2118"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ctab3 = pd.crosstab(vstable[\"race\"], vstable[\"income\"])\n",
    "ctab3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(ctab3.plot(kind=\"bar\",title = \"Count based on Income & Race\", ylabel=\"Count\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart and count shows the count of records based on \"Income\" and \"Race\". \n",
    "\n",
    "The chart clearly shows the majority of the people have income below 50k and belong to White race. Black and Asian are the next most popular race. There are very few Amer-Indian and Hispanic people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amer-Indian</th>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>224</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>773</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>5944</td>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income       <=50K  >50K\n",
       "race                    \n",
       "Amer-Indian     83     9\n",
       "Asian          224    67\n",
       "Black          773   119\n",
       "Hispanic        69     6\n",
       "White         5944  2118"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg_pct = pd.crosstab(vstable[\"race\"], vstable['income'])\n",
    "gg_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "      <th>percent_low_income_race</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "      <td>92.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amer-Indian</th>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "      <td>90.217391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>773</td>\n",
       "      <td>119</td>\n",
       "      <td>86.659193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>224</td>\n",
       "      <td>67</td>\n",
       "      <td>76.975945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>5944</td>\n",
       "      <td>2118</td>\n",
       "      <td>73.728603</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income       <=50K  >50K  percent_low_income_race\n",
       "race                                             \n",
       "Hispanic        69     6                92.000000\n",
       "Amer-Indian     83     9                90.217391\n",
       "Black          773   119                86.659193\n",
       "Asian          224    67                76.975945\n",
       "White         5944  2118                73.728603"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg_pct[\"percent_low_income_race\"] = (gg_pct[\"<=50K\"]/(gg_pct[\"<=50K\"]+gg_pct[\">50K\"]))*100\n",
    "gg_pct = gg_pct.sort_values(\"percent_low_income_race\", ascending=False)\n",
    "gg_pct"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare and contrast the characteristics of the low-income and high-income categories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show(gg_pct[\"percent_low_income_race\"].plot(kind=\"bar\",title = \"Percentage of people in Low Income Class\", ylabel=\"Percentage %\", xlabel =\"Race\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the income distibution for people with different races. Among White and compared to others, we see least number of people belong to low class income group. We see more than 90% of the Hispanics belong to low income group. Nearly 90% of the people and 87% of Amer Indian race and Black race respectively belong to the low income group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">age</th>\n",
       "      <th>count</th>\n",
       "      <td>7093.000000</td>\n",
       "      <td>2319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.644604</td>\n",
       "      <td>43.632531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.340142</td>\n",
       "      <td>10.042135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>43.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">education</th>\n",
       "      <th>count</th>\n",
       "      <td>7093.000000</td>\n",
       "      <td>2319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.646976</td>\n",
       "      <td>11.588185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.397358</td>\n",
       "      <td>2.410764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">hours-per-week</th>\n",
       "      <th>count</th>\n",
       "      <td>7093.000000</td>\n",
       "      <td>2319.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.567038</td>\n",
       "      <td>45.708495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.868506</td>\n",
       "      <td>10.678794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income                      <=50K         >50K\n",
       "age            count  7093.000000  2319.000000\n",
       "               mean     36.644604    43.632531\n",
       "               std      13.340142    10.042135\n",
       "               min      17.000000    19.000000\n",
       "               25%      26.000000    36.000000\n",
       "               50%      35.000000    43.000000\n",
       "               75%      45.000000    50.000000\n",
       "               max      90.000000    90.000000\n",
       "education      count  7093.000000  2319.000000\n",
       "               mean      9.646976    11.588185\n",
       "               std       2.397358     2.410764\n",
       "               min       1.000000     2.000000\n",
       "               25%       9.000000     9.000000\n",
       "               50%       9.000000    12.000000\n",
       "               75%      10.000000    13.000000\n",
       "               max      16.000000    16.000000\n",
       "hours-per-week count  7093.000000  2319.000000\n",
       "               mean     39.567038    45.708495\n",
       "               std      11.868506    10.678794\n",
       "               min       1.000000     1.000000\n",
