Income_Binary_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the dataset\n",
    "dataset = pd.read_csv('adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>?</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</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>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>18</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>?</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>?</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</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>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90         ?   77053       HS-grad              9        Widowed   \n",
       "1   82   Private  132870       HS-grad              9        Widowed   \n",
       "2   66         ?  186061  Some-college             10        Widowed   \n",
       "3   54   Private  140359       7th-8th              4       Divorced   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0                  ?  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2                  ?      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week native.country income  \n",
       "0          4356              40  United-States  <=50K  \n",
       "1          4356              18  United-States  <=50K  \n",
       "2          4356              40  United-States  <=50K  \n",
       "3          3900              40  United-States  <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education.num     0\n",
       "marital.status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital.gain      0\n",
       "capital.loss      0\n",
       "hours.per.week    0\n",
       "native.country    0\n",
       "income            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for null values, if any\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing question marks in dataset with null values\n",
    "\n",
    "df = dataset.replace('?',np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0.00\n",
       "workclass         5.64\n",
       "fnlwgt            0.00\n",
       "education         0.00\n",
       "education.num     0.00\n",
       "marital.status    0.00\n",
       "occupation        5.66\n",
       "relationship      0.00\n",
       "race              0.00\n",
       "sex               0.00\n",
       "capital.gain      0.00\n",
       "capital.loss      0.00\n",
       "hours.per.week    0.00\n",
       "native.country    1.79\n",
       "income            0.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of Missing Data from each column\n",
    "# no.of nulls / total no.of rows * 100 rounded to 2 decimals\n",
    "round(df.isnull().sum()/df.shape[0]*100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count              30718\n",
       "unique                14\n",
       "top       Prof-specialty\n",
       "freq                4140\n",
       "Name: occupation, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Since a very small amount of data is missing, we can replace the null values with the mode of each column\n",
    "\n",
    "df['occupation'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since mode is Prof-specialty, replacing null values with it\n",
    "\n",
    "df['occupation'] = df['occupation'].fillna('Prof-specialty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       30725\n",
       "unique          8\n",
       "top       Private\n",
       "freq        22696\n",
       "Name: workclass, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Observing workclass column\n",
    "\n",
    "df['workclass'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since mode is `Private`, replacing null values with it\n",
    "\n",
    "df['workclass'] = df['workclass'].fillna('Private')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count             31978\n",
       "unique               41\n",
       "top       United-States\n",
       "freq              29170\n",
       "Name: native.country, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['native.country'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Since mode is United-States, replacing null values with it\n",
    "\n",
    "df['native.country'] = df['native.country'].fillna('United-States')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age               0\n",
       "workclass         0\n",
       "fnlwgt            0\n",
       "education         0\n",
       "education.num     0\n",
       "marital.status    0\n",
       "occupation        0\n",
       "relationship      0\n",
       "race              0\n",
       "sex               0\n",
       "capital.gain      0\n",
       "capital.loss      0\n",
       "hours.per.week    0\n",
       "native.country    0\n",
       "income            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": [
    "color = plt.get_cmap('RdYlGn')   # default color\n",
    "color.set_bad('lightblue')\n",
    "sns.heatmap(df.corr(), annot=True, cmap=color); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.256100e+04\n",
       "mean     1.897784e+05\n",
       "std      1.055500e+05\n",
       "min      1.228500e+04\n",
       "25%      1.178270e+05\n",
       "50%      1.783560e+05\n",
       "75%      2.370510e+05\n",
       "max      1.484705e+06\n",
       "Name: fnlwgt, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fnlwgt'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.drop(['fnlwgt'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4232</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>4233</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>4234</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>4235</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4237</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</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>32558</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>32559</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>24424 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass     education  education.num      marital.status  \\\n",
       "4232    38           Private       HS-grad              9            Divorced   \n",
       "4233    53           Private          11th              7  Married-civ-spouse   \n",
       "4234    28           Private     Bachelors             13  Married-civ-spouse   \n",
       "4235    37           Private       Masters             14  Married-civ-spouse   \n",
       "4237    52  Self-emp-not-inc       HS-grad              9  Married-civ-spouse   \n",
       "...    ...               ...           ...            ...                 ...   \n",
       "32555   53           Private       Masters             14  Married-civ-spouse   \n",
       "32556   22           Private  Some-college             10       Never-married   \n",
       "32557   27           Private    Assoc-acdm             12  Married-civ-spouse   \n",
       "32558   40           Private       HS-grad              9  Married-civ-spouse   \n",
       "32559   58           Private       HS-grad              9             Widowed   \n",
       "\n",
       "              occupation   relationship   race     sex  capital.gain  \\\n",
       "4232   Handlers-cleaners  Not-in-family  White    Male             0   \n",
       "4233   Handlers-cleaners        Husband  Black    Male             0   \n",
       "4234      Prof-specialty           Wife  Black  Female             0   \n",
