Naive Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48b5b9de",
   "metadata": {},
   "source": [
    "# Prepare a classification model using Naive Bayes for salary data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed92ea54",
   "metadata": {},
   "source": [
    "## Data Description:\n",
    "\n",
    "age -- age of a person\n",
    "\n",
    "workclass\t-- A work class is a grouping of work \n",
    "\n",
    "education\t-- Education of an individuals\t\n",
    "\n",
    "maritalstatus -- Marital status of an individulas\t\n",
    "\n",
    "occupation\t -- occupation of an individuals\n",
    "\n",
    "relationship -- \t\n",
    "\n",
    "race --  Race of an Individual\n",
    "\n",
    "sex --  Gender of an Individual\n",
    "\n",
    "capitalgain --  profit received from the sale of an investment\t\n",
    "\n",
    "capitalloss\t-- A decrease in the value of a capital asset\n",
    "\n",
    "hoursperweek -- number of hours work per week\t\n",
    "\n",
    "native -- Native of an individual\n",
    "\n",
    "Salary -- salary of an individual"
   ]
  },
  {
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   "id": "4c4c8cf3",
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    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.371696Z",
     "start_time": "2022-11-10T14:58:52.873840Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') "
   ]
  },
  {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\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>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>30156</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30157</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30158</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30159</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30160</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30161 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass    education  educationno        maritalstatus  \\\n",
       "0       39          State-gov    Bachelors           13        Never-married   \n",
       "1       50   Self-emp-not-inc    Bachelors           13   Married-civ-spouse   \n",
       "2       38            Private      HS-grad            9             Divorced   \n",
       "3       53            Private         11th            7   Married-civ-spouse   \n",
       "4       28            Private    Bachelors           13   Married-civ-spouse   \n",
       "...    ...                ...          ...          ...                  ...   \n",
       "30156   27            Private   Assoc-acdm           12   Married-civ-spouse   \n",
       "30157   40            Private      HS-grad            9   Married-civ-spouse   \n",
       "30158   58            Private      HS-grad            9              Widowed   \n",
       "30159   22            Private      HS-grad            9        Never-married   \n",
       "30160   52       Self-emp-inc      HS-grad            9   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship    race      sex  capitalgain  \\\n",
       "0            Adm-clerical   Not-in-family   White     Male         2174   \n",
       "1         Exec-managerial         Husband   White     Male            0   \n",
       "2       Handlers-cleaners   Not-in-family   White     Male            0   \n",
       "3       Handlers-cleaners         Husband   Black     Male            0   \n",
       "4          Prof-specialty            Wife   Black   Female            0   \n",
       "...                   ...             ...     ...      ...          ...   \n",
       "30156        Tech-support            Wife   White   Female            0   \n",
       "30157   Machine-op-inspct         Husband   White     Male            0   \n",
       "30158        Adm-clerical       Unmarried   White   Female            0   \n",
       "30159        Adm-clerical       Own-child   White     Male            0   \n",
       "30160     Exec-managerial            Wife   White   Female        15024   \n",
       "\n",
       "       capitalloss  hoursperweek          native  Salary  \n",
       "0                0            40   United-States   <=50K  \n",
       "1                0            13   United-States   <=50K  \n",
       "2                0            40   United-States   <=50K  \n",
       "3                0            40   United-States   <=50K  \n",
       "4                0            40            Cuba   <=50K  \n",
       "...            ...           ...             ...     ...  \n",
       "30156            0            38   United-States   <=50K  \n",
       "30157            0            40   United-States    >50K  \n",
       "30158            0            40   United-States   <=50K  \n",
       "30159            0            20   United-States   <=50K  \n",
       "30160            0            40   United-States    >50K  \n",
       "\n",
       "[30161 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_train = pd.read_csv('SalaryData_Train.csv')\n",
    "salary_train "
   ]
  },
  {
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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>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
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       "      <th>hoursperweek</th>\n",
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       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
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       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "      <th>15055</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15056</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15057</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15058</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15059</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15060 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass      education  educationno        maritalstatus  \\\n",
       "0       25        Private           11th            7        Never-married   \n",
       "1       38        Private        HS-grad            9   Married-civ-spouse   \n",
       "2       28      Local-gov     Assoc-acdm           12   Married-civ-spouse   \n",
       "3       44        Private   Some-college           10   Married-civ-spouse   \n",
       "4       34        Private           10th            6        Never-married   \n",
       "...    ...            ...            ...          ...                  ...   \n",
       "15055   33        Private      Bachelors           13        Never-married   \n",
       "15056   39        Private      Bachelors           13             Divorced   \n",
       "15057   38        Private      Bachelors           13   Married-civ-spouse   \n",
       "15058   44        Private      Bachelors           13             Divorced   \n",
       "15059   35   Self-emp-inc      Bachelors           13   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship                 race      sex  \\\n",
       "0       Machine-op-inspct       Own-child                Black     Male   \n",
       "1         Farming-fishing         Husband                White     Male   \n",
       "2         Protective-serv         Husband                White     Male   \n",
       "3       Machine-op-inspct         Husband                Black     Male   \n",
       "4           Other-service   Not-in-family                White     Male   \n",
       "...                   ...             ...                  ...      ...   \n",
       "15055      Prof-specialty       Own-child                White     Male   \n",
       "15056      Prof-specialty   Not-in-family                White   Female   \n",
       "15057      Prof-specialty         Husband                White     Male   \n",
       "15058        Adm-clerical       Own-child   Asian-Pac-Islander     Male   \n",
       "15059     Exec-managerial         Husband                White     Male   \n",
       "\n",
       "       capitalgain  capitalloss  hoursperweek          native  Salary  \n",
       "0                0            0            40   United-States   <=50K  \n",
       "1                0            0            50   United-States   <=50K  \n",
       "2                0            0            40   United-States    >50K  \n",
       "3             7688            0            40   United-States    >50K  \n",
       "4                0            0            30   United-States   <=50K  \n",
       "...            ...          ...           ...             ...     ...  \n",
       "15055            0            0            40   United-States   <=50K  \n",
       "15056            0            0            36   United-States   <=50K  \n",
       "15057            0            0            50   United-States   <=50K  \n",
       "15058         5455            0            40   United-States   <=50K  \n",
       "15059            0            0            60   United-States    >50K  \n",
       "\n",
       "[15060 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_test = pd.read_csv('SalaryData_Test.csv')\n",
    "salary_test "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1be38bd",
   "metadata": {},
   "source": [
    "Conventionally, We perform EDA, visualisation and then split data into train and test.\n",
    "However, here we have train and test sets in first place only.. so we will merge them, perform EDA and visualisation.. and then again split data in order to apply ML algorithms. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73bd9315",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.740720Z",
     "start_time": "2022-11-10T14:58:54.476308Z"
    }
   },
   "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>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45216</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45217</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45218</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      workclass   education  educationno        maritalstatus  \\\n",
       "45216   33        Private   Bachelors           13        Never-married   \n",
       "45217   39        Private   Bachelors           13             Divorced   \n",
       "45218   38        Private   Bachelors           13   Married-civ-spouse   \n",
       "45219   44        Private   Bachelors           13             Divorced   \n",
       "45220   35   Self-emp-inc   Bachelors           13   Married-civ-spouse   \n",
       "\n",
       "             occupation    relationship                 race      sex  \\\n",
       "45216    Prof-specialty       Own-child                White     Male   \n",
       "45217    Prof-specialty   Not-in-family                White   Female   \n",
       "45218    Prof-specialty         Husband                White     Male   \n",
       "45219      Adm-clerical       Own-child   Asian-Pac-Islander     Male   \n",
       "45220   Exec-managerial         Husband                White     Male   \n",
       "\n",
       "       capitalgain  capitalloss  hoursperweek          native  Salary  \n",
       "45216            0            0            40   United-States   <=50K  \n",
       "45217            0            0            36   United-States   <=50K  \n",
       "45218            0            0            50   United-States   <=50K  \n",
       "45219         5455            0            40   United-States   <=50K  \n",
       "45220            0            0            60   United-States    >50K  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concatenate Train and Test datasets\n",
    "salary = salary_train.append(salary_test)\n",
    "salary.reset_index(inplace=True)\n",
    "salary = salary.drop(['index'], axis=1) \n",
    "salary.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56cd36af",
   "metadata": {},
   "source": [
    "# Descriptive statistics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "76d864c1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.796805Z",
     "start_time": "2022-11-10T14:58:54.740720Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 45221 entries, 0 to 45220\n",
      "Data columns (total 14 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   age            45221 non-null  int64 \n",
      " 1   workclass      45221 non-null  object\n",
      " 2   education      45221 non-null  object\n",
      " 3   educationno    45221 non-null  int64 \n",
      " 4   maritalstatus  45221 non-null  object\n",
      " 5   occupation     45221 non-null  object\n",
      " 6   relationship   45221 non-null  object\n",
      " 7   race           45221 non-null  object\n",
      " 8   sex            45221 non-null  object\n",
      " 9   capitalgain    45221 non-null  int64 \n",
      " 10  capitalloss    45221 non-null  int64 \n",
      " 11  hoursperweek   45221 non-null  int64 \n",
      " 12  native         45221 non-null  object\n",
      " 13  Salary         45221 non-null  object\n",
      "dtypes: int64(5), object(9)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "salary.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "832aea56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.828831Z",
     "start_time": "2022-11-10T14:58:54.796805Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>educationno</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45221.000000</td>\n",
       "      <td>45221.000000</td>\n",
       "      <td>45221.000000</td>\n",
       "      <td>45221.000000</td>\n",
       "      <td>45221.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.548086</td>\n",
       "      <td>10.118463</td>\n",
       "      <td>1101.454700</td>\n",
       "      <td>88.548617</td>\n",
       "      <td>40.938038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.217981</td>\n",
       "      <td>2.552909</td>\n",
       "      <td>7506.511295</td>\n",
       "      <td>404.838249</td>\n",
       "      <td>12.007640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>0.000000</td>\n",