       "               25%      38.000000    40.000000\n",
       "               50%      40.000000    40.000000\n",
       "               75%      40.000000    50.000000\n",
       "               max      99.000000    99.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vstable.groupby('income').describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the average age among low class income group is 37 while their education is nearly 10 and people work around 39.5 hours per week.\n",
    "\n",
    "The average age among high class income group is 44 while their education is around 12 and these group is people are hard working people compared to low income group. They work around 47 hours per week."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workclass</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Private</th>\n",
       "      <td>5443</td>\n",
       "      <td>1504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public</th>\n",
       "      <td>925</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self-emp</th>\n",
       "      <td>725</td>\n",
       "      <td>423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income     <=50K  >50K\n",
       "workclass             \n",
       "Private     5443  1504\n",
       "Public       925   392\n",
       "Self-emp     725   423"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg1 = pd.crosstab(vstable[\"workclass\"], vstable['income'])\n",
    "gg1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the income distibution for people with different workclass. We see majority of the people belong to Private workclass. The ratio of Self Employeed people earning greater than 50K is high compared to Private and Public workclass. The ratio of people earning greater than 50K is least within Private workclass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marital-status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Married</th>\n",
       "      <td>2730</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Single</th>\n",
       "      <td>4363</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income          <=50K  >50K\n",
       "marital-status             \n",
       "Married          2730  2007\n",
       "Single           4363   312"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg2 = pd.crosstab(vstable[\"marital-status\"], vstable['income'])\n",
    "gg2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the income distibution for people with different marital status. There are 4983 married people and 5017 singles. W see majority of the singles are earning less than 50k. Among married people, the number of people earning more than 50k is much higher."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>race</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amer-Indian</th>\n",
       "      <td>83</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>224</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>773</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>69</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td>5944</td>\n",
       "      <td>2118</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income       <=50K  >50K\n",
       "race                    \n",
       "Amer-Indian     83     9\n",
       "Asian          224    67\n",
       "Black          773   119\n",
       "Hispanic        69     6\n",
       "White         5944  2118"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg3 = pd.crosstab(vstable[\"race\"], vstable['income'])\n",
    "gg3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the income distibution for people with different races. We see majority of the people belong to White race. Among White, we see 1/3rd people are earning more than 50K. The ratio of people earning more than 50K is least amongst Hispanic. Nearly 1/4th of the Asian population earn greater than 50K "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income</th>\n",
       "      <th>&lt;=50K</th>\n",
       "      <th>&gt;50K</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2669</td>\n",
       "      <td>360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4424</td>\n",
       "      <td>1959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income  <=50K  >50K\n",
       "sex                \n",
       "Female   2669   360\n",
       "Male     4424  1959"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gg4 = pd.crosstab(vstable[\"sex\"], vstable['income'])\n",
    "gg4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above chart shows us the income distibution for gender. The table shows us Males are a majority compared to Females.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 3 Section b)\n",
    "#### Predictive Modeling and Model Evaluation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading data from table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>Public</td>\n",
       "      <td>13</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>Self-emp</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Single</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Married</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Married</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age workclass  education marital-status   race     sex  hours-per-week  \\\n",
       "0  39.0    Public         13         Single  White    Male              40   \n",
       "1  50.0  Self-emp         13        Married  White    Male              13   \n",
       "2  38.0   Private          9         Single  White    Male              40   \n",
       "3  53.0   Private          7        Married  Black    Male              40   \n",
       "4  28.0   Private         13        Married  Black  Female              40   \n",
       "\n",
       "  income  \n",
       "0  <=50K  \n",
       "1  <=50K  \n",
       "2  <=50K  \n",