       "4235     Exec-managerial           Wife  White  Female             0   \n",
       "4237     Exec-managerial        Husband  White    Male             0   \n",
       "...                  ...            ...    ...     ...           ...   \n",
       "32555    Exec-managerial        Husband  White    Male             0   \n",
       "32556    Protective-serv  Not-in-family  White    Male             0   \n",
       "32557       Tech-support           Wife  White  Female             0   \n",
       "32558  Machine-op-inspct        Husband  White    Male             0   \n",
       "32559       Adm-clerical      Unmarried  White  Female             0   \n",
       "\n",
       "       capital.loss  hours.per.week native.country income  \n",
       "4232              0              40  United-States  <=50K  \n",
       "4233              0              40  United-States  <=50K  \n",
       "4234              0              40           Cuba  <=50K  \n",
       "4235              0              40  United-States  <=50K  \n",
       "4237              0              45  United-States   >50K  \n",
       "...             ...             ...            ...    ...  \n",
       "32555             0              40  United-States   >50K  \n",
       "32556             0              40  United-States  <=50K  \n",
       "32557             0              38  United-States  <=50K  \n",
       "32558             0              40  United-States   >50K  \n",
       "32559             0              40  United-States  <=50K  \n",
       "\n",
       "[24424 rows x 14 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q1 = df2.quantile(0.25)\n",
    "Q3 = df2.quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "cols = ['capital.gain','age','capital.loss','hours.per.week']\n",
    "df2 = df2[~((df2[cols] < (Q1 - 3 * IQR)) |(df2[cols] > (Q3 + 3 * IQR))).any(axis=1)]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24424.0\n",
       "mean         0.0\n",
       "std          0.0\n",
       "min          0.0\n",
       "25%          0.0\n",
       "50%          0.0\n",
       "75%          0.0\n",
       "max          0.0\n",
       "Name: capital.gain, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['capital.gain'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24424.0\n",
       "mean         0.0\n",
       "std          0.0\n",
       "min          0.0\n",
       "25%          0.0\n",
       "50%          0.0\n",
       "75%          0.0\n",
       "max          0.0\n",
       "Name: capital.loss, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['capital.loss'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24424.000000\n",
       "mean        41.632042\n",
       "std          7.065386\n",
       "min         25.000000\n",
       "25%         40.000000\n",
       "50%         40.000000\n",
       "75%         45.000000\n",
       "max         60.000000\n",
       "Name: hours.per.week, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['hours.per.week'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    " # Dropping capital.loss, capital.gain as all values are 0 after removing outliers\n",
    "\n",
    "df2 = df2.drop(['capital.loss', 'capital.gain'], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United-States                 90.939240\n",
       "Mexico                         2.292827\n",
       "Philippines                    0.626433\n",
       "Germany                        0.421716\n",
       "Puerto-Rico                    0.376679\n",
       "El-Salvador                    0.348018\n",
       "Canada                         0.348018\n",
       "Cuba                           0.302981\n",
       "Jamaica                        0.290698\n",
       "India                          0.274320\n",
       "England                        0.253849\n",
       "Dominican-Republic             0.253849\n",
       "Guatemala                      0.241566\n",
       "China                          0.225188\n",
       "Italy                          0.225188\n",
       "Columbia                       0.212905\n",
       "South                          0.200622\n",
       "Vietnam                        0.196528\n",
       "Japan                          0.180151\n",
       "Poland                         0.180151\n",
       "Portugal                       0.143302\n",
       "Taiwan                         0.139207\n",
       "Haiti                          0.135113\n",
       "Iran                           0.131019\n",
       "Nicaragua                      0.114641\n",
       "Peru                           0.094170\n",
       "Ecuador                        0.094170\n",
       "France                         0.090075\n",
       "Ireland                        0.085981\n",
       "Hong                           0.073698\n",
       "Laos                           0.069604\n",
       "Greece                         0.065509\n",
       "Cambodia                       0.057321\n",
       "Trinadad&Tobago                0.053226\n",
       "Outlying-US(Guam-USVI-etc)     0.053226\n",
       "Thailand                       0.049132\n",
       "Yugoslavia                     0.049132\n",
       "Honduras                       0.040943\n",
       "Scotland                       0.040943\n",
       "Hungary                        0.028660\n",
       "Name: native.country, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df2['native.country'].value_counts(normalize=True)*100\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Majority of the values in native.country seem to be USA i.e., 90.9%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.drop(['native.country'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>education</th>\n",
       "      <th>10th</th>\n",
       "      <th>11th</th>\n",
       "      <th>12th</th>\n",
       "      <th>1st-4th</th>\n",
       "      <th>5th-6th</th>\n",
       "      <th>7th-8th</th>\n",
       "      <th>9th</th>\n",
       "      <th>Assoc-acdm</th>\n",
       "      <th>Assoc-voc</th>\n",
       "      <th>Bachelors</th>\n",
       "      <th>Doctorate</th>\n",
       "      <th>HS-grad</th>\n",
       "      <th>Masters</th>\n",
       "      <th>Preschool</th>\n",
       "      <th>Prof-school</th>\n",
       "      <th>Some-college</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education.num</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>273</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>485</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>410</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>683</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>751</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1092</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>820</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3934</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "education      10th  11th  12th  1st-4th  5th-6th  7th-8th  9th  Assoc-acdm  \\\n",
       "education.num                                                                 \n",
       "1                 0     0     0        0        0        0    0           0   \n",
       "2                 0     0     0      131        0        0    0           0   \n",
       "3                 0     0     0        0      273        0    0           0   \n",
       "4                 0     0     0        0        0      485    0           0   \n",
       "5                 0     0     0        0        0        0  410           0   \n",