       "      <td>0.000000</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>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age   educationno   capitalgain   capitalloss  hoursperweek\n",
       "count  45221.000000  45221.000000  45221.000000  45221.000000  45221.000000\n",
       "mean      38.548086     10.118463   1101.454700     88.548617     40.938038\n",
       "std       13.217981      2.552909   7506.511295    404.838249     12.007640\n",
       "min       17.000000      1.000000      0.000000      0.000000      1.000000\n",
       "25%       28.000000      9.000000      0.000000      0.000000     40.000000\n",
       "50%       37.000000     10.000000      0.000000      0.000000     40.000000\n",
       "75%       47.000000     13.000000      0.000000      0.000000     45.000000\n",
       "max       90.000000     16.000000  99999.000000   4356.000000     99.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "963cc391",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.844979Z",
     "start_time": "2022-11-10T14:58:54.829860Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45221, 14)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6db760ed",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.892873Z",
     "start_time": "2022-11-10T14:58:54.844979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Missing values\n",
    "salary.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "779a00e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.908877Z",
     "start_time": "2022-11-10T14:58:54.892873Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numeric features: ['age', 'educationno', 'capitalgain', 'capitalloss', 'hoursperweek'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Types of variables\n",
    "\n",
    "numeric_features = [feature for feature in salary.columns if salary[feature].dtypes != 'O']\n",
    "print('numeric features:', numeric_features, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e2cd9db",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.924877Z",
     "start_time": "2022-11-10T14:58:54.908877Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "discrete features: ['educationno'] \n",
      "\n",
      "continuous features: ['age', 'capitalgain', 'capitalloss', 'hoursperweek'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "#under numeric features there are 2 types of features ie. discrete and categorical\n",
    "\n",
    "discrete_features = [feature for feature in numeric_features if len(salary[feature].unique())<25]\n",
    "print('discrete features:', discrete_features, '\\n')\n",
    "\n",
    "continuous_features = [feature for feature in numeric_features if feature not in discrete_features]\n",
    "print('continuous features:', continuous_features, '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38a522be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.941479Z",
     "start_time": "2022-11-10T14:58:54.924877Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categorical_features: ['workclass', 'education', 'maritalstatus', 'occupation', 'relationship', 'race', 'sex', 'native', 'Salary']\n"
     ]
    }
   ],
   "source": [
    "#categorical_features\n",
    "\n",
    "categorical_features = [feature for feature in salary.columns if salary[feature].dtypes == 'O']\n",
    "print('categorical_features:', categorical_features) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8fd28ceb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:54.981902Z",
     "start_time": "2022-11-10T14:58:54.941479Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Private             33306\n",
      " Self-emp-not-inc     3796\n",
      " Local-gov            3100\n",
      " State-gov            1946\n",
      " Self-emp-inc         1646\n",
      " Federal-gov          1406\n",
      " Without-pay            21\n",
      "Name: workclass, dtype: int64\n",
      " HS-grad         14783\n",
      " Some-college     9898\n",
      " Bachelors        7570\n",
      " Masters          2514\n",
      " Assoc-voc        1959\n",
      " 11th             1619\n",
      " Assoc-acdm       1507\n",
      " 10th             1223\n",
      " 7th-8th           823\n",
      " Prof-school       785\n",
      " 9th               676\n",
      " 12th              577\n",
      " Doctorate         544\n",
      " 5th-6th           449\n",
      " 1st-4th           222\n",
      " Preschool          72\n",
      "Name: education, dtype: int64\n",
      " Married-civ-spouse       21055\n",
      " Never-married            14597\n",
      " Divorced                  6297\n",
      " Separated                 1411\n",
      " Widowed                   1277\n",
      " Married-spouse-absent      552\n",
      " Married-AF-spouse           32\n",
      "Name: maritalstatus, dtype: int64\n",
      " Craft-repair         6020\n",
      " Prof-specialty       6008\n",
      " Exec-managerial      5984\n",
      " Adm-clerical         5540\n",
      " Sales                5408\n",
      " Other-service        4808\n",
      " Machine-op-inspct    2969\n",
      " Transport-moving     2316\n",
      " Handlers-cleaners    2046\n",
      " Farming-fishing      1480\n",
      " Tech-support         1420\n",
      " Protective-serv       976\n",
      " Priv-house-serv       232\n",
      " Armed-Forces           14\n",
      "Name: occupation, dtype: int64\n",
      " Husband           18666\n",
      " Not-in-family     11702\n",
      " Own-child          6626\n",
      " Unmarried          4788\n",
      " Wife               2091\n",
      " Other-relative     1348\n",
      "Name: relationship, dtype: int64\n",
      " White                 38902\n",
      " Black                  4228\n",
      " Asian-Pac-Islander     1303\n",
      " Amer-Indian-Eskimo      435\n",
      " Other                   353\n",
      "Name: race, dtype: int64\n",
      " Male      30527\n",
      " Female    14694\n",
      "Name: sex, dtype: int64\n",
      " United-States                 41292\n",
      " Mexico                          903\n",
      " Philippines                     283\n",
      " Germany                         193\n",
      " Puerto-Rico                     175\n",
      " Canada                          163\n",
      " El-Salvador                     147\n",
      " India                           147\n",
      " Cuba                            133\n",
      " England                         119\n",
      " China                           113\n",
      " Jamaica                         103\n",
      " South                           101\n",
      " Italy                           100\n",
      " Dominican-Republic               97\n",
      " Japan                            89\n",
      " Guatemala                        86\n",
      " Vietnam                          83\n",
      " Columbia                         82\n",
      " Poland                           81\n",
      " Haiti                            69\n",
      " Portugal                         62\n",
      " Iran                             56\n",
      " Taiwan                           55\n",
      " Greece                           49\n",
      " Nicaragua                        48\n",
      " Peru                             45\n",
      " Ecuador                          43\n",
      " Ireland                          36\n",
      " France                           36\n",
      " Thailand                         29\n",
      " Hong                             28\n",
      " Cambodia                         26\n",
      " Trinadad&Tobago                  26\n",
      " Yugoslavia                       23\n",
      " Outlying-US(Guam-USVI-etc)       22\n",
      " Laos                             21\n",
      " Scotland                         20\n",
      " Honduras                         19\n",
      " Hungary                          18\n",
      "Name: native, dtype: int64\n",
      " <=50K    34013\n",
      " >50K     11208\n",
      "Name: Salary, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# frequency counts of values in categorical variables\n",
    "\n",
    "for feature in categorical_features:\n",
    "    print(salary[feature].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9e6697a2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:55.013932Z",
     "start_time": "2022-11-10T14:58:54.981902Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "workclass has 7 labels\n",
      "education has 16 labels\n",
      "maritalstatus has 7 labels\n",
      "occupation has 14 labels\n",
      "relationship has 6 labels\n",
      "race has 5 labels\n",
      "sex has 2 labels\n",
      "native has 40 labels\n",
      "Salary has 2 labels\n"
     ]
    }
   ],
   "source": [
    "#cardinality in categorical variables\n",
    "\n",
    "for feature in categorical_features:\n",
    "    print(feature, 'has', len(salary[feature].unique()), 'labels')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da32e3fb",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d12fb17",
   "metadata": {},
   "source": [
    "### Outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a5b2cb00",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:55.288133Z",
     "start_time": "2022-11-10T14:58:55.013932Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='hoursperweek'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for continuous features -- Boxplot\n",
    "fig, axes=plt.subplots(4,1,figsize=(14,8))\n",
    "\n",
    "sns.boxplot(salary.age,  ax=axes[0])\n",
    "sns.boxplot(salary.capitalgain,data=salary ,  ax=axes[1])\n",
    "sns.boxplot(salary.capitalloss,  ax=axes[2])\n",
    "sns.boxplot(salary.hoursperweek,  ax=axes[3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5aa9c30",
   "metadata": {},
   "source": [
    "There are lot of outliers present in the dataframe but we can't drop them because they are present in a very large quantity and can be important for model building "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "613aef15",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.322051Z",
     "start_time": "2022-11-10T14:58:55.288133Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x3600 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for categorical_features -- barplot\n",
    "fig, axes=plt.subplots(len(categorical_features),1,figsize=(14,50))\n",
    "\n",
    "for i in range (0, len(categorical_features)):\n",
    "    sns.countplot(salary[categorical_features[i]],ax=axes[i], order=salary[categorical_features[i]].value_counts().index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5abacb",
   "metadata": {},
   "source": [
    "## Univariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83620c48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.338178Z",
     "start_time": "2022-11-10T14:58:56.322051Z"
    }
   },
   "outputs": [],
   "source": [
    "salary_more_than_50=pd.DataFrame()\n",
    "salary_less_than_50=pd.DataFrame()\n",
    "\n",
    "salary_more_than_50 = salary.loc[salary['Salary']==' >50K']\n",
    "\n",
    "salary_less_than_50 = salary.loc[salary['Salary']==' <=50K']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "741667e1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.354190Z",
     "start_time": "2022-11-10T14:58:56.338178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45221, 14)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "38d0a704",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.370189Z",
     "start_time": "2022-11-10T14:58:56.354190Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(salary_less_than_50.shape[0] + salary_more_than_50.shape[0]) ==salary.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf41f116",
   "metadata": {},
   "source": [
    "### Age distribution wrt salary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c25bd1aa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.675208Z",
     "start_time": "2022-11-10T14:58:56.370189Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.figure(figsize=(15,14))\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.hist(salary_more_than_50.age, bins=[10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
    "plt.title('Age of those with salary_more_than_50k')\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.hist(salary_less_than_50.age, color = \"orange\", bins=[10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
    "plt.title('Age of those with salary_less_than_50k')\n",
    "\n",
    "\n",
    "age_describe = pd.DataFrame()\n",
    "\n",
    "age_describe['Age of those with salary_more_than_50k'] = salary_more_than_50['age'].describe()\n",
    "age_describe['Age of those with salary_less_than_50k'] = salary_less_than_50['age'].describe()\n",
    "\n",
    "age_describe  = age_describe.drop(['count', '25%', '50%', '75%'])\n",
    "plt.subplot(3,1,2)\n",
    "age_describe.plot.bar()\n",
    "\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "393c2557",
   "metadata": {},
   "source": [
    "Insight: \n",
    "\n",
    "Age of those with salary more than 50K: Most of the people are in the age group of 30 - 50 and no one is below 20yrs\n",
    "    \n",
    "Age of those with salary less than 50K: As age increases, no of people are decreasing. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965056bd",
   "metadata": {},
   "source": [
    "### Hours/week distribution wrt salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "767438de",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:56.955818Z",
     "start_time": "2022-11-10T14:58:56.675208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.figure(figsize=(15,14))\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.hist(salary_more_than_50.hoursperweek)\n",
    "plt.title('Hours/week of those with salary_more_than_50k')\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.hist(salary_less_than_50.hoursperweek, color = \"orange\")\n",
    "plt.title('Hours/week of those with salary_less_than_50k')\n",
    "\n",
    "\n",