       "3  <=50K  \n",
       "4  <=50K  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_records = vstable[['age', 'workclass', 'education', 'marital-status', 'race', 'sex','hours-per-week','income']]\n",
    "vs_records.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting into standardised variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Public</th>\n",
       "      <th>workclass_Self-emp</th>\n",
       "      <th>marital-status_Married</th>\n",
       "      <th>marital-status_Single</th>\n",
       "      <th>race_Amer-Indian</th>\n",
       "      <th>race_Asian</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Hispanic</th>\n",
       "      <th>race_White</th>\n",
       "      <th>sex_Female</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>income_&lt;=50K</th>\n",
       "      <th>income_&gt;50K</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39.0</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.0</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.0</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37.0</td>\n",
       "      <td>14</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49.0</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52.0</td>\n",
       "      <td>9</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31.0</td>\n",
       "      <td>14</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  education  hours-per-week  workclass_Private  workclass_Public  \\\n",
       "0  39.0         13              40                  0                 1   \n",
       "1  50.0         13              13                  0                 0   \n",
       "2  38.0          9              40                  1                 0   \n",
       "3  53.0          7              40                  1                 0   \n",
       "4  28.0         13              40                  1                 0   \n",
       "5  37.0         14              40                  1                 0   \n",
       "6  49.0          5              16                  1                 0   \n",
       "7  52.0          9              45                  0                 0   \n",
       "8  31.0         14              50                  1                 0   \n",
       "9  42.0         13              40                  1                 0   \n",
       "\n",
       "   workclass_Self-emp  marital-status_Married  marital-status_Single  \\\n",
       "0                   0                       0                      1   \n",
       "1                   1                       1                      0   \n",
       "2                   0                       0                      1   \n",
       "3                   0                       1                      0   \n",
       "4                   0                       1                      0   \n",
       "5                   0                       1                      0   \n",
       "6                   0                       1                      0   \n",
       "7                   1                       1                      0   \n",
       "8                   0                       0                      1   \n",
       "9                   0                       1                      0   \n",
       "\n",
       "   race_Amer-Indian  race_Asian  race_Black  race_Hispanic  race_White  \\\n",
       "0                 0           0           0              0           1   \n",
       "1                 0           0           0              0           1   \n",
       "2                 0           0           0              0           1   \n",
       "3                 0           0           1              0           0   \n",
       "4                 0           0           1              0           0   \n",
       "5                 0           0           0              0           1   \n",
       "6                 0           0           1              0           0   \n",
       "7                 0           0           0              0           1   \n",
       "8                 0           0           0              0           1   \n",
       "9                 0           0           0              0           1   \n",
       "\n",
       "   sex_Female  sex_Male  income_<=50K  income_>50K  \n",
       "0           0         1             1            0  \n",
       "1           0         1             1            0  \n",
       "2           0         1             1            0  \n",
       "3           0         1             1            0  \n",
       "4           1         0             1            0  \n",
       "5           1         0             1            0  \n",
       "6           1         0             1            0  \n",
       "7           0         1             0            1  \n",
       "8           1         0             0            1  \n",
       "9           0         1             0            1  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_matrix = pd.get_dummies(vs_records[['age', 'workclass', 'education', 'marital-status', 'race', 'sex','hours-per-week','income']])\n",
    "vs_matrix.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping column  income_<=50K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "del vs_matrix['income_<=50K']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: income_>50K, dtype: uint8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_target = vs_matrix['income_>50K']\n",
    "vs_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'education', 'hours-per-week', 'workclass_Private',\n",
       "       'workclass_Public', 'workclass_Self-emp', 'marital-status_Married',\n",
       "       'marital-status_Single', 'race_Amer-Indian', 'race_Asian', 'race_Black',\n",