       "6               683     0     0        0        0        0    0           0   \n",
       "7                 0   751     0        0        0        0    0           0   \n",
       "8                 0     0   311        0        0        0    0           0   \n",
       "9                 0     0     0        0        0        0    0           0   \n",
       "10                0     0     0        0        0        0    0           0   \n",
       "11                0     0     0        0        0        0    0           0   \n",
       "12                0     0     0        0        0        0    0         820   \n",
       "13                0     0     0        0        0        0    0           0   \n",
       "14                0     0     0        0        0        0    0           0   \n",
       "15                0     0     0        0        0        0    0           0   \n",
       "16                0     0     0        0        0        0    0           0   \n",
       "\n",
       "education      Assoc-voc  Bachelors  Doctorate  HS-grad  Masters  Preschool  \\\n",
       "education.num                                                                 \n",
       "1                      0          0          0        0        0         38   \n",
       "2                      0          0          0        0        0          0   \n",
       "3                      0          0          0        0        0          0   \n",
       "4                      0          0          0        0        0          0   \n",
       "5                      0          0          0        0        0          0   \n",
       "6                      0          0          0        0        0          0   \n",
       "7                      0          0          0        0        0          0   \n",
       "8                      0          0          0        0        0          0   \n",
       "9                      0          0          0     8400        0          0   \n",
       "10                     0          0          0        0        0          0   \n",
       "11                  1092          0          0        0        0          0   \n",
       "12                     0          0          0        0        0          0   \n",
       "13                     0       3934          0        0        0          0   \n",
       "14                     0          0          0        0     1151          0   \n",
       "15                     0          0          0        0        0          0   \n",
       "16                     0          0        238        0        0          0   \n",
       "\n",
       "education      Prof-school  Some-college  \n",
       "education.num                             \n",
       "1                        0             0  \n",
       "2                        0             0  \n",
       "3                        0             0  \n",
       "4                        0             0  \n",
       "5                        0             0  \n",
       "6                        0             0  \n",
       "7                        0             0  \n",
       "8                        0             0  \n",
       "9                        0             0  \n",
       "10                       0          5402  \n",
       "11                       0             0  \n",
       "12                       0             0  \n",
       "13                       0             0  \n",
       "14                       0             0  \n",
       "15                     305             0  \n",
       "16                       0             0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for correlation between columns 'education' and 'education-num'\n",
    "\n",
    "pd.crosstab(df2['education.num'],df2['education'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "We can clearly see that categorical feature 'education' can perfectly be described numeric feature 'education.num'. Hence, we can drop one column.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.drop(['education'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4232</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4233</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4234</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4235</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4237</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>45</td>\n",
       "      <td>&gt;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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</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>38</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</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>40</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24424 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age         workclass  education.num      marital.status  \\\n",
       "4232    38           Private              9            Divorced   \n",
       "4233    53           Private              7  Married-civ-spouse   \n",
       "4234    28           Private             13  Married-civ-spouse   \n",
       "4235    37           Private             14  Married-civ-spouse   \n",
       "4237    52  Self-emp-not-inc              9  Married-civ-spouse   \n",
       "...    ...               ...            ...                 ...   \n",
       "32555   53           Private             14  Married-civ-spouse   \n",
       "32556   22           Private             10       Never-married   \n",
       "32557   27           Private             12  Married-civ-spouse   \n",
       "32558   40           Private              9  Married-civ-spouse   \n",
       "32559   58           Private              9             Widowed   \n",
       "\n",
       "              occupation   relationship   race     sex  hours.per.week income  \n",
       "4232   Handlers-cleaners  Not-in-family  White    Male              40  <=50K  \n",
       "4233   Handlers-cleaners        Husband  Black    Male              40  <=50K  \n",
       "4234      Prof-specialty           Wife  Black  Female              40  <=50K  \n",
       "4235     Exec-managerial           Wife  White  Female              40  <=50K  \n",
       "4237     Exec-managerial        Husband  White    Male              45   >50K  \n",
       "...                  ...            ...    ...     ...             ...    ...  \n",
       "32555    Exec-managerial        Husband  White    Male              40   >50K  \n",
       "32556    Protective-serv  Not-in-family  White    Male              40  <=50K  \n",
       "32557       Tech-support           Wife  White  Female              38  <=50K  \n",
       "32558  Machine-op-inspct        Husband  White    Male              40   >50K  \n",
       "32559       Adm-clerical      Unmarried  White  Female              40  <=50K  \n",
       "\n",
       "[24424 rows x 10 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": [
    "sns.heatmap(df2.corr(), annot=True); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data set into features and outcome\n",
    "\n",
    "X = df2.drop(['income'], axis=1)\n",
    "Y = df2['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map income to 0 for <= 50k and map to 1 if income > 50k\n",