    "Hoursweek_describe = pd.DataFrame()\n",
    "\n",
    "Hoursweek_describe['Hours/week of those with salary_more_than_50k'] = salary_more_than_50['hoursperweek'].describe()\n",
    "Hoursweek_describe['Hours/week of those with salary_less_than_50k'] = salary_less_than_50['hoursperweek'].describe()\n",
    "\n",
    "Hoursweek_describe = Hoursweek_describe.drop(['count', '25%', '50%', '75%'])\n",
    "plt.subplot(3,1,2)\n",
    "Hoursweek_describe.plot.bar()\n",
    "\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0010e0",
   "metadata": {},
   "source": [
    "Insight: \n",
    "\n",
    "For both groups, majority is working for 30 to 40 hrs per week, and people working for 80 to 90 hrs is present in both groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2bf4b15",
   "metadata": {},
   "source": [
    "## Capital Gains distribution wrt salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d917ee0c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:57.204152Z",
     "start_time": "2022-11-10T14:58:56.955818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.figure(figsize=(15,14))\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.hist(salary_more_than_50.capitalgain)\n",
    "plt.title('capitalgain of those with salary_more_than_50k')\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.hist(salary_less_than_50.capitalgain, color = \"orange\")\n",
    "plt.title('capitalgain of those with salary_less_than_50k')\n",
    "\n",
    "\n",
    "capitalgain_describe = pd.DataFrame()\n",
    "\n",
    "capitalgain_describe['capitalgain of those with salary_more_than_50k'] = salary_more_than_50['capitalgain'].describe()\n",
    "capitalgain_describe['capitalgain of those with salary_less_than_50k'] = salary_less_than_50['capitalgain'].describe()\n",
    "\n",
    "capitalgain_describe = capitalgain_describe.drop(['count', '25%', '50%', '75%'])\n",
    "plt.subplot(3,1,2)\n",
    "capitalgain_describe.plot.bar()\n",
    "\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed02761b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:45:11.079500Z",
     "start_time": "2022-11-10T14:45:11.066364Z"
    }
   },
   "source": [
    "Insight:\n",
    "\n",
    "Salary more than 50k: Capital gain for majority is between 0 to 1000\n",
    "\n",
    "Salary less than 50k: capital gain for majority is less than 500"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec4133f",
   "metadata": {},
   "source": [
    "## Capital Loss distribution wrt salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b8a3a0b1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:57.476745Z",
     "start_time": "2022-11-10T14:58:57.204152Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZ6YU9JL8cZINSb6V5CNJHp5k/ySXJbmpPe83sP1pSTYmuTHJcQPlRya5rq07K0mm0i5JkiRJms8mHfSSLAb+CFhRVYcDewCrgFOBy6tqOXB5e02SQ9v6w4CVwNlJ9mi7OwdYAyxvj5WTbZckSZIkzXdTnbq5ANg7yQLgEcDtwPHA2rZ+LXBCWz4euKiq7q2qm4GNwFFJDgYWVtWVVVXABQN1JEmSJEkTNOmgV1XfBd4J3ApsAe6pqs8BB1XVlrbNFuDAVmUxcNvALja3ssVteWS5JEmSJGkSpjJ1cz+6UbpDgMcCj0zyyrGqDCmrMcqHveeaJOuTrN+2bdtEmyxJkiRJ88JUpm4+F7i5qrZV1c+BTwC/DtzRpmPSnre27TcDSwfqL6Gb6rm5LY8s30lVnVtVK6pqxaJFi6bQdEmSJEnqr6kEvVuBo5M8ot0l81jgBuASYHXbZjVwcVu+BFiVZK8kh9DddOXqNr1ze5Kj235OHKgjSZIkSZqgBZOtWFVXJfkY8HXgPuAfgHOBRwHrkpxEFwZf0rbfkGQdcH3b/pSqur/t7mTgfGBv4NL2kCRJkiRNwqSDHkBVvQV4y4jie+lG94ZtfwZwxpDy9cDhU2mLJEmSJKkz1T+vIEmSJEmaZQx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzUwp6SR6d5GNJvp3khiTPTLJ/ksuS3NSe9xvY/rQkG5PcmOS4gfIjk1zX1p2VJFNplyRJkiTNZ1Md0fsfwGer6leApwE3AKcCl1fVcuDy9pokhwKrgMOAlcDZSfZo+zkHWAMsb4+VU2yXJEmSJM1bkw56SRYCvwF8AKCqflZVdwPHA2vbZmuBE9ry8cBFVXVvVd0MbASOSnIwsLCqrqyqAi4YqCNJkiRJmqCpjOg9AdgG/FWSf0jyl0keCRxUVVsA2vOBbfvFwG0D9Te3ssVteWT5TpKsSbI+yfpt27ZNoemSJEmS1F9TCXoLgGcA51TV04F/ok3THMWw6+5qjPKdC6vOraoVVbVi0aJFE22vJEmSJM0LUwl6m4HNVXVVe/0xuuB3R5uOSXveOrD90oH6S4DbW/mSIeWSJEmSpEmYdNCrqu8BtyV5Sis6FrgeuARY3cpWAxe35UuAVUn2SnII3U1Xrm7TO7cnObrdbfPEgTqSJEmSpAlaMMX6fwh8OMnDgO8Av0cXHtclOQm4FXgJQFVtSLKOLgzeB5xSVfe3/ZwMnA/sDVzaHpIkSZKkSZhS0Kuqa4EVQ1YdO8r2ZwBnDClfDxw+lbZIkiRJkjpT/Tt6kiRJkqRZxqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6ZspBL8keSf4hyafa6/2TXJbkpva838C2pyXZmOTGJMcNlB+Z5Lq27qwkmWq7JEmSJGm+mo4RvdcBNwy8PhW4vKqWA5e31yQ5FFgFHAasBM5Oskercw6wBljeHiunoV2SJEmSNC9NKeglWQK8APjLgeLjgbVteS1wwkD5RVV1b1XdDGwEjkpyMLCwqq6sqgIuGKgjSZIkSZqgqY7o/TnwJuAXA2UHVdUWgPZ8YCtfDNw2sN3mVra4LY8s30mSNUnWJ1m/bdu2KTZdkiRJkvpp0kEvyQuBrVV1zXirDCmrMcp3Lqw6t6pWVNWKRYsWjfNtJUmSJGl+WTCFus8CfjvJ84GHAwuTfAi4I8nBVbWlTcvc2rbfDCwdqL8EuL2VLxlSLkmSJEmahEmP6FXVaVW1pKqW0d1k5YqqeiVwCbC6bbYauLgtXwKsSrJXkkPobrpydZveuT3J0e1umycO1JEkSZIkTdBURvRGcyawLslJwK3ASwCqakOSdcD1wH3AKVV1f6tzMnA+sDdwaXtIkiRJkiZhWoJeVX0R+GJb/gFw7CjbnQGcMaR8PXD4dLRFkiRJkua76fg7epIkSZKkWcSgJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSembSQS/J0iRfSHJDkg1JXtfK909yWZKb2vN+A3VOS7IxyY1JjhsoPzLJdW3dWUkytcOSJEmSpPlrKiN69wFvqKqnAkcDpyQ5FDgVuLyqlgOXt9e0dauAw4CVwNlJ9mj7OgdYAyxvj5VTaJckSZIkzWuTDnpVtaWqvt6WtwM3AIuB44G1bbO1wAlt+Xjgoqq6t6puBjYCRyU5GFhYVVdWVQEXDNSRJEmSJE3QtFyjl2QZ8HTgKuCgqtoCXRgEDmybLQZuG6i2uZUtbssjy4e9z5ok65Os37Zt23Q0XZIkSZJ6Z8pBL8mjgI8Dr6+qH4616ZCyGqN858Kqc6tqRVWtWLRo0cQbK0mSJEnzwJSCXpI96ULeh6vqE634jjYdk/a8tZVvBpYOVF8C3N7KlwwplyRJkiRNwlTuuhngA8ANVfXugVWXAKvb8mrg4oHyVUn2SnII3U1Xrm7TO7cnObrt88SBOpIkSZKkCVowhbrPAl4FXJfk2lb2ZuBMYF2Sk4BbgZcAVNWGJOuA6+nu2HlKVd3f6p0MnA/sDVzaHpIkSZKkSZh00KuqrzD8+jqAY0epcwZwxpDy9cDhk22LJEmSJOkB03LXTUmSJEnS7GHQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8smOkGSJI0Vy079dMz3YQ5ZdOZL5jpJkjSvOGIniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8smOkG7JBkJfA/gD2Av6yqM2e4SZIkaRotO/XTM92EOWXTmS+Y6SZImsNmRdBLsgfwPuBfA5uBryW5pKqun9mWSdL84Q/hkiT1x2yZunkUsLGqvlNVPwMuAo6f4TZJkiRJ0pw0K0b0gMXAbQOvNwO/NnKjJGuANe3lj5Lc+BC0baIOAL4/042YK/KOh/Tt7JvZzf6Zveyb2cu+md2m1D8P8f+R843fndnLvpmYx4+2YrYEvQwpq50Kqs4Fzt39zZm8JOurasVMt0M7s29mN/tn9rJvZi/7Znazf2Yv+2b2sm+mz2yZurkZWDrweglw+wy1RZIkSZLmtNkS9L4GLE9ySJKHAauAS2a4TZIkSZI0J82KqZtVdV+S1wJ/S/fnFc6rqg0z3KzJmtVTS+c5+2Z2s39mL/tm9rJvZjf7Z/ayb2Yv+2aapGqnS+EkSZIkSXPYbJm6KUmSJEmaJgY9SZIkSeoZg940SrIyyY1JNiY5dabbMx8l2ZTkuiTXJlnfyvZPclmSm9rzfgPbn9b668Ykx81cy/snyXlJtib51kDZhPsiyZGtTzcmOSvJsD/HogkYpW9OT/Ld9t25NsnzB9bZNw+RJEuTfCHJDUk2JHldK/e7MwuM0T9+f2ZYkocnuTrJN1rfvLWV+92ZYWP0jd+b3a2qfEzDg+4mMv8IPAF4GPAN4NCZbtd8ewCbgANGlP0ZcGpbPhV4R1s+tPXTXsAhrf/2mOlj6MsD+A3gGcC3ptIXwNXAM+n+3ualwPNm+tjm+mOUvjkdeOOQbe2bh7ZvDgae0Zb3Af5P6wO/O7PgMUb/+P2Z+b4J8Ki2vCdwFXC0352Zf4zRN35vdvPDEb3pcxSwsaq+U1U/Ay4Cjp/hNqlzPLC2La8FThgov6iq7q2qm4GNdP2oaVBVXwbuHFE8ob5IcjCwsKqurO5f+AsG6miSRumb0dg3D6Gq2lJVX2/L24EbgMX43ZkVxuif0dg/D5Hq/Ki93LM9Cr87M26MvhmNfTNNDHrTZzFw28DrzYz9j792jwI+l+SaJGta2UFVtQW6/6SBA1u5ffbQm2hfLG7LI8u1e7w2yTfb1M4d05vsmxmSZBnwdLrffvvdmWVG9A/4/ZlxSfZIci2wFbisqvzuzBKj9A34vdmtDHrTZ9gcYf92xUPvWVX1DOB5wClJfmOMbe2z2WO0vrCPHjrnAE8EjgC2AO9q5fbNDEjyKODjwOur6odjbTqkzP7ZzYb0j9+fWaCq7q+qI4AldCNAh4+xuX3zEBqlb/ze7GYGvemzGVg68HoJcPsMtWXeqqrb2/NW4K/ppmLe0Yb7ac9b2+b22UNvon2xuS2PLNc0q6o72n/EvwDezwPTmO2bh1iSPelCxIer6hOt2O/OLDGsf/z+zC5VdTfwRWAlfndmlcG+8Xuz+xn0ps/XgOVJDknyMGAVcMkMt2leSfLIJPvsWAZ+C/gWXT+sbputBi5uy5cAq5LsleQQYDndRb7afSbUF22azfYkR7c7a504UEfTaMcPQs2/ofvugH3zkGqf5QeAG6rq3QOr/O7MAqP1j9+fmZdkUZJHt+W9gecC38bvzowbrW/83ux+C2a6AX1RVfcleS3wt3R34DyvqjbMcLPmm4OAv2532l0AXFhVn03yNWBdkpOAW4GXAFTVhiTrgOuB+4BTqur+mWl6/yT5CHAMcECSzcBbgDOZeF+cDJwP7E13h61LH8LD6KVR+uaYJEfQTYPZBPwB2Dcz4FnAq4Dr2vUsAG/G785sMVr/vMzvz4w7GFibZA+6gYx1VfWpJFfid2emjdY3H/R7s3ulu2mNJEmSJKkvnLopSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SXqIJHl2khunUP/8JP+tLR+TZPP0tW7ykhyU5MtJtid51zjrbEry3N3dtqlK8uYkfznG+t9N8pWHoB2V5Em7+31m+j1HaccXk7x6ptsxXnOtvZL6y6AnSQ+Rqvq7qnrKjtdzJeyMwxrg+8DCqnrDyJWDAXWuqaq3VdWrAZIsa+FnwUy3q6+SnJ7kQ7OgHZuS/CTJj9rjcyPWvzzJLUn+KcnfJNl/ptoqSaMx6EmSpurxwPVVVTPdED2YoXRKXlRVj2qP39pRmOQw4H8CrwIOAn4MnD1DbZSkURn0JGmIJEuTfCLJtiQ/SPLeVv7EJFe0su8n+XCSRw/U25TktCTXJ7kryV8leXhb98vplkk+CDwO+GQbMXhTK/9fSb6X5J42HfKwcbb3qW3K2N1JNiT57YF1z2/t2Z7ku0ne2MoPSPKpVufOJH+XZOj/C0l+PcnXWru+luTXW/n5wGrgTe04njui3hrgFQPrPzmw+ogk32z7/OiOz6nV+/0kG1u7Lkny2FaeJO9JsrXV+2aSw9u6vZK8M8mtSe5I8hdJ9h7leG5JcmRbfmUbqTu0vX51kr9py4MjTF9uz3e3Y3nmwP7e2fr75iTPG6Of/rT1wfYkNyY5tpUfleTK1hdbkrw3ycNG2ccLkvxDkh8muS3J6QPrdow6npTkVuCKJJ9O8ocj9vHNJCeM1s4h7znqZzvWeTTa8Y7yHiuBNwMvbZ/vNwZWPz7J37f9fC7JAQP1Rv3OpBtNfl/7DLYnuSrJE8d73KN4BfDJqvpyVf0I+E/Av02yz5BjOrh91m+c4ntK0oQZ9CRphCR7AJ8CbgGWAYuBi3asBt4OPBZ4KrAUOH3ELl4BHAc8EXgy8B9HvkdVvQq4lQdGDf6srboUWA4cCHwd+PA42rsn8Engc63eHwIfTrJjmugHgD+oqn2Aw4ErWvkbgM3AIrqRiTcDO43KpZuW9mngLOAxwLuBTyd5TFX9bmvjn7Xj+PyI4zx3xPoXDaz+d8BK4BDgV4Hfbe/3HLrP+N8BB9P1w47P/7eA36D7XB8NvBT4QVv3jlZ+BPAkun77z6N8bF8CjmnLvwF8B/jNgddfGlLnN9rzo9uxXNle/xpwI3AA8GfAB5JkZOXWH68F/mXri+OATW31/cAft308EzgW+PejtP2fgBPpjv8FwMlDQttv0p2fxwFrgVcOtONpdJ/NZ0bZ/zBjfbZDz6NdHO9OquqzwNuAj7bP92kDq18O/B7d+f0wYDA47eo78zLgrcB+wEbgjHEe84fT/aLnc+0z2+Ew4JchtKr+EfgZ3efzS0mW0Z1H762qd47zPSVp2hj0JGlnR9EFuT+pqn+qqp9W1VcAqmpjVV1WVfdW1Ta60PObI+q/t6puq6o76X6ofNl437iqzquq7VV1L12AfFqSfXdR7WjgUcCZVfWzqrqCLqjueN+fA4cmWVhVd1XV1wfKDwYeX1U/b9cQDpt++QLgpqr6YFXdV1UfAb4NvGjIthNxVlXd3j6nT9KFCOiC8nlV9fX2OZwGPLP94PxzYB/gV4BU1Q1VtaUFq98H/riq7qyq7XShYdUo7/0lHui3Z9MFyx2vf5PhQW80t1TV+6vqfrpQdTBd4BnpfmAvur7Ys6o2tZBAVV1TVV9tn+8muqmBI88r2rZfrKrrquoXVfVN4CNDtj29nbs/AS4GlidZ3ta9ii5M/Ww8BzeOz3a082jU452Ev6qq/9OOZx0PnCvj+c58oqqurqr76ELgEezaK+h+yfN44AvA3+aBkftHAfeM2P4euvNyh0OBLwJvab/skKSHnEFPkna2lO6H9/tGrkhyYJKL2nS0HwIfohuFGXTbwPItdKFxl5LskeTMJP/Y9r2prRq5/5EeC9xWVb8Y8b6L2/LvAM8HbknypYEph/+dboTjc0m+k+TUMfZ/y4iywf1P1vcGln9M9wP0Tu/Xpsf9AFjcQux7gfcBdyQ5N8lCutGkRwDXtCmEdwOfbeXDfAl4dpJ/DuwBfBR4VguT+wLXTuY4qurHbfFRIzeqqo3A6+nCyNZ2Hu2YkvrkNv3xe63v38Yo/Z7k15J8oY023QO8Zsi2vzwHWwBaB7yyTal8GfDBCRzfrj7boefRWMc7CUPPlXF+Z0Y7z0ZVVX9fVT+pqh9X1duBu+l+IQDwI2DhiCoLge0Dr18BfBf42K7eS5J2F4OeJO3sNuBxGX4ji7fTTW/81apaSDclbuQ0vaUDy48Dbh/lfUaOnr0cOB54Ll3YWNbKd5oGOMLtwNI8+Pq6x9H9oElVfa2qjqeb2vY3dD/000ZB3lBVT6AbnfsPo1xDdTvdyMagX+5/HCZ6k5YHvV+SR9JNGd1xPGdV1ZF0U+ieDPwJ3V0/fwIcVlWPbo99q2roD/UthPwY+CPgy22U6nt0dxD9yojQPNnjGPa+F1bVv2rHV3RTIgHOoRslXd7Oqzczer9fCFwCLK2qfYG/GLLtyLaupQsfxwI/Hph2Oh5jfrZjnUdjHO9oJvoZT/Y7M1E1sM8NwC+nciZ5At3I5f8Z2P50us/twjYVXJIecgY9SdrZ1cAW4Mwkj0zy8CTPauv2ofuN/t1JFtOFjJFOSbKkXdv2ZrrRomHuAJ4w8Hof4F660atH0I3qjMdVdNdtvSnJnkmOofuB+6IkD0vyiiT7VtXPgR/STakjyQuTPKlNzdtRfv+Q/X8GeHK6W8ovSPJSuqlpnxpn+0Ye565cCPxekiOS7EX3OVxVVZuS/Ms2orVnO+afAve3YPZ+4D1JDmzHtzjJcWO8z5foriHbMU3ziyNej7QN+MUEj+WXkjwlyXPaMf2ULjzt+Lz3oeuDHyX5FeDkMXa1D3BnVf00yVF0YWdMLdj9AngXExvNY1ef7Wjn0S6OdzR3AMsyyk2Bhpjsd2ZUSR6X5Fntu/PwJH9CN0L4922TDwMvSvd3MR8J/Be66aGDI3o/B14CPBL44ASOR5Kmjf/wSNII7VqrF9HddOJWuhtNvLStfivwDLprcj4NfGLILi6kuzHKd9pjtL8h93bgP7bpcG8ELqCbsvhd4Hrgq+Ns78+A3waeRzeKcDZwYlV9u23yKmBTm9r2Gh64Mcdy4PN0wfVK4Oyq+uKQ/f8AeCHdTTd+ALwJeGFVfX887aO7Gcyh7Tj/ZhzHczndnQw/The4n8gD14MtpAsdd9F9Vj8Adtzo4k/pphB+tR3r54GnMLov0QWFL4/yemS7fkx3zeXft2M5elfHMsJewJl0ffQ9uhHWN7d1b6QLbNvb8Y32ywHobtLyX5Jsp7shyrpxvv8FwL+gm248UWN9tqOdR2Md72j+V3v+QZKvj7llZ1LfmV3Yh26E9a6235XA89r3gKraQPc9+jCwtW2/041z2vfy39Id93mGPUkPtQy/7l6SNBlJNgGvrhF3n5RmWpITgTVtKqUkqef87ZIkST2X5BF0o07eAVKS5gmDniRJPdaupdtGd/3bhQPlz073h8l3euzm9lw6yvvualrndLZhRo5dkh5KTt2UJEmSpJ5xRE+SJEmSembY34iaEw444IBatmzZTDdDkiRJkmbENddc8/2qWjRs3ZwNesuWLWP9+vUz3QxJkiRJmhFJbhltnVM3JUmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnFsx0A3rnwsx0C+aWl9dMt0CSJEnqHUf0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ7ZZdBL8vAkVyf5RpINSd7ayvdPclmSm9rzfgN1TkuyMcmNSY4bKD8yyXVt3VlJ0sr3SvLRVn5VkmW74VglSZIkaV4Yz4jevcBzquppwBHAyiRHA6cCl1fVcuDy9pokhwKrgMOAlcDZSfZo+zoHWAMsb4+Vrfwk4K6qehLwHuAdUz80SZIkSZqfdhn0qvOj9nLP9ijgeGBtK18LnNCWjwcuqqp7q+pmYCNwVJKDgYVVdWVVFXDBiDo79vUx4Ngdo32SJEmSpIkZ1zV6SfZIci2wFbisqq4CDqqqLQDt+cC2+WLgtoHqm1vZ4rY8svxBdarqPuAe4DFD2rEmyfok67dt2zauA5QkSZKk+WZcQa+q7q+qI4AldKNzh4+x+bCRuBqjfKw6I9txblWtqKoVixYt2kWrJUmSJGl+mtBdN6vqbuCLdNfW3dGmY9Ket7bNNgNLB6otAW5v5UuGlD+oTpIFwL7AnRNpmyRJkiSpM567bi5K8ui2vDfwXODbwCXA6rbZauDitnwJsKrdSfMQupuuXN2md25PcnS7/u7EEXV27OvFwBXtOj5JkiRJ0gQtGMc2BwNr250z/xmwrqo+leRKYF2Sk4BbgZcAVNWGJOuA64H7gFOq6v62r5OB84G9gUvbA+ADwAeTbKQbyVs1HQcnSZIkSfNR5urA2YoVK2r9+vUz3YydXejNQifk5XPz/JMkSZJmWpJrqmrFsHUTukZPkiRJkjT7GfQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST2zy6CXZGmSLyS5IcmGJK9r5acn+W6Sa9vj+QN1TkuyMcmNSY4bKD8yyXVt3VlJ0sr3SvLRVn5VkmW74VglSZIkaV4Yz4jefcAbquqpwNHAKUkObeveU1VHtMdnANq6VcBhwErg7CR7tO3PAdYAy9tjZSs/Cbirqp4EvAd4x9QPTZIkSZLmp10GvaraUlVfb8vbgRuAxWNUOR64qKruraqbgY3AUUkOBhZW1ZVVVcAFwAkDdda25Y8Bx+4Y7ZMkSZIkTcyErtFrUyqfDlzVil6b5JtJzkuyXytbDNw2UG1zK1vclkeWP6hOVd0H3AM8Zsj7r0myPsn6bdu2TaTpkiRJkjRvjDvoJXkU8HHg9VX1Q7ppmE8EjgC2AO/asemQ6jVG+Vh1HlxQdW5VraiqFYsWLRpv0yVJkiRpXhlX0EuyJ13I+3BVfQKgqu6oqvur6hfA+4Gj2uabgaUD1ZcAt7fyJUPKH1QnyQJgX+DOyRyQJEmSJM1347nrZoAPADdU1bsHyg8e2OzfAN9qy5cAq9qdNA+hu+nK1VW1Bdie5Oi2zxOBiwfqrG7LLwauaNfxSZIkSZImaME4tnkW8CrguiTXtrI3Ay9LcgTdFMtNwB8AVNWGJOuA6+nu2HlKVd3f6p0MnA/sDVzaHtAFyQ8m2Ug3krdqKgclSZIkSfPZLoNeVX2F4dfQfWaMOmcAZwwpXw8cPqT8p8BLdtUWSZIkSdKuTeium5IkSZKk2c+gJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqmV0GvSRLk3whyQ1JNiR5XSvfP8llSW5qz/sN1DktycYkNyY5bqD8yCTXtXVnJUkr3yvJR1v5VUmW7YZjlSRJkqR5YTwjevcBb6iqpwJHA6ckORQ4Fbi8qpYDl7fXtHWrgMOAlcDZSfZo+zoHWAMsb4+Vrfwk4K6qehLwHuAd03BskiRJkjQv7TLoVdWWqvp6W94O3AAsBo4H1rbN1gIntOXjgYuq6t6quhnYCByV5GBgYVVdWVUFXDCizo59fQw4dsdonyRJkiRpYiZ0jV6bUvl04CrgoKraAl0YBA5smy0GbhuotrmVLW7LI8sfVKeq7gPuAR4zkbZJkiRJkjrjDnpJHgV8HHh9Vf1wrE2HlNUY5WPVGdmGNUnWJ1m/bdu2XTVZkiRJkualcQW9JHvShbwPV9UnWvEdbTom7XlrK98MLB2ovgS4vZUvGVL+oDpJFgD7AneObEdVnVtVK6pqxaJFi8bTdEmSJEmad8Zz180AHwBuqKp3D6y6BFjdllcDFw+Ur2p30jyE7qYrV7fpnduTHN32eeKIOjv29WLginYdnyRJkiRpghaMY5tnAa8CrktybSt7M3AmsC7JScCtwEsAqmpDknXA9XR37Dylqu5v9U4Gzgf2Bi5tD+iC5AeTbKQbyVs1tcOSJEmSpPlrl0Gvqr7C8GvoAI4dpc4ZwBlDytcDhw8p/yktKEqSJEmSpmZCd92UJEmSJM1+Bj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZwx6kiRJktQzBj1JkiRJ6hmDniRJkiT1jEFPkiRJknrGoCdJkiRJPWPQkyRJkqSeMehJkiRJUs8Y9CRJkiSpZ3YZ9JKcl2Rrkm8NlJ2e5LtJrm2P5w+sOy3JxiQ3JjluoPzIJNe1dWclSSvfK8lHW/lVSZZN8zFKkiRJ0rwynhG984GVQ8rfU1VHtMdnAJIcCqwCDmt1zk6yR9v+HGANsLw9duzzJOCuqnoS8B7gHZM8FkmSJEkS4wh6VfVl4M5x7u944KKqureqbgY2AkclORhYWFVXVlUBFwAnDNRZ25Y/Bhy7Y7RPkiRJkjRxU7lG77VJvtmmdu7XyhYDtw1ss7mVLW7LI8sfVKeq7gPuAR4z7A2TrEmyPsn6bdu2TaHpkiRJktRfkw165wBPBI4AtgDvauXDRuJqjPKx6uxcWHVuVa2oqhWLFi2aUIMlSZIkab6YVNCrqjuq6v6q+gXwfuCotmozsHRg0yXA7a18yZDyB9VJsgDYl/FPFZUkSZIkjTCpoNeuudvh3wA77sh5CbCq3UnzELqbrlxdVVuA7UmObtffnQhcPFBndVt+MXBFu45PkiRJkjQJC3a1QZKPAMcAByTZDLwFOCbJEXRTLDcBfwBQVRuSrAOuB+4DTqmq+9uuTqa7g+fewKXtAfAB4INJNtKN5K2ahuOSJEmSpHkrc3XwbMWKFbV+/fqZbsbOLvSGoRPy8rl5/kmSJEkzLck1VbVi2Lqp3HVTkiRJkjQLGfQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST2zy6CX5LwkW5N8a6Bs/ySXJbmpPe83sO60JBuT3JjkuIHyI5Nc19adlSStfK8kH23lVyVZNs3HKEmSJEnzynhG9M4HVo4oOxW4vKqWA5e31yQ5FFgFHNbqnJ1kj1bnHGANsLw9duzzJOCuqnoS8B7gHZM9GEmSJEnSOIJeVX0ZuHNE8fHA2ra8FjhhoPyiqrq3qm4GNgJHJTkYWFhVV1ZVAReMqLNjXx8Djt0x2idJkiRJmrjJXqN3UFVtAWjPB7byxcBtA9ttbmWL2/LI8gfVqar7gHuAxwx70yRrkqxPsn7btm2TbLokSZIk9dt034xl2EhcjVE+Vp2dC6vOraoVVbVi0aJFk2yiJEmSJPXbZIPeHW06Ju15ayvfDCwd2G4JcHsrXzKk/EF1kiwA9mXnqaKSJEmSpHGabNC7BFjdllcDFw+Ur2p30jyE7qYrV7fpnduTHN2uvztxRJ0d+3oxcEW7jk+SJEmSNAkLdrVBko8AxwAHJNkMvAU4E1iX5CTgVuAlAFW1Ick64HrgPuCUqrq/7epkujt47g1c2h4AHwA+mGQj3Ujeqmk5MkmSJEmap3YZ9KrqZaOsOnaU7c8AzhhSvh44fEj5T2lBUZIkSZI0ddN9MxZJkiRJ0gwz6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DMGPUmSJEnqGYOeJEmSJPWMQU+SJEmSemZKQS/JpiTXJbk2yfpWtn+Sy5Lc1J73G9j+tCQbk9yY5LiB8iPbfjYmOStJptIuSZIkSZrPpmNE7/+pqiOqakV7fSpweVUtBy5vr0lyKLAKOAxYCZydZI9W5xxgDbC8PVZOQ7skSZIkaV7aHVM3jwfWtuW1wAkD5RdV1b1VdTOwETgqycHAwqq6sqoKuGCgjiRJkiRpgqYa9Ar4XJJrkqxpZQdV1RaA9nxgK18M3DZQd3MrW9yWR5bvJMmaJOuTrN+2bdsUmy5JkiRJ/bRgivWfVVW3JzkQuCzJt8fYdth1dzVG+c6FVecC5wKsWLFi6DaSJEmSNN9NaUSvqm5vz1uBvwaOAu5o0zFpz1vb5puBpQPVlwC3t/IlQ8olSZIkSZMw6aCX5JFJ9tmxDPwW8C3gEmB122w1cHFbvgRYlWSvJIfQ3XTl6ja9c3uSo9vdNk8cqCNJkiRJmqCpTN08CPjr9pcQFgAXVtVnk3wNWJfkJOBW4CUAVbUhyTrgeuA+4JSqur/t62TgfGBv4NL2kCRJkiRNwqSDXlV9B3jakPIfAMeOUucM4Iwh5euBwyfbFkmSJEnSA3bHn1eQJEmSJM0gg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcMepIkSZLUMwY9SZIkSeoZg54kSZIk9YxBT5IkSZJ6xqAnSZIkST1j0JMkSZKknjHoSZIkSVLPGPQkSZIkqWcWzHQDJGm3uTAz3YK55eU10y2QJEnTZNaM6CVZmeTGJBuTnDrT7ZEkSZKkuWpWjOgl2QN4H/Cvgc3A15JcUlXXz2zLJGkecQR04hwFlSTNUrMi6AFHARur6jsASS4CjgcMepKk2ctwPDEGY0l6yMyWoLcYuG3g9Wbg10ZulGQNsKa9/FGSGx+Ctk3UAcD3Z7oRc8Yr/CFpFvHc1VzkeTuX+G/+IM9dzVWeu7PL40dbMVuC3rB/+Xf6tV9VnQucu/ubM3lJ1lfVipluhzRRnruaizxvNVd57mqu8tydO2bLzVg2A0sHXi8Bbp+htkiSJEnSnDZbgt7XgOVJDknyMGAVcMkMt0mSJEmS5qRZMXWzqu5L8lrgb4E9gPOqasMMN2uyZvXUUmkMnruaizxvNVd57mqu8tydI1LlHbAkSZIkqU9my9RNSZIkSdI0MehJkiRJUs8Y9KZRkpVJbkyyMcmpM90ezW9JzkuyNcm3Bsr2T3JZkpva834D605r5+6NSY4bKD8yyXVt3VlJ/ENY2m2SLE3yhSQ3JNmQ5HWt3HNXs1qShye5Osk32rn71lbuuas5IckeSf4hyafaa8/dOc6gN02S7AG8D3gecCjwsiSHzmyrNM+dD6wcUXYqcHlVLQcub69p5+oq4LBW5+x2TgOcA6wBlrfHyH1K0+k+4A1V9VTgaOCUdn567mq2uxd4TlU9DTgCWJnkaDx3NXe8Drhh4LXn7hxn0Js+RwEbq+o7VfUz4CLg+Bluk+axqvoycOeI4uOBtW15LXDCQPlFVXVvVd0MbASOSnIwsLCqrqzuzk0XDNSRpl1Vbamqr7fl7XQ/dCzGc1ezXHV+1F7u2R6F567mgCRLgBcAfzlQ7Lk7xxn0ps9i4LaB15tbmTSbHFRVW6D7gRo4sJWPdv4ubssjy6XdLsky4OnAVXjuag5oU9+uBbYCl1WV567mij8H3gT8YqDMc3eOM+hNn2FzkP3bFZorRjt/Pa81I5I8Cvg48Pqq+uFYmw4p89zVjKiq+6vqCGAJ3QjH4WNs7rmrWSHJC4GtVXXNeKsMKfPcnYUMetNnM7B04PUS4PYZaos0mjva1Ara89ZWPtr5u7ktjyyXdpske9KFvA9X1Sdaseeu5oyquhv4It31SZ67mu2eBfx2kk10lx49J8mH8Nyd8wx60+drwPIkhyR5GN1FqpfMcJukkS4BVrfl1cDFA+WrkuyV5BC6C6ivblM1tic5ut0568SBOtK0a+fZB4AbqurdA6s8dzWrJVmU5NFteW/gucC38dzVLFdVp1XVkqpaRvfz6xVV9Uo8d+e8BTPdgL6oqvuSvBb4W2AP4Lyq2jDDzdI8luQjwDHAAUk2A28BzgTWJTkJuBV4CUBVbUiyDrie7q6Hp1TV/W1XJ9PdwXNv4NL2kHaXZwGvAq5r1zoBvBnPXc1+BwNr290H/xmwrqo+leRKPHc1N/nv7hyX7qY4kiRJkqS+cOqmJEmSJPWMQU+SJEmSesagJ0mSJEk9Y9CTJEmSpJ4x6EmSJElSzxj0JEmSJKlnDHqSJEmS1DP/FwoIE7xTlMcrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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.figure(figsize=(15,14))\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.hist(salary_more_than_50.capitalloss)\n",
    "plt.title('capitalloss of those with salary_more_than_50k')\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.hist(salary_less_than_50.capitalloss, color = \"orange\")\n",
    "plt.title('capitalloss of those with salary_less_than_50k')\n",
    "\n",
    "\n",
    "capitalloss_describe = pd.DataFrame()\n",
    "\n",
    "capitalloss_describe['capitalloss of those with salary_more_than_50k'] = salary_more_than_50['capitalloss'].describe()\n",
    "capitalloss_describe['capitalloss of those with salary_less_than_50k'] = salary_less_than_50['capitalloss'].describe()\n",
    "\n",
    "capitalloss_describe = capitalloss_describe.drop(['count', '25%', '50%', '75%'])\n",
    "plt.subplot(3,1,2)\n",
    "capitalloss_describe.plot.bar()\n",
    "\n",
    "plt.show() \n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fbedf6d",
   "metadata": {},
   "source": [
    "## Categorical variables wrt salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "31110b9f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.681617Z",
     "start_time": "2022-11-10T14:58:57.476745Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4y0yEIuK7EfF0RNzT0vaWiJgcETPK729u2XdCRDwUEQ9GxL4t7aMj4u5y35kREWX76hFxWdl+e0QMWc7nKEmSJEkL6UqP0IXAfou0HQ/clJnDgJvKbSJiK2A8sHV5zNkR0a885hxgIjCs/Op4zKOAv2Tm24H/BE7v6clIkiRJUlcsMxHKzF8Bzy/SfCBwUXn7IuCglvZLM/P1zHwEeAjYISIGAQMy87bMTOB7ixzT8VhXAHt19BZJkiRJ0orQ0zlCG2XmUwDl9w3L9k2Ax1vuN7Ns26S8vWj7Qsdk5hzgBWD9HsYlSZIkScu0vIsldNaTk0tpX9oxiz94xMSImBIRU5555pkehihJkiSp6XqaCM0qh7tRfn+6bJ8JbNpyv8HAk2X74E7aFzomIvoD67H4UDwAMvO8zByTmWMGDhzYw9AlSZIkNV1PE6FrgAnl7QnA1S3t48tKcEMpiiLcUQ6fmx0RO5Xzfw5f5JiOx/og8ItyHpEkSZIkrRD9l3WHiLgE2B3YICJmAicBk4DLI+Io4DFgHEBm3hsRlwP3AXOAYzJzbvlQR1NUoFsTuL78Ajgf+N+IeIiiJ2j8cjkzSZIkSVqCZSZCmXnoEnbttYT7nwqc2kn7FGB4J+2vUSZSkiRJkrQyLO9iCZIkSZLU9kyEJEmSJDWOiZAkSZKkxjERkiRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxepUIRcSnI+LeiLgnIi6JiDUi4i0RMTkiZpTf39xy/xMi4qGIeDAi9m1pHx0Rd5f7zoyI6E1ckiRJkrQ0PU6EImIT4FhgTGYOB/oB44HjgZsycxhwU7lNRGxV7t8a2A84OyL6lQ93DjARGFZ+7dfTuCRJkiRpWXo7NK4/sGZE9AfWAp4EDgQuKvdfBBxU3j4QuDQzX8/MR4CHgB0iYhAwIDNvy8wEvtdyjCRJkiQtdz1OhDLzCeAM4DHgKeCFzLwR2Cgznyrv8xSwYXnIJsDjLQ8xs2zbpLy9aLskSZIkrRC9GRr3ZopenqHAxsDaEfHRpR3SSVsupb2z55wYEVMiYsozzzzT3ZAlSZIkCejd0Li/Bx7JzGcy8w3gR8C7gVnlcDfK70+X958JbNpy/GCKoXQzy9uLti8mM8/LzDGZOWbgwIG9CF2SJElSk/UmEXoM2Cki1iqrvO0F3A9cA0wo7zMBuLq8fQ0wPiJWj4ihFEUR7iiHz82OiJ3Kxzm85RhJkiRJWu769/TAzLw9Iq4AfgfMAX4PnAesA1weEUdRJEvjyvvfGxGXA/eV9z8mM+eWD3c0cCGwJnB9+SXByet18/4vrJg4JEmS1Kf0OBECyMyTgJMWaX6doneos/ufCpzaSfsUYHhvYpEkSZKkrupt+WxJkiRJqh0TIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY3Tq0QoIt4UEVdExAMRcX9E7BwRb4mIyRExo/z+5pb7nxARD0XEgxGxb0v76Ii4u9x3ZkREb+KSJEmSpKXpbY/QfwM/y8x3AiOB+4HjgZsycxhwU7lNRGwFjAe2BvYDzo6IfuXjnANMBIaVX/v1Mi5JkiRJWqIeJ0IRMQDYDTgfIDP/lpl/BQ4ELirvdhFwUHn7QODSzHw9Mx8BHgJ2iIhBwIDMvC0zE/heyzGSJEmStNz1pkdoC+AZ4IKI+H1EfCci1gY2ysynAMrvG5b33wR4vOX4mWXbJuXtRdsXExETI2JKREx55plnehG6JEmSpCbrTSLUH9gOOCcztwVephwGtwSdzfvJpbQv3ph5XmaOycwxAwcO7G68kiRJkgT0LhGaCczMzNvL7SsoEqNZ5XA3yu9Pt9x/05bjBwNPlu2DO2mXJEmSpBWix4lQZv4ZeDwitiyb9gLuA64BJpRtE4Cry9vXAOMjYvWIGEpRFOGOcvjc7IjYqawWd3jLMZIkSZK03PXv5fH/AlwcEasBDwNHUiRXl0fEUcBjwDiAzLw3Ii6nSJbmAMdk5tzycY4GLgTWBK4vvyRJkiRphehVIpSZ04Axnezaawn3PxU4tZP2KcDw3sQiSZIkSV3V23WEJEmSJKl2TIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxulfdQBt6+T1enDMC8s/DkmSJEnLnT1CkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxep0IRUS/iPh9RPy03H5LREyOiBnl9ze33PeEiHgoIh6MiH1b2kdHxN3lvjMjInoblyRJkiQtyfLoEToOuL9l+3jgpswcBtxUbhMRWwHjga2B/YCzI6Jfecw5wERgWPm133KIS5IkSZI61atEKCIGAwcA32lpPhC4qLx9EXBQS/ulmfl6Zj4CPATsEBGDgAGZeVtmJvC9lmMkSZIkabnrbY/QfwH/AcxradsoM58CKL9vWLZvAjzecr+ZZdsm5e1F2yVJkiRphehxIhQR7wOezsypXT2kk7ZcSntnzzkxIqZExJRnnnmmi08rSZIkSQvrTY/QLsDYiHgUuBTYMyK+D8wqh7tRfn+6vP9MYNOW4wcDT5btgztpX0xmnpeZYzJzzMCBA3sRuiRJkqQm63EilJknZObgzBxCUQThF5n5UeAaYEJ5twnA1eXta4DxEbF6RAylKIpwRzl8bnZE7FRWizu85RhJkiRJWu76r4DHnARcHhFHAY8B4wAy896IuBy4D5gDHJOZc8tjjgYuBNYEri+/JEmSJGmFWC6JUGbeAtxS3n4O2GsJ9zsVOLWT9inA8OURiyRJkiQty/JYR0iSJEmSasVESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlxTIQkSZIkNY6JkCRJkqTG6V91ACvLkOOv7db9H11jBQUiSZIkqXL2CEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjdO/6gDUdUOOv7Zb9390jRUUiCRJklRz9ghJkiRJahwTIUmSJEmNYyIkSZIkqXFMhCRJkiQ1jomQJEmSpMYxEZIkSZLUOD1OhCJi04i4OSLuj4h7I+K4sv0tETE5ImaU39/ccswJEfFQRDwYEfu2tI+OiLvLfWdGRPTutCRJkiRpyXrTIzQH+LfMfBewE3BMRGwFHA/clJnDgJvKbcp944Gtgf2AsyOiX/lY5wATgWHl1369iEuSJEmSlqrHiVBmPpWZvytvzwbuBzYBDgQuKu92EXBQeftA4NLMfD0zHwEeAnaIiEHAgMy8LTMT+F7LMZIkSZK03C2XOUIRMQTYFrgd2Cgzn4IiWQI2LO+2CfB4y2Ezy7ZNytuLtkuSJEnSCtG/tw8QEesAVwKfyswXlzK9p7MduZT2zp5rIsUQOjbbbLPuB6vKDTn+2m7d/9E1VlAgkiRJarRe9QhFxKoUSdDFmfmjsnlWOdyN8vvTZftMYNOWwwcDT5btgztpX0xmnpeZYzJzzMCBA3sTuiRJkqQG603VuADOB+7PzG+07LoGmFDengBc3dI+PiJWj4ihFEUR7iiHz82OiJ3Kxzy85RhJkiRJWu56MzRuF+Aw4O6ImFa2fQ6YBFweEUcBjwHjADLz3oi4HLiPouLcMZk5tzzuaOBCYE3g+vJLkiRJklaIHidCmflrOp/fA7DXEo45FTi1k/YpwPCexiJJkiRJ3bFcqsZJkiRJUp2YCEmSJElqHBMhSZIkSY1jIiRJkiSpcUyEJEmSJDWOiZAkSZKkxjERkiRJktQ4vVlQVZKkZjl5vW7e/4UVE4ckqdfsEZIkSZLUOPYISWoGr+RLkqQWJkJSNw05/tpu3f/RSQesoEgkSZLUUw6NkyRJktQ4JkKSJEmSGsdESJIkSVLjmAhJkiRJahwTIUmSJEmNYyIkSZIkqXEsny2taK5fIxX8XWgPvg6SBNgjJEmSJKmBTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgmQpIkSZIax0RIkiRJUuOYCEmSJElqHBMhSZIkSY3jgqqSJKleXBRW0nJgj5AkSZKkxjERkiRJktQ4Do2TJElS93R3eCI4RFFtx0RIaqAhx1/brfs/OumAFRSJJElSNRwaJ0mSJKlx7BGSJEla2ax8J1XOREhS7XR3aB/Ao2usgEAkSVJtmQhJkiSpeeyVazwTIUmqQF8oWNHtc7BXTpLURiyWIEmSJKlxTIQkSZIkNY6JkCRJkqTGMRGSJEmS1DgWS5C0bFbWkSSp/fj/uVfapkcoIvaLiAcj4qGIOL7qeCRJkiT1XW3RIxQR/YD/AfYGZgJ3RsQ1mXlftZFJUpvo7lU/8MqfJElL0RaJELAD8FBmPgwQEZcCBwImQpKkFaYvrIXUF85BUoNVOLyvXRKhTYDHW7ZnAjtWFIskSVqJ6p7MdTd+qP85tFv8Uk9EZlYdAxExDtg3Mz9ebh8G7JCZ/7LI/SYCE8vNLYEHV2BYGwDPrsDHXxk8h/bgObSHup9D3eMHz6FdeA7toe7nUPf4wXNoFyv6HDbPzIGd7WiXHqGZwKYt24OBJxe9U2aeB5y3MgKKiCmZOWZlPNeK4jm0B8+hPdT9HOoeP3gO7cJzaA91P4e6xw+eQ7uo8hzapWrcncCwiBgaEasB44FrKo5JkiRJUh/VFj1CmTknIv4ZuAHoB3w3M++tOCxJkiRJfVRbJEIAmXkdcF3VcbRYKUPwVjDPoT14Du2h7udQ9/jBc2gXnkN7qPs51D1+8BzaRWXn0BbFEiRJkiRpZWqXOUKSJEmStNKYCEmSJElqnLaZIyS1ioi1M/PlquNQfUXE2sCrmTmv3F4FWCMzX6k2MtVNRGwCbE7L/8zM/FV1EXVfHzmHfsBGLHwOj1UXUff1hdeh7vrC+6iuIuItS9ufmc+vrFg6mAiVIuI44AJgNvAdYFvg+My8sdLAuiAizgKWONkrM49dieH0SkS8m+Lnvw6wWUSMBP4pMz9ZbWRdFxFfA74CvAr8DBgJfCozv19pYF0QET9h6e+lsSsxnN66Cfh74KVyey3gRuDdlUXUDRHxDuAcYKPMHB4RI4CxmfmVikPrsvIDxwHAEBb+0PGNqmLqrog4HTgEuA+YWzYnUJsPr33kHP4FOAmYBcwrmxMYUVlQ3VT31yEidgFOZkEiF0Bm5hZVxtUdfeR9dDBwOrAhxWvQ8ToMqDSwrplK8fOOTvYlsNLfSxZLKEXE9MwcGRH7AscAXwAuyMztKg5tmSJiQnlzF2Ar4LJyexwwNTM/XUlgPRARtwMfBK7JzG3Ltnsyc3i1kXVdREzLzFER8X7gIODTwM2ZObLayJYtIv5uafsz85crK5be6ngdltXWriLil8BngG/V+HfhOuA14G4WfOggM79UWVDdFBEPAiMy8/WqY+mpPnIODwE7ZuZzVcfSU3V/HSLiAYr/Z1NZkMhRp9ekj7yPHgL+ITPvrzqWvsAeoQU6stP9KRKg6RHRWcbadjLzIoCIOALYIzPfKLfPpbgCXiuZ+fgiP/q5S7pvm1q1/L4/cElmPl+Tt9JCiU65uPE7ys0HO95XNfJyRGyXmb8DiIjRFL10dbFWZt6xyHtnTlXB9NDgzKzNldYleJjid7qWH15LfeEcHgdeqDqIXqr76/BCZl5fdRC91BfeR7P6QhIUEWOB3crNWzLzp1XEYSK0wNSIuBEYCpwQEevScgWzJjYG1gU6xliuU7bVyePl8LgsP4gfC9TtF/6a8srZq8AnI2IgxVXx2oiI3YGLgEcpLhJsGhETajaW/VPADyPiyXJ7EMWwlLp4NiLeRjlUMSI+CDxVbUjddn1E7FOHIcaLahly/AowLSJuouUDbB2GHPeRc/jX8ubDwC0RcS0Ln0PbD7Os++sQER0jY26OiK8DP2Lh+H9XSWDd0EfeRweXN6dExGXAj1n4HH5URVw9ERGTgO2Bi8um4yJil8w8YaXH4tA4KHt+BgMDgYcz868RsT6wSWbeVW10XRcRR1KM3725bPo74OSOHqM6iIgNgP+mmNsRFD1ax9WlG7uckL8TRfL2YmbOLSftr5uZf642uq6LiKnAhzPzwXL7HRS9W6Orjax7ImJVYEuK99IDderViogtKBaZezfwF+AR4COZ+adKA+uGcnjo9ykqlL5Bjcaytww57kxm5vdWWjA91EfO4aSl7M7MPGWlBdNDdX8dIuLmpezOzNxzpQXTQ33kfXTBUnZnZn5spQXTSxFxFzCqpZhRP+D3VYwgMBEqRcTUun3I60xEvBXYsdy8vU4fvvuKiLgtM3euOo7eiIi7Fv2D1FlbOyuToKNp6XqnmG9Ti2QoIoZm5iNlIr1KZs7uaKs6tq6KiIcp5sndnTX9ZxMRx2Xmfy+rrZ31kXMYl5k/XFZbO6v76xARW2Tmw8tqa2d95H20S2beuqy2dlYmQrt3VIkrq8ndYiJUoYj4H+DCzLyz6lh6IyLeDAwD1uhoq9Nwpog4s5PmF4ApmXn1yo6nJyLiS8BdwI9q/OHvuxRDOf63bPoI0D8zj6wuqu6JiO9QjMfv6BE9DJibmR+vLqqui4jfLVqspW4XbCLiBuC9HVf96mgJr8PvOwpY1EEfPofF2tpZ3V+HPvI3qa++j+p2DocCkyhGMAXFBcsTMvPSlR2Lc4QW2AP4REQ8CrzMgiEcdboC/nHgOIphftMohmjdBrR9t3WLNYB3Ah1XZz4A3AscFRF7ZOanqgqsG/4VWBuYGxGvUqPhQC2OpqieeCxF/L8Czq40ou7bPheu1PeLiJheWTRdFBHvBLYG1msZEw4wgJYLHDXxFMV4/Oup33j8Q4EPA0Mj4pqWXesCdRmq2xfO4b0UhWc2WeRC2QBqUjyk7q9DX/ib1EfeRztTDJUe2DLnCYpz6FdNVD2TmZdExC0U84QC+GxVI5hMhBZ4b9UBLAfHUbypfpuZe5R/vGpTprb0dmDPzJwDEBHnUMwT2puiBG/by8x1q46htzLz9Yj4JjCZomeojlXj5kbE2zLzjzB/zk0dKhBuCbwPeBPwDy3ts4F/rCKgXnik/Fqt/KqT31AkchsA/6+lfTZFj28d9IVzeJKiXPPY8nuH2RSlnOug7q9DX/ib9CQwhXq/j1ajKILVnyKJ7vAixbIjdbM9C4auzwN+UkUQDo1rEcXinbuWm/+XmW1/9bhVRNyZmdtHxDSKOvmvR43WTYH56yzskJkvlNvrUcx1emddhhCUxTc+AgzNzC9HxKbAoMy8o+LQuiw6qRoHTKjZMMu9KBZJfpjiHDYHjszMpU38bRsRsXNm3lZ1HOobyjH4mZl/qTqWnoiIVWt4MaZP6Qt/k1rfRxGxfl0KMbWKiM3rVDSnM51UjTuUYgqEVeOqEhHHUVzZ6Cg/+H7gvMw8q7qouicirgKOpCgbvCdFpalVM3P/KuPqjog4CjiRYmJ7x7jR04BLKCrgfaa66Lqm7MWaR9Gz9a5y3taNmbl9xaF1WR+qGrc6C1eNq836HRGxBnAUxZCU1jl/bV8ZKCJ+Qln2uzOZOXYlhrPcRcTdmblN1XEsS0RsBnyN4v/BCxS/BwOAXwDHZ+aj1UXXNRExBvg68ARwAvBdig9QM4CJmfn7CsPrkvJi2NeBTYDrga+3fBj/cWYeVGF4XRIRe1AMVd+UYijZDODbHT3u7a784H1GZj5bvqcup/g/vSpweNZosfBWEfGHzHzHsu/ZXtqpapxD4xY4iqIX5WWAiDidYn5NbRKhzHx/efPkKMpdrgf8rMKQui0zz49iNfodKP5pfy4zO9aBafskqLRjZm4XEb8HyMy/RLEmUp2s2pEEAWTmH8oqbLVRzge6FLi8Lv+sF/G/wAPAvsApFL2MdVlT64zy+8HAWylKaENx1e/RKgLqrkXmQiy0i+Kc6uAy4L8oyq7PhfkfOMZR/G7sVF1oXXY2cBLFsKzfAJ/OzL3LHt+zgTpU6PwucCXwW4rPGr+MiH8oeyM2rzSyLiiTiI2Amyje+48AfwSuiIjTalJx7YDMPL68/XXgkMy8s7zI9wNgTHWhdU1EzGbBBaaOlbbX6miv2TxkKH6nO9a9XK+qIOwRKkXE3RSTq18rt9cA7qzDVT+gY/2auzJzeNWx9EbLsLItMvOU8ormW2s2rOx2igmNd5YJ0UCKHqG2H9bXoY9UjducYgHVQyiu/F1GkRQ9VmlgXdQxFDTKsuVlInpD1mDNjg4R8avM3G1Zbe0oIt6gGLbR2T/JD9ZhLmBEzMjMYd3d105ah0RHxGOZuVln+9rZokPUI+KjFL1bY4Eftnu1r9Ye0IjoD/wyM3cpRzv8Xx0+d0SxyPnwzJwTEb/NzJ1a9tWlh/csioThM5k5q2x7JDOHVhtZ91k1rj1dANxeDi+DYu2L86sLp3syc15ETI+IzeryQW8JzqYcVkZxFXw2xZW02gwrA84ErgI2jIhTKSYxfqHakLqt9lXjyjHUXwO+FhHDKF6D06lPdZ2O+RB/jYjhwJ+BIdWF0yMDo2WdkYgYSrFwdR3cRTGU5p5Fd0TE31cQT09MjYizKeb7PV62bQpMANp+SFnptYjYh+IDYEbEQZn544j4O+pR/ARg1YhYo+NCa2Z+PyL+DNxAUWG03c2LiLdksebLxpR/Q8vRDrH0Q9vG/wDXlb1bP4uI/6KYCrEXRZXdtpeZ/xIRo4FLIuLHwDdZyhDkdtZOVePsEWoREdsB76H84FeHscetIuIXFG+qOyhKgAP1Go8fZS38Ra4CTs+FyyC3vbJi314U76WbMrMuQ5r6lIgYAnyIoldoLnBZZv6/pR7UJqIoh38lsA1wIUW1oC9k5reqjKs7ImI/4DyKghVQJHL/lJk3VBZUF0XErsCfOruwFBFjMnNKBWF1Szkk9yjgQIr5KUGREP0EOL8Oc+bKIkZfo7hA9mmKizQTKOYM/WNm/qbC8LokIj4N/G7ReSgRsS3wtczcu5rIuiYiDqF4DR6kWN7i6My8thzt8N+Z+eFKA+yisgjQ0cA7KDoCZlJctLygToU4yhFA/0wxxPVtmblxxSH1SERsQjE0dH6nTBUFmRqfCJWVdJaovAJSC+UVssXUaRJgHxlW9r+Zediy2tpZROwCnMzif6S2qCqm7irfS6tSTIq9POu1+vkqFMOvLq86lt4qC1a8s9ysVcEKSYXys9IWwEOZ+deKwxEQEYOAbTPzuqpj6a5yHv4hFOtEdiy4nVVcuDcRiniEomuxtXu3Yzvr9MGvL4iIj1D8cmxHMZzjg8CJNZmMCSzo1WrZ7gfcnZlbVRhWt5TjqT9Nsd7C/OEnWaNSoxHxzsx8oLz91qq63XuqLnNpliYijgEu7vjgVM4pODQzazXMclER8cXMPKXqOHqjzucQET/NzPdVHcfyUOfXoe76wvsoIs7LzIlVx