Python/ml/Income_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# INCOME CLASSIFICATION"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Link to the Dataset: [Income Evaluation](https://www.kaggle.com/overload10/adult-census-dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The aim is to classify the income of a person as <=50K or >50K."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>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>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</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",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</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>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</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",
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       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
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      "text/plain": [
       "       age          workclass   fnlwgt    education   education-num  \\\n",
       "0       39          State-gov    77516    Bachelors              13   \n",
       "1       50   Self-emp-not-inc    83311    Bachelors              13   \n",
       "2       38            Private   215646      HS-grad               9   \n",
       "3       53            Private   234721         11th               7   \n",
       "4       28            Private   338409    Bachelors              13   \n",
       "...    ...                ...      ...          ...             ...   \n",
       "32556   27            Private   257302   Assoc-acdm              12   \n",
       "32557   40            Private   154374      HS-grad               9   \n",
       "32558   58            Private   151910      HS-grad               9   \n",
       "32559   22            Private   201490      HS-grad               9   \n",
       "32560   52       Self-emp-inc   287927      HS-grad               9   \n",
       "\n",
       "            marital-status          occupation    relationship    race  \\\n",
       "0            Never-married        Adm-clerical   Not-in-family   White   \n",
       "1       Married-civ-spouse     Exec-managerial         Husband   White   \n",
       "2                 Divorced   Handlers-cleaners   Not-in-family   White   \n",
       "3       Married-civ-spouse   Handlers-cleaners         Husband   Black   \n",
       "4       Married-civ-spouse      Prof-specialty            Wife   Black   \n",
       "...                    ...                 ...             ...     ...   \n",
       "32556   Married-civ-spouse        Tech-support            Wife   White   \n",
       "32557   Married-civ-spouse   Machine-op-inspct         Husband   White   \n",
       "32558              Widowed        Adm-clerical       Unmarried   White   \n",
       "32559        Never-married        Adm-clerical       Own-child   White   \n",
       "32560   Married-civ-spouse     Exec-managerial            Wife   White   \n",
       "\n",
       "           sex   capital-gain   capital-loss   hours-per-week  native-country  \\\n",
       "0         Male           2174              0               40   United-States   \n",
       "1         Male              0              0               13   United-States   \n",
       "2         Male              0              0               40   United-States   \n",
       "3         Male              0              0               40   United-States   \n",
       "4       Female              0              0               40            Cuba   \n",
       "...        ...            ...            ...              ...             ...   \n",
       "32556   Female              0              0               38   United-States   \n",
       "32557     Male              0              0               40   United-States   \n",
       "32558   Female              0              0               40   United-States   \n",
       "32559     Male              0              0               20   United-States   \n",
       "32560   Female          15024              0               40   United-States   \n",
       "\n",
       "       income  \n",
       "0       <=50K  \n",
       "1       <=50K  \n",
       "2       <=50K  \n",
       "3       <=50K  \n",
       "4       <=50K  \n",
       "...       ...  \n",
       "32556   <=50K  \n",
       "32557    >50K  \n",
       "32558   <=50K  \n",
       "32559   <=50K  \n",
       "32560    >50K  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'income_evaluation.csv', encoding = 'unicode_escape')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                False\n",
       " workclass         False\n",
       " fnlwgt            False\n",
       " education         False\n",
       " education-num     False\n",
       " marital-status    False\n",
       " occupation        False\n",
       " relationship      False\n",
       " race              False\n",
       " sex               False\n",
       " capital-gain      False\n",
       " capital-loss      False\n",
       " hours-per-week    False\n",
       " native-country    False\n",
       " income            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any()  # checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# no null values are present\n",
    "# we need to do label encoding for columns that don't have int/float type values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   age              32561 non-null  int64 \n",
      " 1    workclass       32561 non-null  object\n",
      " 2    fnlwgt          32561 non-null  int64 \n",
      " 3    education       32561 non-null  object\n",
      " 4    education-num   32561 non-null  int64 \n",
      " 5    marital-status  32561 non-null  object\n",
      " 6    occupation      32561 non-null  object\n",
      " 7    relationship    32561 non-null  object\n",
      " 8    race            32561 non-null  object\n",
      " 9    sex             32561 non-null  object\n",
      " 10   capital-gain    32561 non-null  int64 \n",
      " 11   capital-loss    32561 non-null  int64 \n",
      " 12   hours-per-week  32561 non-null  int64 \n",
      " 13   native-country  32561 non-null  object\n",
      " 14   income          32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>257302</td>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>154374</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>151910</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>201490</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>287927</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass   fnlwgt   education   education-num   marital-status  \\\n",