    "Y = Y.map({'<=50K':0, '>50K':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting the data into test data and training data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17709</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25626</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</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>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32383</th>\n",
       "      <td>55</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28209</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19423</th>\n",
       "      <td>47</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>4</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27012</th>\n",
       "      <td>59</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>30</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>63</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17096 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  education.num      marital.status       occupation  \\\n",
       "17709   20       Private              9       Never-married   Prof-specialty   \n",
       "25626   29       Private             10  Married-civ-spouse  Exec-managerial   \n",
       "32383   55     Local-gov             14       Never-married   Prof-specialty   \n",
       "28209   19       Private              7       Never-married    Other-service   \n",
       "7978    25       Private             13       Never-married     Craft-repair   \n",
       "...    ...           ...            ...                 ...              ...   \n",
       "19423   47  Self-emp-inc              4       Never-married     Craft-repair   \n",
       "27012   59  Self-emp-inc             11            Divorced   Prof-specialty   \n",
       "15609   51       Private             11            Divorced     Adm-clerical   \n",
       "16718   30   Federal-gov             13       Never-married  Protective-serv   \n",
       "7360    63       Private             13       Never-married            Sales   \n",
       "\n",
       "        relationship   race     sex  hours.per.week  \n",
       "17709      Own-child  White  Female              40  \n",
       "25626        Husband  White    Male              40  \n",
       "32383  Not-in-family  White  Female              45  \n",
       "28209  Not-in-family  Black    Male              40  \n",
       "7978   Not-in-family  White    Male              40  \n",
       "...              ...    ...     ...             ...  \n",
       "19423  Not-in-family  Other    Male              40  \n",
       "27012  Not-in-family  White    Male              50  \n",
       "15609      Unmarried  White  Female              38  \n",
       "16718  Not-in-family  White  Female              60  \n",
       "7360   Not-in-family  White    Male              27  \n",
       "\n",
       "[17096 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>education.num</th>\n",
       "      <th>hours.per.week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17709</th>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25626</th>\n",
       "      <td>29</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32383</th>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28209</th>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19423</th>\n",
       "      <td>47</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27012</th>\n",
       "      <td>59</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>51</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>63</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17096 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age  education.num  hours.per.week\n",
       "17709   20              9              40\n",
       "25626   29             10              40\n",
       "32383   55             14              45\n",
       "28209   19              7              40\n",
       "7978    25             13              40\n",
       "...    ...            ...             ...\n",
       "19423   47              4              40\n",
       "27012   59             11              50\n",
       "15609   51             11              38\n",
       "16718   30             13              60\n",
       "7360    63             13              27\n",
       "\n",
       "[17096 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_cols = X_train.select_dtypes(np.number).columns\n",
    "X_train_num = X_train.loc[:,num_cols]\n",
    "X_train_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "# Standardizing the features \n",
    "\n",
    "normalizer = preprocessing.Normalizer()\n",
    "X_train_num = normalizer.fit_transform(X_train_num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_num = X_test.loc[:,num_cols]\n",
    "X_test_num = normalizer.transform(X_test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA()\n",
    "\n",
    "principalComponents = pca.fit(X_train_num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PC_values = np.arange(pca.n_components_) + 1\n",
    "plt.plot(PC_values, pca.explained_variance_ratio_ *100, 'ro-', linewidth=2)\n",
    "plt.title('Scree Plot')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.ylabel('Proportion of Variance Explained')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91336956, 0.07753828, 0.00909215])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.91336956, 0.07753828])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca99 = PCA(.99)\n",
    "pca99.fit(X_train_num)\n",
    "pca99.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_num_pca = pca99.transform(X_train_num)\n",
    "X_test_num_pca = pca99.transform(X_test_num) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17096, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_num_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7328, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_num_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['workclass', 'marital.status', 'occupation', 'relationship', 'race',\n",
       "       'sex'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encoding categorical features\n",
    "\n",
    "categorical_columns = X_train.select_dtypes(exclude=np.number).columns\n",
    "categorical_columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cat = X_train.loc[:,categorical_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
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       "  <tbody>\n",
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       "      <th>17709</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>25626</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>10</td>\n",
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       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>32383</th>\n",
       "      <td>55</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>28209</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7978</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
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       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>19423</th>\n",
       "      <td>47</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>4</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Other</td>\n",
       "      <td>Male</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27012</th>\n",
       "      <td>59</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15609</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>11</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16718</th>\n",
       "      <td>30</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7360</th>\n",