9FdUSyVMqIdLow1PhHqSxapKNLhBYpFxP6tLlfE6zqsLCJOAD4HrAm80tEM/I2iFPtKr4/fUxFxe2buWHUcy8uiyWkdRMQXgFcpijy0DnWtUy/1YuuY1WWC+9IsOmm/jup8Dn3hPdShzq9D3fWF91Ed/7cBRMT1wLjMfKnqWCyWUGqpVtaxCGbtqpUB36BYPfkHFB/Ax1OUunyQonzn7pVFtgyLDFF8mmLdoPn76vDhLzO/Cnw1Ir5ap6SnVTlPDuDmiPg6xWTS+VdsMvN3lQTWe3WZ0NuqY72gY1rakmJ4Sl2sEhGR5RW3sne0FqXkI+LFJe2iuNjR9vrCOSxB3ebv9tXXoe5q9T5agqerDqA7ysp3SXGxeFpE3MTCnzGOXekx2SNUiL6xCOZiV/E7ykS2e8GBvjREsZxfMy0zXy7LpG5HMaG07VeCjmL9qSXJrFHp5lYR8cm6D8eqozKZHgKcS/H7/Ang8cz8tyrj6oqIeIxiSYVZnex7PDM3rSCsbukL59AX+DpoeYqIcYtOF+isrR1FxISl7c/Mi1ZWLB3sEVqgLyyCOS8iPgRcUW5/sGVfW2e8dayDvxTnACPLakf/QVGG/XtAp8Us2klm7lF1DMtLROwE3JuZszPz7IhYF9gqM2+vOrauiGLdoKMp1lcAuAX4Vp2qGwGfBf6J4jwCuBH4TqURdd33KIqFLPbhlaLXvQ76wjkAnRZwqdNFsj7zOtRdzd9HHU4AFk16OmtrOx2JTkSsDbyWCy/0vHoVMdkjVOoj1cq2AP6bBStt30Yx4f0JYHRm/rqq2JalZUhWp+o0JKulBPgXgScy8/y6jeONiNMoyrr+tdx+M8U8sxMrDawbyosa27UMy1oFmFKX1yEivkNR9a7jCtlhwNzM/Hh1UUnV6AsFXFS9Or+PIuK9wP4US0Jc1rJrAMVFvh0qCawHIuK3wN93zBGKiHUoPnO/e2XHYo/QAp0tglmbD30AZTGEf1jC7rZNgkpLW9slKRZYrYvZZeGEjwK7lVc6Vq04pu56b2Z+rmOj7CHdn3r9TsyfmwLzFx2u09+87RcZzvqLiJheWTQ9EMVCtl8FtgLW6Giv2dVXtYcXMvP6qoNQ7dX5ffQkRfGrsRSJXIfZFMldnazRWighM1+KiLWqCKROHwpWqMy8OCKmsqBa2UF1qVbWmbr1QPSlIVkU5b8/DByVmX8uC298veKYuqtfRKzeUdoyItakom7rXng4Io6lGKoI8EkWLOxZB3Mj4m2Z+UeY3+M7dxnHtJsLgJOA/wT2AI6knoUrVJE+XMBFK1FfeB9l5nRgekT8oGOIdDlaY9PM/Eu10XXbyxGxXcfPPSJGU1RJXekcGteivHK/EQsvILnYquJ1UNeykH1kXkTtRcR/UFx1uoCiR+5jwDWZ+bVKA+uGiNiQoqe3ozfx58CnMrMWVXYiYi+Kn//DFMnD5sDHMvMXlQbWDRExNTNHR8TdmblN2fZ/mblr1bGpHvpqARetXH3pfRQRt1D8f+4PTAOeAX6Zmf9aYVjdEhHbA5dS9HIBDAIOycypSz5qBcViIlSIiH+huHI5i+Kqa8cEuhGVBtZDEfGVOs3n6NAX5kWUk/TPAt5FUSq4H/BSZq5XaWDdFBH7AX9POck9M2+oOKRGiYiOHrgtKV6DBwCyDRag66qIuBXYlaKAyy8o5itOyswtKw1MtRMRW+Qia+F11iYtTV94H3Vc6I6Ij1P0Bp0UEXfV7fNqeeF7/v+3qi54r1LFk7ap44AtM3PrzByRmdvU7U3VISLWB6aWXY11s31mTsjMX5RfRwK1KWFe+iZwKDCDYo2IjwP/U2lE3VRWdLkxM/8dOA9YvfyjVRsRMTgiroqIpyNiVkRcGRGDq46rG27LzNcz867MnF4mQLdVHVQ3fQpYCzgWGE0xb26p5VOlJbiik7a2r5KlttMX3kf9I2IQRdGEn1YdTE9ExDiKeUL3AAcCly2raNaK4hyhBR4HXqg6iJ6IiJ8Cx2fmPeUvx+8oJtS9LSLOy8z/qjTA7ukL8yLIzIciol9ZGvKCiPhN1TF106+AXcvxxz+neD8dQrHocF1cQFGadly5/dGybe/KIuqCiHgrsAmwZkRsy4I5NQMokorayMw7y5svRcQJmfnnSgNS7UTEO4GtgfUi4uCWXQNoKcAhLU0fex+dAtwA/Doz7yw/J82oOKbu+kJm/jAi3gPsC5xBMZ93x6Uftvw1PhGKiI4xlQ8Dt0TEtSw8ge4blQTWPUPLrBqKyciTM/Pwct2UW4H/qiyy7vsMxWTG1nkRR1YbUre9Uq5BNS0ivgY8BaxdcUzdFZn5SkQcBZyVmV/rWGOrRgZm5gUt2xdGxKeqCqYb9gWOAAYDrX9/XgQ+19kBNXEdxeLCUndsCbwPeBMLV0WdDfxjFQGplvrM+6hcOPWHLdsPAx+oLqIe6bjAfQBwTmZeHREnVxFI4xMhYN3y+2Pl12rlV520jqvcC/g2QGbOjoh51YTUM5l5U1lyt3XcaG3mRJQOo5gX9M8UJS03pX5/pCIidqboATqqbKvb34tnI+KjwCXl9qFA268VUS44d1FEfCAzr6w6nuXIanHqtsy8Grg6InbOzLoNDVWb6Ovvo4j4YmaeUnUc3fBERHyLYh7y6eWc2Eqm61gsoQ+IiJ9QrNg+E/guRQ/RX8uSx1Myc+tKA+yGiDgGuHiRhTwPzcyzKw2sYSJiN+DfgVsz8/Sy6/1TmXlsxaF1WVm2/JssWGD4VuC4zPxTdVF1XV9Y1LZVRHzS32N1V0R8A7gyM2+tOhbVV0RskJnPtmx/FNgBuAf4dtb8w3BEPJaZm1UdR1eVawbtB9ydmTPKaR3bZOaNKz2Wmr/2y01ETAbGLfKh49LM3LfSwLqgLBN8CkX5wf/peCNFxB7A6Mw8o8r4uiMipmXmqEXaalEKPCLuWtr+GhffeKtzO1a+zt73dVsfrKygeG9mzi6316VYAf32aiNTXUTEM8CfgIHAZcAlmVm3YbqqWOvfzog4kaKa5Q8ohsvNzMy2X5A0Il5c0i5gzcys1aiNcn7QsMy8ICIGAutk5iMrO45a/dBWsIEdSRBAZv6lTDDaXrkuyic6ab8ZWFrt/Ha0SkREx9WZcm2nugxVnEex5s4PgJ9Q0eJgK0At53aU87O+QvE6/AwYSdGr9f1KA+u6vrCo7Tks/N55uZM2aWlmZuaYcsj0eOD75f+FSyiSoj9UG55qonVo7sHArpn5ckT8gKLAVB38laKy7qxFd0TE4ys/nJ6LiJOAMRTTIC6gWDbl+8AuKzsWE6EF5kbEZlkuoBoRm1N8qG175dC4JcaamWNXYji9dQNweUScS3FOn6D4ENv2MnNUWZnmUIpk6L7y+42ZOafS4HqnrnM79snM/4iI91MMGx1HcWGgLonQ94GbIqJ1UduLln5I25l/UQMgM+dFhP931B0JkJkzgC8DX46IERR/Z68D3l5hbKqPjiqcqwD9MvNlgMx8IyLqUpn2exQFpBZLhCg+a9TJ+4FtKZPQzHyyHDGw0vkPaYHPA7+OiF+W27sBEyuMpzs6hr4FRaGE2iw+2onPUvzcj6ZcyBP4TqURdUNmPkCxMO9JEXEIxR+u04GvVxpYF0XE6Zn52YgYV1amgbL4Rg11rHu0P8WV4+cj6pPTlZX67qKYTArw5RouavtwRBxL0QsE8EmKCp1SVy32S5uZdwF3ASes/HBUU0+xoArn8xExKDOfKtddrMWFyqXND83Mz67MWJaDv2VmRkTH6J/KKus6R6hFRGwA7ETxh/e21ol1dVGX+TRdERHbZWZduqwBiIhNKIZvvB/4C3A5cFVmvlRpYF0UEXdTDFu6vU5zUToTEZOAgyiGxu1AUTb1p5m50tcp6KmI2Igi9gTuKIfB1kY5vPhMYM+y6ecUwxNrdR6qTkSsU5e/n6qfcpjl6pn5StWxNElE/DswjGJdv69SjHj4QWaetdJjMRFaoCyQMIyWxbUy81fVRdR9dZtMvTR1O5eyN3FdiuTnCuD51v2Z+Xxnx7WTiPg6RY/c2sArFBcFsuN7Zg6oMLxuK3+nX8zMuWWVmgF1KfwQER+i6Em8heLnvyvwmczsbGV0SZLaXhRDMwYD7wT2ofj/dkNmTq4kHhOhQkR8HDiO4sWZRtEzdFtm7rm049pBRLylZfNmYHdahhPU4QN4Z+rWuxURj7JgrlbrL1ZHErHFSg+qhyLi6sw8sOo4eiMixgE/K9fTOpGip+srdelljIjpwN4dvSdlVZ2fZ+bIaiPruogYDJxFMQE2gV9TlDCfWWlgkqTKRMTUzBxddRzgHKFWxwHbA7/NzD3KSe9fqjimrprKgqv2sHAFlARq8wF8EXX5+QOQmUOqjmF5ycwDy2FZ25dNt2fmM1XG1ANfyMwfliU696WYS3cOUJehcassMoTsOSpacK4XLqCYxDuu3P5o2bZ3ZRFJkqr224jYPjPvrDoQE6EFXsvM1yKCsmTtAxGxZdVBdUVmDq06huWl7DL9CLBFZp5SLor51sy8o+LQGqXsTTmDBcOyzoqIug3L6qgEdABwTmZeHREnVxhPd/0sIm6gKBMMcAhFlaw6GZiZF7RsXxgRn6oqGElSW9gD+EQ5kuZlFoycWenrLZoILTAzIt4E/BiYHBF/AZ6sNKJmOptiPZ49KRaJnQ1cyYKeCa0cJ1KsV7DQsCyKuU918UREfIui6trpEbE6NepRyczPRMTBwHso/kmcl5lXVRxWdz1bruDekcwdStGzJUlqrvdWHUAH5wh1IiL+DliPYn7B36qOp0k6CiS0zg+KiOl1mhfRF0TE3Zm5Tcv2KsD01rZ2VxZH2A+4OzNnRMQgYHhVEzK7q7wwM6zc/ENmvlBhOD1S9uh+E9i5bLqVYo7Qn6qLSpJUhbKS6Oco1v+6G/hqZr5YZUz2CC2i/PD0MnCfSVAl3ijLWXbUlh9I0UOklav2w7LKcqg/gvlrFOxF0SPR1olQRKwGnEdR+vthil6szSPiKuATdfq7VC5QXacFnSVJK873KOa1nwW8j2J5hSOqDKg2w0RWlIgYGxGPRsTvImJ/4F6KK5h3R8SEisNrojOBq4ANI+JUiipTp1UbUrOU87TOBL4FjABGUgzLqtWCbRGxWkQcFBGXUyymtxdwbsVhdcWJFIvBbpqZ22XmKGAzigtXX6gysO6KiK9FxICIWDUiboqIjqFykqTmeWtmfj4zb8jMf6H4jFGpxg+NK0vUjqMYCnczMCIzHy67726q01CgvqKs2LcXxbyImzLz/opDapx2Km3ZXRGxN0XPz74Uv9OXAWfVpapfRNwD7LDoAn8RsQ5FVcvh1UTWfRExLTNHRcT7KXq4Pg3c7FBXSWqe8jP37iyocrzQki9VLPfi0DiYl5l/AIiIRzLzYYDMfDoi5lQbWmPNAv6P4v25ZkRsV5e1X/qQtilt2QM3ULx/3pOZjwBExH9XG1K3zOtslfPMfCki6nblatXy+/7AJZn5fNHhKElqoPUohsa1/iPo+HxXyXIvJkKwSrn6/CrAvPJ2xwvU+KGDK1tEfJlivOgfWXhx0rZf2LaPaZvSlj0wGhgP/DwiHgYuBfpVG1K35CJ/h1rVbb7cTyLiAeBV4JPlnL/XKo5JklSBdhyZ4dC44oPePDr/0JGZWdfFSGspIh4EtqnThPC+KCI276y9btW+ImIXimFyHwCmAVdl5nmVBrUMfe1vUpnUvZiZc8tiNAMy889VxyVJUuMTIbWXiLgSOLpj/RqtXO1Y2nJ5KMt/7w2Mz8wjq46nKcqFeX+WmbMj4kRgO+ArDnWVJLUDEyG1lYgYA1wN3AO83tGemZbgXQki4mcU43d/RVHact3MPKLSoFRbEXFXZo6IiPcAXwXOAD6XmTtWHJokSc4RUtu5CDidojeibvMh+oK3Zubny9s3RIRX7tUbc8vvBwDnZObVEXFyhfFIkjSfiZDazbOZeWbVQTRYLDJRv1/rdhWlLVVrT0TEt4C/B06PiNWxCI0kqU04NE5tJSK+QTEk7hoWHhpnz8RK0Ncm6qtaZXGE/YC7M3NGRAwChmfm5IpDkyTJREjtJSJu7qQ5M9Py2VKNRcTawPuBQzPzgKrjkSTJREiStEJExGoUi6l+mKJn6ErgR5n5k0oDkyQJEyG1mYhYDzgJ2K1s+iVwSma+UF1UkrojIvamWL9pX+Bm4DLgrHZcTE+S1FwmQmor5TpC91BUjwM4DBiZmQdXF5Wk7oiIecD/AUdk5iNl28POMZMktROrxqndvC0zP9Cy/aWImFZVMJJ6ZDQwHvh5RDwMXAr0qzYkSZIWZhlTtZtXy8UXAYiIXYBXK4xHUjdl5u8z87OZ+TbgZGBbYLWIuD4iJlYbnSRJBYfGqa1ExEjge8B6FCWcn6cYXjO90sAk9UpErALsDYzPzCOrjkeSJBMhtaWIGACQmS9WHYskSZL6HofGqS1ExD9ExOYtTZ8C/i8iromIoRWFJUmSpD7KREjt4lTgGYCIeB/wUeBjwDXAuRXGJUmSpD7IREjtIjPzlfL2wcD5mTk1M78DDKwwLkmSJPVBJkJqFxER65QTqvcCbmrZt0ZFMUmSJKmPch0htYv/AqYBLwL3Z+YUgIjYFniqurAkSZLUF1k1Tm0jIjYBNgSmZ+a8sm0QsGpmPlZpcJIkSepTTIQkSZIkNY5zhCRJkiQ1jomQJEmSpMYxEZIkSZLUOCZCkiRJkhrHREiSJElS45gISZIkSWocEyFJkiRJjWMiJEmSJKlx/j87kEQ8pZvpAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "j = 1\n",
    "categorical_features.remove('Salary') #removing column salary since it is output variable itself. \n",
    "\n",
    "for feature in categorical_features:\n",
    "    df = pd.DataFrame()\n",
    "    df['More than 50k'] = salary[salary['Salary'] == ' >50K'][feature].value_counts()\n",
    "    df['Less than 50k'] = salary[salary['Salary'] == ' <=50K'][feature].value_counts()\n",
    "    \n",
    "    df.plot.bar(figsize=(14,6))\n",
    "    \n",
    "    j = j + 1 \n",
    "    \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d140c6",
   "metadata": {},
   "source": [
    "Insight:\n",
    "\n",
    "Majority of both the groups is employed in private sectors\n",
    "\n",
    "As educational qualification decreases, no. of people in group of 'salary more than 50k' reduces.\n",
    "\n",
    "Majority of people having salary more than 50k are married, whereas very small proportion of them is never married, divorced, widow, separated. \n",
    "Significant proportion of  people having salary less than 50k are never married, divorced, widow, separated. \n",
    "\n",
    "Most of the people having salary less than 50k are engaged in manual and low skilled work like craft repair, clerk, transport, machine inspection, farming fishing, handlers cleaners and other services.\n",
    "Whereas people having salary more than 50k are in high skilled works like managers, professors, sales\n",
    "\n",
    "Insignificant amount of non natives are having salary more than 50k"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e2c7826",
   "metadata": {},
   "source": [
    "# Data Pre-Processing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a3fb2fbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.689544Z",
     "start_time": "2022-11-10T14:58:58.681617Z"
    }
   },
   "outputs": [],
   "source": [
    "salary1 = salary.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "34e3f29e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.705544Z",
     "start_time": "2022-11-10T14:58:58.689544Z"
    }
   },
   "outputs": [],
   "source": [
    "salary1.drop('Salary',axis=1, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e3330908",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.737610Z",
     "start_time": "2022-11-10T14:58:58.705544Z"
    }
   },
   "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>educationno</th>\n",
       "      <th>maritalstatus</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>native</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45216</th>\n",
       "      <td>33</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45217</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45218</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Asian-Pac-Islander</td>\n",
       "      <td>Male</td>\n",
       "      <td>5455</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>35</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45221 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass   education  educationno        maritalstatus  \\\n",
       "0       39          State-gov   Bachelors           13        Never-married   \n",
       "1       50   Self-emp-not-inc   Bachelors           13   Married-civ-spouse   \n",
       "2       38            Private     HS-grad            9             Divorced   \n",
       "3       53            Private        11th            7   Married-civ-spouse   \n",
       "4       28            Private   Bachelors           13   Married-civ-spouse   \n",
       "...    ...                ...         ...          ...                  ...   \n",
       "45216   33            Private   Bachelors           13        Never-married   \n",
       "45217   39            Private   Bachelors           13             Divorced   \n",
       "45218   38            Private   Bachelors           13   Married-civ-spouse   \n",
       "45219   44            Private   Bachelors           13             Divorced   \n",
       "45220   35       Self-emp-inc   Bachelors           13   Married-civ-spouse   \n",
       "\n",
       "               occupation    relationship                 race      sex  \\\n",
       "0            Adm-clerical   Not-in-family                White     Male   \n",
       "1         Exec-managerial         Husband                White     Male   \n",
       "2       Handlers-cleaners   Not-in-family                White     Male   \n",
       "3       Handlers-cleaners         Husband                Black     Male   \n",
       "4          Prof-specialty            Wife                Black   Female   \n",
       "...                   ...             ...                  ...      ...   \n",
       "45216      Prof-specialty       Own-child                White     Male   \n",
       "45217      Prof-specialty   Not-in-family                White   Female   \n",
       "45218      Prof-specialty         Husband                White     Male   \n",
       "45219        Adm-clerical       Own-child   Asian-Pac-Islander     Male   \n",
       "45220     Exec-managerial         Husband                White     Male   \n",
       "\n",
       "       capitalgain  capitalloss  hoursperweek          native  \n",
       "0             2174            0            40   United-States  \n",
       "1                0            0            13   United-States  \n",
       "2                0            0            40   United-States  \n",
       "3                0            0            40   United-States  \n",
       "4                0            0            40            Cuba  \n",
       "...            ...          ...           ...             ...  \n",
       "45216            0            0            40   United-States  \n",
       "45217            0            0            36   United-States  \n",
       "45218            0            0            50   United-States  \n",
       "45219         5455            0            40   United-States  \n",
       "45220            0            0            60   United-States  \n",
       "\n",
       "[45221 rows x 13 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "90a978be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.801883Z",
     "start_time": "2022-11-10T14:58:58.737610Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "   age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "0   39           13         2174            0            40   \n",
       "1   50           13            0            0            13   \n",
       "2   38            9            0            0            40   \n",
       "3   53            7            0            0            40   \n",
       "4   28           13            0            0            40   \n",
       "\n",
       "   workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
       "0                       0                     0                   0   \n",
       "1                       0                     0                   0   \n",
       "2                       0                     0                   1   \n",
       "3                       0                     0                   1   \n",
       "4                       0                     0                   1   \n",
       "\n",