       "       'race_Hispanic', 'race_White', 'sex_Female', 'sex_Male', 'income_>50K'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_matrix.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs_matrix = vs_matrix[['age', 'education', 'hours-per-week', 'workclass_Private','workclass_Public', 'workclass_Self-emp', 'marital-status_Married','marital-status_Single', 'race_Amer-Indian', 'race_Asian', 'race_Black','race_Hispanic', 'race_White', 'sex_Female', 'sex_Male']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  education  hours-per-week  workclass_Private  workclass_Public  \\\n",
       "0  39.0         13              40                  0                 1   \n",
       "1  50.0         13              13                  0                 0   \n",
       "2  38.0          9              40                  1                 0   \n",
       "3  53.0          7              40                  1                 0   \n",
       "4  28.0         13              40                  1                 0   \n",
       "\n",
       "   workclass_Self-emp  marital-status_Married  marital-status_Single  \\\n",
       "0                   0                       0                      1   \n",
       "1                   1                       1                      0   \n",
       "2                   0                       0                      1   \n",
       "3                   0                       1                      0   \n",
       "4                   0                       1                      0   \n",
       "\n",
       "   race_Amer-Indian  race_Asian  race_Black  race_Hispanic  race_White  \\\n",
       "0                 0           0           0              0           1   \n",
       "1                 0           0           0              0           1   \n",
       "2                 0           0           0              0           1   \n",
       "3                 0           0           1              0           0   \n",
       "4                 0           0           1              0           0   \n",
       "\n",
       "   sex_Female  sex_Male  \n",
       "0           0         1  \n",
       "1           0         1  \n",
       "2           0         1  \n",
       "3           0         1  \n",
       "4           1         0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_matrix.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the data into training and test sets (80%-20% split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1883, 15)\n"
     ]
    },
    {
     "data": {
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       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>30.0</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  education  hours-per-week  workclass_Private  workclass_Public  \\\n",
       "8419  56.0         10              45                  1                 0   \n",
       "2629  54.0          9              45                  1                 0   \n",
       "2523  52.0         11              35                  1                 0   \n",
       "7143  42.0         16              60                  0                 0   \n",
       "5902  30.0          9              50                  0                 0   \n",
       "\n",
       "      workclass_Self-emp  marital-status_Married  marital-status_Single  \\\n",
       "8419                   0                       1                      0   \n",
       "2629                   0                       1                      0   \n",
       "2523                   0                       0                      1   \n",
       "7143                   1                       1                      0   \n",
       "5902                   1                       1                      0   \n",
       "\n",
       "      race_Amer-Indian  race_Asian  race_Black  race_Hispanic  race_White  \\\n",
       "8419                 0           0           0              0           1   \n",
       "2629                 0           0           0              0           1   \n",
       "2523                 0           0           0              0           1   \n",
       "7143                 0           0           0              0           1   \n",
       "5902                 0           0           0              0           1   \n",
       "\n",
       "      sex_Female  sex_Male  \n",
       "8419           0         1  \n",
       "2629           0         1  \n",
       "2523           1         0  \n",
       "7143           0         1  \n",
       "5902           0         1  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "vs_train, vs_test, vs_target_train, vs_target_test = train_test_split(vs_matrix, vs_target, test_size=0.2, random_state=33)\n",
    "\n",
    "print(vs_test.shape)\n",
    "vs_test[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7529, 15)\n"
     ]
    },
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7852</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>38.449806</td>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4791</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6540</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age  education  hours-per-week  workclass_Private  \\\n",
       "287   50.000000         10              45                  1   \n",
       "7852  41.000000          9              40                  1   \n",
       "5887  38.449806          9              50                  0   \n",
       "4791  44.000000          9              40                  1   \n",
       "6540  21.000000         10              45                  1   \n",
       "\n",
       "      workclass_Public  workclass_Self-emp  marital-status_Married  \\\n",
       "287                  0                   0                       0   \n",
       "7852                 0                   0                       0   \n",