       "      <td>63</td>\n",
       "      <td>Private</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17096 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age     workclass  education.num      marital.status       occupation  \\\n",
       "17709   20       Private              9       Never-married   Prof-specialty   \n",
       "25626   29       Private             10  Married-civ-spouse  Exec-managerial   \n",
       "32383   55     Local-gov             14       Never-married   Prof-specialty   \n",
       "28209   19       Private              7       Never-married    Other-service   \n",
       "7978    25       Private             13       Never-married     Craft-repair   \n",
       "...    ...           ...            ...                 ...              ...   \n",
       "19423   47  Self-emp-inc              4       Never-married     Craft-repair   \n",
       "27012   59  Self-emp-inc             11            Divorced   Prof-specialty   \n",
       "15609   51       Private             11            Divorced     Adm-clerical   \n",
       "16718   30   Federal-gov             13       Never-married  Protective-serv   \n",
       "7360    63       Private             13       Never-married            Sales   \n",
       "\n",
       "        relationship   race     sex  hours.per.week  \n",
       "17709      Own-child  White  Female              40  \n",
       "25626        Husband  White    Male              40  \n",
       "32383  Not-in-family  White  Female              45  \n",
       "28209  Not-in-family  Black    Male              40  \n",
       "7978   Not-in-family  White    Male              40  \n",
       "...              ...    ...     ...             ...  \n",
       "19423  Not-in-family  Other    Male              40  \n",
       "27012  Not-in-family  White    Male              50  \n",
       "15609      Unmarried  White  Female              38  \n",
       "16718  Not-in-family  White  Female              60  \n",
       "7360   Not-in-family  White    Male              27  \n",
       "\n",
       "[17096 rows x 9 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "      <td>White</td>\n",
       "      <td>Female</td>\n",
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       "    <tr>\n",
       "      <th>24853</th>\n",
       "      <td>Private</td>\n",
       "      <td>Divorced</td>\n",
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       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25965</th>\n",
       "      <td>Private</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>7328 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              workclass      marital.status       occupation   relationship  \\\n",
       "27824           Private       Never-married   Prof-specialty  Not-in-family   \n",
       "21723  Self-emp-not-inc  Married-civ-spouse     Craft-repair        Husband   \n",
       "21708           Private           Separated            Sales  Not-in-family   \n",
       "18574           Private       Never-married   Prof-specialty  Not-in-family   \n",
       "25397  Self-emp-not-inc  Married-civ-spouse  Exec-managerial           Wife   \n",
       "...                 ...                 ...              ...            ...   \n",
       "13340         Local-gov       Never-married     Adm-clerical      Own-child   \n",
       "18125         Local-gov  Married-civ-spouse   Prof-specialty        Husband   \n",
       "20748           Private             Widowed     Adm-clerical  Not-in-family   \n",
       "24853           Private            Divorced            Sales      Unmarried   \n",
       "25965           Private  Married-civ-spouse     Adm-clerical           Wife   \n",
       "\n",
       "        race     sex  \n",
       "27824  White    Male  \n",
       "21723  White    Male  \n",
       "21708  White  Female  \n",
       "18574  White    Male  \n",
       "25397  White  Female  \n",
       "...      ...     ...  \n",
       "13340  White  Female  \n",
       "18125  White    Male  \n",
       "20748  White  Female  \n",
       "24853  White    Male  \n",
       "25965  White  Female  \n",
       "\n",
       "[7328 rows x 6 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_cat = X_test.loc[:,categorical_columns]\n",
    "X_test_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
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       "      <th>19423</th>\n",
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       "      <th>27012</th>\n",
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       "    <tr>\n",
       "      <th>15609</th>\n",
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       "    <tr>\n",
       "      <th>16718</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>7360</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17096 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "17709                    0                       0                  1   \n",
       "25626                    0                       0                  1   \n",
       "32383                    1                       0                  0   \n",
       "28209                    0                       0                  1   \n",
       "7978                     0                       0                  1   \n",
       "...                    ...                     ...                ...   \n",
       "19423                    0                       0                  0   \n",
       "27012                    0                       0                  0   \n",
       "15609                    0                       0                  1   \n",
       "16718                    0                       0                  0   \n",
       "7360                     0                       0                  1   \n",
       "\n",
       "       workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "17709                       0                           0   \n",
       "25626                       0                           0   \n",
       "32383                       0                           0   \n",
       "28209                       0                           0   \n",
       "7978                        0                           0   \n",
       "...                       ...                         ...   \n",
       "19423                       1                           0   \n",
       "27012                       1                           0   \n",
       "15609                       0                           0   \n",
       "16718                       0                           0   \n",
       "7360                        0                           0   \n",
       "\n",
       "       workclass_State-gov  workclass_Without-pay  \\\n",
       "17709                    0                      0   \n",
       "25626                    0                      0   \n",
       "32383                    0                      0   \n",
       "28209                    0                      0   \n",
       "7978                     0                      0   \n",
       "...                    ...                    ...   \n",
       "19423                    0                      0   \n",
       "27012                    0                      0   \n",
       "15609                    0                      0   \n",
       "16718                    0                      0   \n",