       "   workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "0                        0                            0  ...   \n",
       "1                        0                            1  ...   \n",
       "2                        0                            0  ...   \n",
       "3                        0                            0  ...   \n",
       "4                        0                            0  ...   \n",
       "\n",
       "   native_ Portugal  native_ Puerto-Rico  native_ Scotland  native_ South  \\\n",
       "0                 0                    0                 0              0   \n",
       "1                 0                    0                 0              0   \n",
       "2                 0                    0                 0              0   \n",
       "3                 0                    0                 0              0   \n",
       "4                 0                    0                 0              0   \n",
       "\n",
       "   native_ Taiwan  native_ Thailand  native_ Trinadad&Tobago  \\\n",
       "0               0                 0                        0   \n",
       "1               0                 0                        0   \n",
       "2               0                 0                        0   \n",
       "3               0                 0                        0   \n",
       "4               0                 0                        0   \n",
       "\n",
       "   native_ United-States  native_ Vietnam  native_ Yugoslavia  \n",
       "0                      1                0                   0  \n",
       "1                      1                0                   0  \n",
       "2                      1                0                   0  \n",
       "3                      1                0                   0  \n",
       "4                      0                0                   0  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encoding categorical 'input' column\n",
    "salary1 = pd.get_dummies(salary1)\n",
    "salary1.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7ba0927e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.817868Z",
     "start_time": "2022-11-10T14:58:58.801883Z"
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   },
   "outputs": [
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       "    <tr>\n",
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       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "0   39           13         2174            0            40   \n",
       "1   50           13            0            0            13   \n",
       "2   38            9            0            0            40   \n",
       "3   53            7            0            0            40   \n",
       "4   28           13            0            0            40   \n",
       "\n",
       "   workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
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       "2                       0                     0                   1   \n",
       "3                       0                     0                   1   \n",
       "4                       0                     0                   1   \n",
       "\n",
       "   workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "0                        0                            0  ...   \n",
       "1                        0                            1  ...   \n",
       "2                        0                            0  ...   \n",
       "3                        0                            0  ...   \n",
       "4                        0                            0  ...   \n",
       "\n",
       "   native_ Puerto-Rico  native_ Scotland  native_ South  native_ Taiwan  \\\n",
       "0                    0                 0              0               0   \n",
       "1                    0                 0              0               0   \n",
       "2                    0                 0              0               0   \n",
       "3                    0                 0              0               0   \n",
       "4                    0                 0              0               0   \n",
       "\n",
       "   native_ Thailand  native_ Trinadad&Tobago  native_ United-States  \\\n",
       "0                 0                        0                      1   \n",
       "1                 0                        0                      1   \n",
       "2                 0                        0                      1   \n",
       "3                 0                        0                      1   \n",
       "4                 0                        0                      0   \n",
       "\n",
       "   native_ Vietnam  native_ Yugoslavia  Salary  \n",
       "0                0                   0   <=50K  \n",
       "1                0                   0   <=50K  \n",
       "2                0                   0   <=50K  \n",
       "3                0                   0   <=50K  \n",
       "4                0                   0   <=50K  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary1['Salary'] = salary.Salary\n",
    "salary1.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8bfd9e48",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.962715Z",
     "start_time": "2022-11-10T14:58:58.817868Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "03bf4034",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:58.978781Z",
     "start_time": "2022-11-10T14:58:58.962715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encoding categorical 'output' column\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(salary1.Salary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70833c4a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.018972Z",
     "start_time": "2022-11-10T14:58:58.978781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\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>educationno</th>\n",
       "      <th>capitalgain</th>\n",
       "      <th>capitalloss</th>\n",
       "      <th>hoursperweek</th>\n",
       "      <th>workclass_ Federal-gov</th>\n",
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       "      <th>workclass_ Self-emp-inc</th>\n",
       "      <th>workclass_ Self-emp-not-inc</th>\n",
       "      <th>...</th>\n",
       "      <th>native_ Puerto-Rico</th>\n",
       "      <th>native_ Scotland</th>\n",
       "      <th>native_ South</th>\n",
       "      <th>native_ Taiwan</th>\n",
       "      <th>native_ Thailand</th>\n",
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       "      <th>45216</th>\n",
       "      <td>33</td>\n",
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       "      <th>45218</th>\n",
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       "      <td>13</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>44</td>\n",
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       "      <td>5455</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>35</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45221 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "0       39           13         2174            0            40   \n",
       "1       50           13            0            0            13   \n",
       "2       38            9            0            0            40   \n",
       "3       53            7            0            0            40   \n",
       "4       28           13            0            0            40   \n",
       "...    ...          ...          ...          ...           ...   \n",
       "45216   33           13            0            0            40   \n",
       "45217   39           13            0            0            36   \n",
       "45218   38           13            0            0            50   \n",
       "45219   44           13         5455            0            40   \n",
       "45220   35           13            0            0            60   \n",
       "\n",
       "       workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
       "0                           0                     0                   0   \n",
       "1                           0                     0                   0   \n",
       "2                           0                     0                   1   \n",
       "3                           0                     0                   1   \n",
       "4                           0                     0                   1   \n",
       "...                       ...                   ...                 ...   \n",
       "45216                       0                     0                   1   \n",
       "45217                       0                     0                   1   \n",
       "45218                       0                     0                   1   \n",
       "45219                       0                     0                   1   \n",
       "45220                       0                     0                   0   \n",
       "\n",
       "       workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "0                            0                            0  ...   \n",
       "1                            0                            1  ...   \n",
       "2                            0                            0  ...   \n",
       "3                            0                            0  ...   \n",
       "4                            0                            0  ...   \n",
       "...                        ...                          ...  ...   \n",
       "45216                        0                            0  ...   \n",
       "45217                        0                            0  ...   \n",
       "45218                        0                            0  ...   \n",
       "45219                        0                            0  ...   \n",
       "45220                        1                            0  ...   \n",
       "\n",
       "       native_ Puerto-Rico  native_ Scotland  native_ South  native_ Taiwan  \\\n",
       "0                        0                 0              0               0   \n",
       "1                        0                 0              0               0   \n",
       "2                        0                 0              0               0   \n",
       "3                        0                 0              0               0   \n",
       "4                        0                 0              0               0   \n",
       "...                    ...               ...            ...             ...   \n",
       "45216                    0                 0              0               0   \n",
       "45217                    0                 0              0               0   \n",
       "45218                    0                 0              0               0   \n",
       "45219                    0                 0              0               0   \n",
       "45220                    0                 0              0               0   \n",
       "\n",
       "       native_ Thailand  native_ Trinadad&Tobago  native_ United-States  \\\n",
       "0                     0                        0                      1   \n",
       "1                     0                        0                      1   \n",
       "2                     0                        0                      1   \n",
       "3                     0                        0                      1   \n",
       "4                     0                        0                      0   \n",
       "...                 ...                      ...                    ...   \n",
       "45216                 0                        0                      1   \n",
       "45217                 0                        0                      1   \n",
       "45218                 0                        0                      1   \n",
       "45219                 0                        0                      1   \n",
       "45220                 0                        0                      1   \n",
       "\n",
       "       native_ Vietnam  native_ Yugoslavia  Salary  \n",
       "0                    0                   0       0  \n",
       "1                    0                   0       0  \n",
       "2                    0                   0       0  \n",
       "3                    0                   0       0  \n",
       "4                    0                   0       0  \n",
       "...                ...                 ...     ...  \n",
       "45216                0                   0       0  \n",
       "45217                0                   0       0  \n",
       "45218                0                   0       0  \n",
       "45219                0                   0       0  \n",
       "45220                0                   0       1  \n",
       "\n",
       "[45221 rows x 103 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary1.Salary = encoder.transform(salary1.Salary)\n",
    "salary1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c1931ce",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "66348f8b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.035029Z",
     "start_time": "2022-11-10T14:58:59.018972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30161, 14)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_train.shape #salary.train is original train set provided by ExcelR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88e5ee1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.051069Z",
     "start_time": "2022-11-10T14:58:59.036548Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15060, 14)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "salary_test.shape #salary.test is original train set provided by ExcelR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9b35e62d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.067062Z",