       "5887                 1                   0                       0   \n",
       "4791                 0                   0                       0   \n",
       "6540                 0                   0                       0   \n",
       "\n",
       "      marital-status_Single  race_Amer-Indian  race_Asian  race_Black  \\\n",
       "287                       1                 0           0           0   \n",
       "7852                      1                 0           0           0   \n",
       "5887                      1                 0           0           0   \n",
       "4791                      1                 0           0           0   \n",
       "6540                      1                 0           0           0   \n",
       "\n",
       "      race_Hispanic  race_White  sex_Female  sex_Male  \n",
       "287               0           1           0         1  \n",
       "7852              0           1           1         0  \n",
       "5887              0           1           0         1  \n",
       "4791              0           1           1         0  \n",
       "6540              0           1           0         1  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(vs_train.shape)\n",
    "vs_train[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8419    0\n",
       "2629    0\n",
       "2523    0\n",
       "7143    1\n",
       "5902    0\n",
       "Name: income_>50K, dtype: uint8"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vs_target_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes (Gaussian)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors, tree, naive_bayes\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbclf = naive_bayes.GaussianNB()\n",
    "nbclf = nbclf.fit(vs_train, vs_target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 0 ... 1 0 1]\n"
     ]
    }
   ],
   "source": [
    "nbpreds_test = nbclf.predict(vs_test)\n",
    "print(nbpreds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.72823779 0.70488323 0.73142251 0.70456961 0.73326249 0.70669501\n",
      " 0.7120085  0.7183847  0.73113709 0.70744681]\n",
      "Overall Accuracy on X-Val: 0.72 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(nbclf, vs_matrix, vs_target, cv=10)\n",
    "print(cv_scores)\n",
    "print(\"Overall Accuracy on X-Val: %0.2f (+/- %0.2f)\" % (cv_scores.mean(), cv_scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on Training:  0.720547217425953\n",
      "Score on Test:  0.7190653212958046\n"
     ]
    }
   ],
   "source": [
    "print(\"Score on Training: \", nbclf.score(vs_train, vs_target_train))\n",
    "print(\"Score on Test: \", nbclf.score(vs_test, vs_target_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gaussian Naive Bayes classification is performed on the dataset. The cross model selection, training model and test model have a accuracy score of 0.72. This simplifies there is no overfitting or underfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "treeclf = tree.DecisionTreeClassifier(criterion='gini')\n",
    "treeclf = treeclf.fit(vs_train, vs_target_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 ... 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "treepreds_test = treeclf.predict(vs_test)\n",
    "print(treepreds_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.75159236 0.77070064 0.77282378 0.77045696 0.77470776 0.77045696\n",
      " 0.75132837 0.77683316 0.77258236 0.7606383 ]\n",
      "Overall Accuracy on X-Val: 0.77 (+/- 0.02)\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(treeclf, vs_matrix, vs_target, cv=10)\n",
    "print(cv_scores)\n",
    "print(\"Overall Accuracy on X-Val: %0.2f (+/- %0.2f)\" % (cv_scores.mean(), cv_scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on Training:  0.942887501660247\n",
      "Score on Test:  0.7684545937334042\n"
     ]
    }
   ],
   "source": [
    "print(\"Score on Training: \",treeclf.score(vs_train, vs_target_train))\n",
    "print(\"Score on Test: \",treeclf.score(vs_test, vs_target_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision tree classification is performed on the dataset. The accuracy score on the training set is higher than the testing set. This concludes the model is overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear discriminant analysis (LDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "\n",
    "ldclf = LinearDiscriminantAnalysis()\n",
    "ldclf = ldclf.fit(vs_train, vs_target_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n",
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.81210191 0.80997877 0.79193206 0.81721573 0.80977683 0.78958555\n",
      " 0.79914984 0.80871413 0.82040383 0.81702128]\n",
      "Overall Accuracy on X-Val: 0.81 (+/- 0.02)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "cv_scores = cross_val_score(ldclf, vs_matrix, vs_target, cv=10)\n",
    "print(cv_scores)\n",
    "print(\"Overall Accuracy on X-Val: %0.2f (+/- %0.2f)\" % (cv_scores.mean(), cv_scores.std() * 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Accuracy score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on Training:  0.8063487846991633\n",
      "Score on Test:  0.8109399893786511\n"
     ]
    }
   ],
   "source": [
    "print(\"Score on Training: \", ldclf.score(vs_train, vs_target_train))\n",
    "print(\"Score on Test: \", ldclf.score(vs_test, vs_target_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear discriminant analysis classification is performed on the dataset. The cross model selection, training model and test model have a accuracy score of 0.81. This concludes there is no overfitting or underfitting.\n",
    "\n",
    "This is best performing and fitting model among Decision Tree, LDA and Naive Bayes classification models"
   ]
  }
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