       "7360                     0                      0   \n",
       "\n",
       "       marital.status_Married-AF-spouse  marital.status_Married-civ-spouse  \\\n",
       "17709                                 0                                  0   \n",
       "25626                                 0                                  1   \n",
       "32383                                 0                                  0   \n",
       "28209                                 0                                  0   \n",
       "7978                                  0                                  0   \n",
       "...                                 ...                                ...   \n",
       "19423                                 0                                  0   \n",
       "27012                                 0                                  0   \n",
       "15609                                 0                                  0   \n",
       "16718                                 0                                  0   \n",
       "7360                                  0                                  0   \n",
       "\n",
       "       marital.status_Married-spouse-absent  ...  relationship_Not-in-family  \\\n",
       "17709                                     0  ...                           0   \n",
       "25626                                     0  ...                           0   \n",
       "32383                                     0  ...                           1   \n",
       "28209                                     0  ...                           1   \n",
       "7978                                      0  ...                           1   \n",
       "...                                     ...  ...                         ...   \n",
       "19423                                     0  ...                           1   \n",
       "27012                                     0  ...                           1   \n",
       "15609                                     0  ...                           0   \n",
       "16718                                     0  ...                           1   \n",
       "7360                                      0  ...                           1   \n",
       "\n",
       "       relationship_Other-relative  relationship_Own-child  \\\n",
       "17709                            0                       1   \n",
       "25626                            0                       0   \n",
       "32383                            0                       0   \n",
       "28209                            0                       0   \n",
       "7978                             0                       0   \n",
       "...                            ...                     ...   \n",
       "19423                            0                       0   \n",
       "27012                            0                       0   \n",
       "15609                            0                       0   \n",
       "16718                            0                       0   \n",
       "7360                             0                       0   \n",
       "\n",
       "       relationship_Unmarried  relationship_Wife  race_Asian-Pac-Islander  \\\n",
       "17709                       0                  0                        0   \n",
       "25626                       0                  0                        0   \n",
       "32383                       0                  0                        0   \n",
       "28209                       0                  0                        0   \n",
       "7978                        0                  0                        0   \n",
       "...                       ...                ...                      ...   \n",
       "19423                       0                  0                        0   \n",
       "27012                       0                  0                        0   \n",
       "15609                       1                  0                        0   \n",
       "16718                       0                  0                        0   \n",
       "7360                        0                  0                        0   \n",
       "\n",
       "       race_Black  race_Other  race_White  sex_Male  \n",
       "17709           0           0           1         0  \n",
       "25626           0           0           1         1  \n",
       "32383           0           0           1         0  \n",
       "28209           1           0           0         1  \n",
       "7978            0           0           1         1  \n",
       "...           ...         ...         ...       ...  \n",
       "19423           0           1           0         1  \n",
       "27012           0           0           1         1  \n",
       "15609           0           0           1         0  \n",
       "16718           0           0           1         0  \n",
       "7360            0           0           1         1  \n",
       "\n",
       "[17096 rows x 36 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final_data = pd.get_dummies(data=X_train_cat, prefix=categorical_columns, drop_first=True)\n",
    "X_train_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>7328 rows × 36 columns</p>\n",
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      ],
      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "27824                    0                       0                  1   \n",
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       "\n",
       "       workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "27824                       0                           0   \n",
       "21723                       0                           1   \n",
       "21708                       0                           0   \n",
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       "       workclass_State-gov  workclass_Without-pay  \\\n",
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       "\n",
       "       marital.status_Married-AF-spouse  marital.status_Married-civ-spouse  \\\n",
       "27824                                 0                                  0   \n",
       "21723                                 0                                  1   \n",
       "21708                                 0                                  0   \n",
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       "24853                                 0                                  0   \n",
       "25965                                 0                                  1   \n",
       "\n",
       "       marital.status_Married-spouse-absent  ...  relationship_Not-in-family  \\\n",
       "27824                                     0  ...                           1   \n",
       "21723                                     0  ...                           0   \n",
       "21708                                     0  ...                           1   \n",
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       "20748                                     0  ...                           1   \n",
       "24853                                     0  ...                           0   \n",
       "25965                                     0  ...                           0   \n",
       "\n",
       "       relationship_Other-relative  relationship_Own-child  \\\n",