     "start_time": "2022-11-10T14:58:59.051069Z"
    }
   },
   "outputs": [],
   "source": [
    "#we concatenated both train n test set for EDA and Visualisation.\n",
    "# now lets separate them. first 30161 entries form train set and rest 15060 entries are test set.\n",
    "\n",
    "train_set = salary1.iloc[:30161, :]\n",
    "test_set = salary1.iloc[30161:, :]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9318e3bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.083148Z",
     "start_time": "2022-11-10T14:58:59.067062Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of new dataframes - (30161, 103) , (15060, 103)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of new dataframes - {} , {}\".format(train_set.shape, test_set.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ef16b3b3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.115062Z",
     "start_time": "2022-11-10T14:58:59.083148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\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",
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       "  </tbody>\n",
       "</table>\n",
       "<p>30161 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "0       39           13         2174            0            40   \n",
       "1       50           13            0            0            13   \n",
       "2       38            9            0            0            40   \n",
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       "4       28           13            0            0            40   \n",
       "...    ...          ...          ...          ...           ...   \n",
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       "30158   58            9            0            0            40   \n",
       "30159   22            9            0            0            20   \n",
       "30160   52            9        15024            0            40   \n",
       "\n",
       "       workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
       "0                           0                     0                   0   \n",
       "1                           0                     0                   0   \n",
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       "...                       ...                   ...                 ...   \n",
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       "\n",
       "       workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "0                            0                            0  ...   \n",
       "1                            0                            1  ...   \n",
       "2                            0                            0  ...   \n",
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       "30158                        0                            0  ...   \n",
       "30159                        0                            0  ...   \n",
       "30160                        1                            0  ...   \n",
       "\n",
       "       native_ Puerto-Rico  native_ Scotland  native_ South  native_ Taiwan  \\\n",
       "0                        0                 0              0               0   \n",
       "1                        0                 0              0               0   \n",
       "2                        0                 0              0               0   \n",
       "3                        0                 0              0               0   \n",
       "4                        0                 0              0               0   \n",
       "...                    ...               ...            ...             ...   \n",
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       "30158                    0                 0              0               0   \n",
       "30159                    0                 0              0               0   \n",
       "30160                    0                 0              0               0   \n",
       "\n",
       "       native_ Thailand  native_ Trinadad&Tobago  native_ United-States  \\\n",
       "0                     0                        0                      1   \n",
       "1                     0                        0                      1   \n",
       "2                     0                        0                      1   \n",
       "3                     0                        0                      1   \n",
       "4                     0                        0                      0   \n",
       "...                 ...                      ...                    ...   \n",
       "30156                 0                        0                      1   \n",
       "30157                 0                        0                      1   \n",
       "30158                 0                        0                      1   \n",
       "30159                 0                        0                      1   \n",
       "30160                 0                        0                      1   \n",
       "\n",
       "       native_ Vietnam  native_ Yugoslavia  Salary  \n",
       "0                    0                   0       0  \n",
       "1                    0                   0       0  \n",
       "2                    0                   0       0  \n",
       "3                    0                   0       0  \n",
       "4                    0                   0       0  \n",
       "...                ...                 ...     ...  \n",
       "30156                0                   0       0  \n",
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       "30159                0                   0       0  \n",
       "30160                0                   0       1  \n",
       "\n",
       "[30161 rows x 103 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "caaf8ff7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.147189Z",
     "start_time": "2022-11-10T14:58:59.115062Z"
    }
   },
   "outputs": [
    {
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      ],
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       "       age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "0       39           13         2174            0            40   \n",
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       "\n",
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       "\n",
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       "30158                      1                0                   0  \n",
       "30159                      1                0                   0  \n",
       "30160                      1                0                   0  \n",
       "\n",
       "[30161 rows x 102 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = train_set.iloc[:,:102]\n",
    "x_train "
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  {
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   "execution_count": 38,
   "id": "75ac3b91",
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     "end_time": "2022-11-10T14:58:59.163189Z",
     "start_time": "2022-11-10T14:58:59.147189Z"
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   },
   "outputs": [
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    "y_train"
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  {
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   "execution_count": 39,
   "id": "96dfd96f",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15060 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "30161   25            7            0            0            40   \n",
       "30162   38            9            0            0            50   \n",
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       "\n",
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       "45219                        0                            0  ...   \n",
       "45220                        1                            0  ...   \n",
       "\n",
       "       native_ Puerto-Rico  native_ Scotland  native_ South  native_ Taiwan  \\\n",
       "30161                    0                 0              0               0   \n",
       "30162                    0                 0              0               0   \n",
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       "       native_ Thailand  native_ Trinadad&Tobago  native_ United-States  \\\n",
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       "45218                 0                        0                      1   \n",
       "45219                 0                        0                      1   \n",
       "45220                 0                        0                      1   \n",
       "\n",
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       "...                ...                 ...     ...  \n",
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       "45220                0                   0       1  \n",
       "\n",
       "[15060 rows x 103 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set "
   ]
  },
  {
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   "id": "cdafed6d",
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     "end_time": "2022-11-10T14:58:59.227224Z",
     "start_time": "2022-11-10T14:58:59.195189Z"
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       "<p>15060 rows × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  educationno  capitalgain  capitalloss  hoursperweek  \\\n",
       "30161   25            7            0            0            40   \n",
       "30162   38            9            0            0            50   \n",
       "30163   28           12            0            0            40   \n",
       "30164   44           10         7688            0            40   \n",
       "30165   34            6            0            0            30   \n",
       "...    ...          ...          ...          ...           ...   \n",
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       "45220   35           13            0            0            60   \n",
       "\n",
       "       workclass_ Federal-gov  workclass_ Local-gov  workclass_ Private  \\\n",
       "30161                       0                     0                   1   \n",
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       "30164                       0                     0                   1   \n",
       "30165                       0                     0                   1   \n",
       "...                       ...                   ...                 ...   \n",
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       "45217                       0                     0                   1   \n",
       "45218                       0                     0                   1   \n",
       "45219                       0                     0                   1   \n",
       "45220                       0                     0                   0   \n",
       "\n",
       "       workclass_ Self-emp-inc  workclass_ Self-emp-not-inc  ...  \\\n",
       "30161                        0                            0  ...   \n",
       "30162                        0                            0  ...   \n",
       "30163                        0                            0  ...   \n",
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       "45218                        0                            0  ...   \n",
       "45219                        0                            0  ...   \n",
       "45220                        1                            0  ...   \n",
       "\n",
       "       native_ Portugal  native_ Puerto-Rico  native_ Scotland  native_ South  \\\n",
       "30161                 0                    0                 0              0   \n",
       "30162                 0                    0                 0              0   \n",
       "30163                 0                    0                 0              0   \n",
       "30164                 0                    0                 0              0   \n",
       "30165                 0                    0                 0              0   \n",
       "...                 ...                  ...               ...            ...   \n",
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       "\n",
       "       native_ Taiwan  native_ Thailand  native_ Trinadad&Tobago  \\\n",
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       "45220               0                 0                        0   \n",
       "\n",
       "       native_ United-States  native_ Vietnam  native_ Yugoslavia  \n",
       "30161                      1                0                   0  \n",
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       "...                      ...              ...                 ...  \n",
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       "45218                      1                0                   0  \n",
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       "45220                      1                0                   0  \n",
       "\n",
       "[15060 rows x 102 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = test_set.iloc[:,:102]\n",
    "x_test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1ea92cb7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.243245Z",
     "start_time": "2022-11-10T14:58:59.227224Z"
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   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30161</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30162</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30163</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30164</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30165</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45216</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45217</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45218</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45219</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45220</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15060 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Salary\n",