       "0       39           7    77516           9              13                4   \n",
       "1       50           6    83311           9              13                2   \n",
       "2       38           4   215646          11               9                0   \n",
       "3       53           4   234721           1               7                2   \n",
       "4       28           4   338409           9              13                2   \n",
       "...    ...         ...      ...         ...             ...              ...   \n",
       "32556   27           4   257302           7              12                2   \n",
       "32557   40           4   154374          11               9                2   \n",
       "32558   58           4   151910          11               9                6   \n",
       "32559   22           4   201490          11               9                4   \n",
       "32560   52           5   287927          11               9                2   \n",
       "\n",
       "        occupation   relationship   race   sex   capital-gain   capital-loss  \\\n",
       "0                1              1      4     1           2174              0   \n",
       "1                4              0      4     1              0              0   \n",
       "2                6              1      4     1              0              0   \n",
       "3                6              0      2     1              0              0   \n",
       "4               10              5      2     0              0              0   \n",
       "...            ...            ...    ...   ...            ...            ...   \n",
       "32556           13              5      4     0              0              0   \n",
       "32557            7              0      4     1              0              0   \n",
       "32558            1              4      4     0              0              0   \n",
       "32559            1              3      4     1              0              0   \n",
       "32560            4              5      4     0          15024              0   \n",
       "\n",
       "        hours-per-week   native-country   income  \n",
       "0                   40               39        0  \n",
       "1                   13               39        0  \n",
       "2                   40               39        0  \n",
       "3                   40               39        0  \n",
       "4                   40                5        0  \n",
       "...                ...              ...      ...  \n",
       "32556               38               39        0  \n",
       "32557               40               39        1  \n",
       "32558               40               39        0  \n",
       "32559               20               39        0  \n",
       "32560               40               39        1  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking from above and label encoding columns that are required to be encoded\n",
    "a = [' workclass', ' education', ' marital-status', ' occupation',' relationship',' race',' sex',' native-country',' income']\n",
    "for i in a:\n",
    "    df[i] = df[i].astype('|S')\n",
    "    df[i] = label_encoder.fit_transform(df[i])\n",
    "df # getting final encoded dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', ' workclass', ' fnlwgt', ' education', ' education-num',\n",
       "       ' marital-status', ' occupation', ' relationship', ' race', ' sex',\n",
       "       ' capital-gain', ' capital-loss', ' hours-per-week', ' native-country',\n",
       "       ' income'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VIF</th>\n",
       "      <th>Features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.521265</td>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.475260</td>\n",
       "      <td>workclass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.031573</td>\n",
       "      <td>fnlwgt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.210325</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.379729</td>\n",
       "      <td>education-num</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.976179</td>\n",
       "      <td>marital-status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.710596</td>\n",
       "      <td>occupation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.612286</td>\n",
       "      <td>relationship</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.578379</td>\n",
       "      <td>race</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.441675</td>\n",
       "      <td>sex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.044798</td>\n",
       "      <td>capital-gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.061817</td>\n",
       "      <td>capital-loss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12.147293</td>\n",
       "      <td>hours-per-week</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>19.726726</td>\n",
       "      <td>native-country</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          VIF         Features\n",
       "0    8.521265              age\n",
       "1    8.475260        workclass\n",
       "2    4.031573           fnlwgt\n",
       "3    9.210325        education\n",
       "4   18.379729    education-num\n",
       "5    3.976179   marital-status\n",
       "6    3.710596       occupation\n",
       "7    2.612286     relationship\n",
       "8   17.578379             race\n",
       "9    4.441675              sex\n",
       "10   1.044798     capital-gain\n",
       "11   1.061817     capital-loss\n",
       "12  12.147293   hours-per-week\n",
       "13  19.726726   native-country"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking vif\n",
    "variables = df[['age', ' workclass', ' fnlwgt', ' education', ' education-num',\n",
    "       ' marital-status', ' occupation', ' relationship', ' race', ' sex',\n",
    "       ' capital-gain', ' capital-loss', ' hours-per-week', ' native-country']]\n",
    "vif = pd.DataFrame()\n",
    "vif['VIF'] = [variance_inflation_factor(variables.values, i) for i in range(variables.shape[1])]\n",
    "vif['Features'] = variables.columns\n",
    "\n",
    "vif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>workclass</th>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
       "      <td>53</td>\n",
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       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
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       "      <td>4</td>\n",
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       "      <td>11</td>\n",
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       "</table>\n",