       "27824                            0                       0   \n",
       "21723                            0                       0   \n",
       "21708                            0                       0   \n",
       "18574                            0                       0   \n",
       "25397                            0                       0   \n",
       "...                            ...                     ...   \n",
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       "18125                            0                       0   \n",
       "20748                            0                       0   \n",
       "24853                            0                       0   \n",
       "25965                            0                       0   \n",
       "\n",
       "       relationship_Unmarried  relationship_Wife  race_Asian-Pac-Islander  \\\n",
       "27824                       0                  0                        0   \n",
       "21723                       0                  0                        0   \n",
       "21708                       0                  0                        0   \n",
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       "...                       ...                ...                      ...   \n",
       "13340                       0                  0                        0   \n",
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       "       race_Black  race_Other  race_White  sex_Male  \n",
       "27824           0           0           1         1  \n",
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       "\n",
       "[7328 rows x 36 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_final_data = pd.get_dummies(data=X_test_cat, prefix=categorical_columns, drop_first=True)\n",
    "X_test_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_final_data['pc1'] = X_train_num_pca[:, 0]\n",
    "X_train_final_data['pc2'] = X_train_num_pca[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "17709                    0                       0                  1   \n",
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       "32383                    1                       0                  0   \n",
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       "...                    ...                     ...                ...   \n",
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       "\n",
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       "17709                       0                           0   \n",
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       "28209                       0                           0   \n",
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       "\n",
       "       workclass_State-gov  workclass_Without-pay  \\\n",
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       "\n",
       "       marital.status_Married-AF-spouse  marital.status_Married-civ-spouse  \\\n",
       "17709                                 0                                  0   \n",
       "25626                                 0                                  1   \n",
       "32383                                 0                                  0   \n",
       "28209                                 0                                  0   \n",
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       "16718                                 0                                  0   \n",
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       "\n",
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       "\n",
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       "[17096 rows x 38 columns]"
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     "execution_count": 52,
     "metadata": {},
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   "source": [
    "X_train_final_data"
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  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
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       "<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>workclass_Local-gov</th>\n",
       "      <th>workclass_Never-worked</th>\n",
       "      <th>workclass_Private</th>\n",
       "      <th>workclass_Self-emp-inc</th>\n",
       "      <th>workclass_Self-emp-not-inc</th>\n",
       "      <th>workclass_State-gov</th>\n",
       "      <th>workclass_Without-pay</th>\n",
       "      <th>marital.status_Married-AF-spouse</th>\n",
       "      <th>marital.status_Married-civ-spouse</th>\n",
       "      <th>marital.status_Married-spouse-absent</th>\n",
       "      <th>...</th>\n",
       "      <th>relationship_Own-child</th>\n",
       "      <th>relationship_Unmarried</th>\n",
       "      <th>relationship_Wife</th>\n",
       "      <th>race_Asian-Pac-Islander</th>\n",
       "      <th>race_Black</th>\n",
       "      <th>race_Other</th>\n",
       "      <th>race_White</th>\n",
       "      <th>sex_Male</th>\n",
       "      <th>pc1</th>\n",
       "      <th>pc2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27824</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.079834</td>\n",
       "      <td>0.027874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21723</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.393859</td>\n",
       "      <td>0.004709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21708</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.137568</td>\n",
       "      <td>0.019130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18574</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138936</td>\n",
       "      <td>0.041780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25397</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.101181</td>\n",
       "      <td>-0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13340</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.060381</td>\n",
       "      <td>-0.010745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18125</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136404</td>\n",
       "      <td>0.045054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20748</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.324517</td>\n",
       "      <td>0.109488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24853</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.097548</td>\n",
       "      <td>-0.017318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25965</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.164212</td>\n",
       "      <td>0.001824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7328 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       workclass_Local-gov  workclass_Never-worked  workclass_Private  \\\n",
       "27824                    0                       0                  1   \n",
       "21723                    0                       0                  0   \n",
       "21708                    0                       0                  1   \n",
       "18574                    0                       0                  1   \n",
       "25397                    0                       0                  0   \n",
       "...                    ...                     ...                ...   \n",
       "13340                    1                       0                  0   \n",
       "18125                    1                       0                  0   \n",