       "30161       0\n",
       "30162       0\n",
       "30163       1\n",
       "30164       1\n",
       "30165       0\n",
       "...       ...\n",
       "45216       0\n",
       "45217       0\n",
       "45218       0\n",
       "45219       0\n",
       "45220       1\n",
       "\n",
       "[15060 rows x 1 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = test_set.loc[:,['Salary']]\n",
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f93f8e24",
   "metadata": {},
   "source": [
    "# Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42257be8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-09T10:34:32.655793Z",
     "start_time": "2022-11-09T10:34:32.642704Z"
    }
   },
   "source": [
    "Naïve Bayes Classifier uses the Bayes’ theorem to predict membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class. This is also known as the Maximum A Posteriori (MAP).\n",
    "\n",
    "The MAP for a hypothesis with 2 events A and B is\n",
    "\n",
    "MAP (A)\n",
    "\n",
    "= max (P (A | B))\n",
    "\n",
    "= max (P (B | A) * P (A))/P (B)\n",
    "\n",
    "= max (P (B | A) * P (A))\n",
    "\n",
    "Here, P (B) is evidence probability. It is used to normalize the result. It remains the same, So, removing it would not affect the result.\n",
    "\n",
    "Naïve Bayes Classifier assumes that all the features are unrelated to each other. Presence or absence of a feature does not influence the presence or absence of any other feature.\n",
    "\n",
    "In real world datasets, we test a hypothesis given multiple evidence on features. So, the calculations become quite complicated. To simplify the work, the feature independence approach is used to uncouple multiple evidence and treat each as an independent one."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e392f99",
   "metadata": {},
   "source": [
    "## Types of Naive Bayes algorithm\n",
    "Table of Contents\n",
    "\n",
    "There are 3 types of Naïve Bayes algorithm. The 3 types are listed below:-\n",
    "\n",
    "Gaussian Naïve Bayes\n",
    "\n",
    "Multinomial Naïve Bayes\n",
    "\n",
    "Bernoulli Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "40537e60",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.291879Z",
     "start_time": "2022-11-10T14:58:59.243245Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB \n",
    "from sklearn.naive_bayes import MultinomialNB \n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "\n",
    "from sklearn.metrics import accuracy_score \n",
    "from sklearn.metrics import classification_report as report "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41989cd1",
   "metadata": {},
   "source": [
    "### Gaussian Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "db8b18ca",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.412576Z",
     "start_time": "2022-11-10T14:58:59.291879Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB Model accuracy score: 0.8029\n"
     ]
    }
   ],
   "source": [
    "#instantiating the object\n",
    "model_GNB = GaussianNB()\n",
    "\n",
    "#fit the model\n",
    "model_GNB.fit(x_train, y_train)\n",
    "\n",
    "#prediction\n",
    "y_pred_GNB = model_GNB.predict(x_test)\n",
    "\n",
    "#Accuracy\n",
    "#first argument is true values, second argument is predicted values\n",
    "print('GaussianNB Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_GNB)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "678b37e5",
   "metadata": {},
   "source": [
    "### Multinomial Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "87f900dc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.461255Z",
     "start_time": "2022-11-10T14:58:59.412576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultinomialNB Model accuracy score: 0.7750\n"
     ]
    }
   ],
   "source": [
    "#instantiating the object\n",
    "model_MNB = MultinomialNB()\n",
    "\n",
    "#fit the model\n",
    "model_MNB.fit(x_train, y_train)\n",
    "\n",
    "#prediction\n",
    "y_pred_MNB = model_MNB.predict(x_test)\n",
    "\n",
    "#Accuracy\n",
    "#first argument is true values, second argument is predicted values\n",
    "print('MultinomialNB Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_MNB)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c488a57",
   "metadata": {},
   "source": [
    "### Bernoulli Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c9894575",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.559086Z",
     "start_time": "2022-11-10T14:58:59.461255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BernoulliNB Model accuracy score: 0.7598\n"
     ]
    }
   ],
   "source": [
    "#instantiating the object\n",
    "model_BNB = BernoulliNB()\n",
    "\n",
    "#fit the model\n",
    "model_BNB.fit(x_train, y_train)\n",
    "\n",
    "#prediction\n",
    "y_pred_BNB = model_BNB.predict(x_test)\n",
    "\n",
    "#Accuracy\n",
    "#first argument is true values, second argument is predicted values\n",
    "print('BernoulliNB Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_BNB)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbea060",
   "metadata": {},
   "source": [
    "### Categorical Naïve Bayes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "569b2577",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.712600Z",
     "start_time": "2022-11-10T14:58:59.559086Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical Model accuracy score: 0.8297\n"
     ]
    }
   ],
   "source": [
    "#instantiating the object\n",
    "model_CNB = CategoricalNB()\n",
    "\n",
    "#fit the model\n",
    "model_CNB.fit(x_train, y_train)\n",
    "\n",
    "#prediction\n",
    "y_pred_CNB = model_CNB.predict(x_test)\n",
    "\n",
    "#Accuracy\n",
    "#first argument is true values, second argument is predicted values\n",
    "print('Categorical Model accuracy score: {0:0.4f}'. format(accuracy_score(y_test, y_pred_CNB)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "87f6a792",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.793611Z",
     "start_time": "2022-11-10T14:58:59.712600Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GaussianNB Model \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.80      0.86     11360\n",
      "           1       0.57      0.82      0.67      3700\n",
      "\n",
      "    accuracy                           0.80     15060\n",
      "   macro avg       0.75      0.81      0.76     15060\n",
      "weighted avg       0.84      0.80      0.81     15060\n",
      "\n",
      "MultinomialNB Model \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.96      0.87     11360\n",
      "           1       0.62      0.21      0.32      3700\n",
      "\n",
      "    accuracy                           0.77     15060\n",
      "   macro avg       0.71      0.58      0.59     15060\n",
      "weighted avg       0.75      0.77      0.73     15060\n",
      "\n",
      "BernoulliNB Model \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.75      0.82     11360\n",
      "           1       0.51      0.79      0.62      3700\n",
      "\n",
      "    accuracy                           0.76     15060\n",
      "   macro avg       0.71      0.77      0.72     15060\n",
      "weighted avg       0.82      0.76      0.77     15060\n",
      "\n",
      "CategoricalNB Model \n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.86      0.88     11360\n",
      "           1       0.63      0.74      0.68      3700\n",
      "\n",
      "    accuracy                           0.83     15060\n",
      "   macro avg       0.77      0.80      0.78     15060\n",
      "weighted avg       0.84      0.83      0.83     15060\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print the classification report\n",
    "\n",
    "print('GaussianNB Model','\\n', report(y_test, y_pred_GNB))\n",
    "print('MultinomialNB Model','\\n',report(y_test, y_pred_MNB))\n",
    "print('BernoulliNB Model','\\n',report(y_test, y_pred_BNB))\n",
    "print('CategoricalNB Model','\\n',report(y_test, y_pred_CNB))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "152f896b",
   "metadata": {},
   "source": [
    "Categorical Naive Bayes scored better accuracy, F1 score, precision and Receall for both Binary outputs. Hence, we are going to continue using the Categorical Model for further Metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "438c702c",
   "metadata": {},
   "source": [
    "## Confusion matrix "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8697835f",
   "metadata": {},
   "source": [
    "A confusion matrix is a tool for summarizing the performance of a classification algorithm.\n",
    "\n",
    "Four types of outcomes are possible while evaluating a classification model performance. These four outcomes are described below:-\n",
    "\n",
    "True Positives (TP) – True Positives occur when we predict an observation belongs to a certain class and the observation actually belongs to that class.\n",
    "\n",
    "True Negatives (TN) – True Negatives occur when we predict an observation does not belong to a certain class and the observation actually does not belong to that class.\n",
    "\n",
    "False Positives (FP) – False Positives occur when we predict an observation belongs to a certain class but the observation actually does not belong to that class. This type of error is called Type I error.\n",
    "\n",
    "False Negatives (FN) – False Negatives occur when we predict an observation does not belong to a certain class but the observation actually belongs to that class. This is a very serious error and it is called Type II error. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "0a8be366",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.841921Z",
     "start_time": "2022-11-10T14:58:59.793611Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9750 1610]\n",
      " [ 954 2746]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix as cm \n",
    "\n",
    "y_pred_CNB = model_CNB.predict(x_test)\n",
    "print(cm(y_test,y_pred_CNB)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2cb68633",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:58:59.979530Z",
     "start_time": "2022-11-10T14:58:59.841921Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_df=cm(y_test, y_pred_CNB)\n",
    "class_label = [\"No\", \"Yes\"]\n",
    "\n",
    "df_cm = pd.DataFrame(cm_df, index = class_label, columns = class_label)\n",
    "sns.heatmap(df_cm, annot = True, fmt = \"d\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"Actual Label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9550d81b",
   "metadata": {},
   "source": [
    "## ROC AUC Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb25d5b",
   "metadata": {},
   "source": [
    " ROC Curve stands for Receiver Operating Characteristic Curve. An ROC Curve is a plot which shows the performance of a classification model at various classification threshold levels.\n",
    "\n",
    "The ROC Curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold levels.\n",
    "\n",
    "True Positive Rate (TPR) is also called Recall. It is defined as the ratio of TP to (TP + FN).\n",
    "\n",
    "False Positive Rate (FPR) is defined as the ratio of FP to (FP + TN).\n",
    "\n",
    "In the ROC Curve, we will focus on the TPR (True Positive Rate) and FPR (False Positive Rate) of a single point. This will give us the general performance of the ROC curve which consists of the TPR and FPR at various threshold levels. So, an ROC Curve plots TPR vs FPR at different classification threshold levels. If we lower the threshold levels, it may result in more items being classified as positve. It will increase both True Positives (TP) and False Positives (FP). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8e1bb677",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:59:00.093138Z",
     "start_time": "2022-11-10T14:58:59.979530Z"
    }
   },
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC-ROC Score: 0.8002184050247431\n"
     ]
    }
   ],
   "source": [
    "# ROC Curve plotting and finding AUC value\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, model_CNB.predict_proba (x_test)[:,1])\n",
    "auc = roc_auc_score(y_test, y_pred_CNB)\n",
    "\n",
    "plt.plot(fpr, tpr, color='red', label='logit model ( area  = %0.2f)' %auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "\n",
    "plt.title('ROC curve for Subscription classifier')\n",
    "plt.xlabel('False Positive Rate or [1 - True Negative Rate]')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()\n",
    "\n",
    "print('AUC-ROC Score:',auc) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "977f2cf1",
   "metadata": {},
   "source": [
    "ROC AUC is a single number summary of classifier performance. The higher the value, the better the classifier. A perfect classifier will have a ROC AUC equal to 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189d10b0",
   "metadata": {},
   "source": [
    "## Check for overfitting and underfitting "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "19e43cbe",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-10T14:59:00.181359Z",
     "start_time": "2022-11-10T14:59:00.093138Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 83.09%\n",
      "Test set score: 82.97%\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.2f}%'.format(model_CNB.score(x_train, y_train)*100))\n",
    "\n",
    "print('Test set score: {:.2f}%'.format(model_CNB.score(x_test, y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e85c170",
   "metadata": {},
   "source": [
    "These two values are quite comparable. So, there is no sign of overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7371617",
   "metadata": {},
   "source": [
    "# Thank You "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}