       "20748                    0                       0                  1   \n",
       "24853                    0                       0                  1   \n",
       "25965                    0                       0                  1   \n",
       "\n",
       "       workclass_Self-emp-inc  workclass_Self-emp-not-inc  \\\n",
       "27824                       0                           0   \n",
       "21723                       0                           1   \n",
       "21708                       0                           0   \n",
       "18574                       0                           0   \n",
       "25397                       0                           1   \n",
       "...                       ...                         ...   \n",
       "13340                       0                           0   \n",
       "18125                       0                           0   \n",
       "20748                       0                           0   \n",
       "24853                       0                           0   \n",
       "25965                       0                           0   \n",
       "\n",
       "       workclass_State-gov  workclass_Without-pay  \\\n",
       "27824                    0                      0   \n",
       "21723                    0                      0   \n",
       "21708                    0                      0   \n",
       "18574                    0                      0   \n",
       "25397                    0                      0   \n",
       "...                    ...                    ...   \n",
       "13340                    0                      0   \n",
       "18125                    0                      0   \n",
       "20748                    0                      0   \n",
       "24853                    0                      0   \n",
       "25965                    0                      0   \n",
       "\n",
       "       marital.status_Married-AF-spouse  marital.status_Married-civ-spouse  \\\n",
       "27824                                 0                                  0   \n",
       "21723                                 0                                  1   \n",
       "21708                                 0                                  0   \n",
       "18574                                 0                                  0   \n",
       "25397                                 0                                  1   \n",
       "...                                 ...                                ...   \n",
       "13340                                 0                                  0   \n",
       "18125                                 0                                  1   \n",
       "20748                                 0                                  0   \n",
       "24853                                 0                                  0   \n",
       "25965                                 0                                  1   \n",
       "\n",
       "       marital.status_Married-spouse-absent  ...  relationship_Own-child  \\\n",
       "27824                                     0  ...                       0   \n",
       "21723                                     0  ...                       0   \n",
       "21708                                     0  ...                       0   \n",
       "18574                                     0  ...                       0   \n",
       "25397                                     0  ...                       0   \n",
       "...                                     ...  ...                     ...   \n",
       "13340                                     0  ...                       1   \n",
       "18125                                     0  ...                       0   \n",
       "20748                                     0  ...                       0   \n",
       "24853                                     0  ...                       0   \n",
       "25965                                     0  ...                       0   \n",
       "\n",
       "       relationship_Unmarried  relationship_Wife  race_Asian-Pac-Islander  \\\n",
       "27824                       0                  0                        0   \n",
       "21723                       0                  0                        0   \n",
       "21708                       0                  0                        0   \n",
       "18574                       0                  0                        0   \n",
       "25397                       0                  1                        0   \n",
       "...                       ...                ...                      ...   \n",
       "13340                       0                  0                        0   \n",
       "18125                       0                  0                        0   \n",
       "20748                       0                  0                        0   \n",
       "24853                       1                  0                        0   \n",
       "25965                       0                  1                        0   \n",
       "\n",
       "       race_Black  race_Other  race_White  sex_Male       pc1       pc2  \n",
       "27824           0           0           1         1 -0.079834  0.027874  \n",
       "21723           0           0           1         1  0.393859  0.004709  \n",
       "21708           0           0           1         0 -0.137568  0.019130  \n",
       "18574           0           0           1         1  0.138936  0.041780  \n",
       "25397           0           0           1         0  0.101181 -0.015603  \n",
       "...           ...         ...         ...       ...       ...       ...  \n",
       "13340           0           0           1         0 -0.060381 -0.010745  \n",
       "18125           0           0           1         1  0.136404  0.045054  \n",
       "20748           0           0           1         0  0.324517  0.109488  \n",
       "24853           0           0           1         1  0.097548 -0.017318  \n",
       "25965           0           0           1         0  0.164212  0.001824  \n",
       "\n",
       "[7328 rows x 38 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_final_data['pc1'] = X_test_num_pca[:,0]\n",
    "X_test_final_data['pc2'] = X_test_num_pca[:,1]\n",
    "X_test_final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train_final_data, Y_train)\n",
    "\n",
    "\n",
    "acc_log = round(logreg.score(X_test_final_data, Y_test) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.42"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train_final_data, Y_train) \n",
    "acc_decision_tree = round(decision_tree.score(X_test_final_data, Y_test) * 100, 2)\n",
    "print(acc_decision_tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.820278384279476"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train_final_data, Y_train)\n",
    " \n",
    "\n",
    "random_forest.score(X_test_final_data, Y_test) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.41\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import  LinearSVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train_final_data, Y_train)\n",
    " \n",
    "\n",
    "acc_linear_svc = round(linear_svc.score(X_test_final_data, Y_test) * 100, 2)\n",
    "print(acc_linear_svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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