       "<p>32561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass   fnlwgt   education   marital-status   occupation  \\\n",
       "0       39           7    77516           9                4            1   \n",
       "1       50           6    83311           9                2            4   \n",
       "2       38           4   215646          11                0            6   \n",
       "3       53           4   234721           1                2            6   \n",
       "4       28           4   338409           9                2           10   \n",
       "...    ...         ...      ...         ...              ...          ...   \n",
       "32556   27           4   257302           7                2           13   \n",
       "32557   40           4   154374          11                2            7   \n",
       "32558   58           4   151910          11                6            1   \n",
       "32559   22           4   201490          11                4            1   \n",
       "32560   52           5   287927          11                2            4   \n",
       "\n",
       "        relationship   sex   capital-gain   capital-loss   income  \n",
       "0                  1     1           2174              0        0  \n",
       "1                  0     1              0              0        0  \n",
       "2                  1     1              0              0        0  \n",
       "3                  0     1              0              0        0  \n",
       "4                  5     0              0              0        0  \n",
       "...              ...   ...            ...            ...      ...  \n",
       "32556              5     0              0              0        0  \n",
       "32557              0     1              0              0        1  \n",
       "32558              4     0              0              0        0  \n",
       "32559              3     1              0              0        0  \n",
       "32560              5     0          15024              0        1  \n",
       "\n",
       "[32561 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now, we'll drop columns which have vif>10\n",
    "df = df.drop([' education-num',' race',' hours-per-week',' native-country'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>83311</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>4</td>\n",
       "      <td>215646</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>4</td>\n",
       "      <td>234721</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>338409</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>4</td>\n",
       "      <td>257302</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>154374</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>151910</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>201490</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>287927</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30428 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age   workclass   fnlwgt   education   marital-status   occupation  \\\n",
       "0       39           7    77516           9                4            1   \n",
       "1       50           6    83311           9                2            4   \n",
       "2       38           4   215646          11                0            6   \n",
       "3       53           4   234721           1                2            6   \n",
       "4       28           4   338409           9                2           10   \n",
       "...    ...         ...      ...         ...              ...          ...   \n",
       "32556   27           4   257302           7                2           13   \n",
       "32557   40           4   154374          11                2            7   \n",
       "32558   58           4   151910          11                6            1   \n",
       "32559   22           4   201490          11                4            1   \n",
       "32560   52           5   287927          11                2            4   \n",
       "\n",
       "        relationship   sex   capital-gain   capital-loss   income  \n",
       "0                  1     1           2174              0        0  \n",
       "1                  0     1              0              0        0  \n",
       "2                  1     1              0              0        0  \n",
       "3                  0     1              0              0        0  \n",
       "4                  5     0              0              0        0  \n",
       "...              ...   ...            ...            ...      ...  \n",
       "32556              5     0              0              0        0  \n",
       "32557              0     1              0              0        1  \n",
       "32558              4     0              0              0        0  \n",
       "32559              3     1              0              0        0  \n",
       "32560              5     0          15024              0        1  \n",
       "\n",
       "[30428 rows x 11 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# removing all outliners\n",
    "df = df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "plt.subplots(figsize=(15,10))\n",
    "sns.heatmap(corr, annot=True)  # quantifying the relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting Data for Training and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.values\n",
    "X,y = data[:,:-1], data[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)  # splitting in the ration 80:20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=50, random_state=0)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators = 50, random_state=0)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making Predictions and Checking Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7531545608108108"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions are 75.31% accurate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results' Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4387,  349],\n",
       "       [ 567,  783]], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Accuracy Score: 0.7531545608108108')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Hk5DCum7SZqcl/2yiaHogAO9mVlbO5k03LTBmaRhyoNIo7DOhJU/0hxOGio9FLik8LuYS0nDWQflqcnbSXTkQN/YUCtPnjx5qpzWCZIGkn4/8dtC8qHA1Xn+atJvTRrSx0TEsoiYBcwE9pDUjzRUe2KkYZPXFNapqkP30S9atLq/trcy69Ej/QCz7rab27km1pF0Oejw9q7C6vgF6eEsxV8T9418g8aImF/4dfwA0sOZGszNae/x/q//i+mN6sgtejOzdYakEUoPMmqYRlQsPxh4LSKeaqSIDxRZJS2aSG9Uh27Rm5mtKyLdZnp0E1k+DXxB0jDSXXB7SroWWCCpX27N9yP9ih1SS734MKWBpF8Xz83zlemNcovezKwNRMRZETEwIrYiXWS9NyL+jXSn2BNythN4/y6t44DhkrpK2pp00fWJ3M2zSNKeebTN8TRzZ1e36M3M2tf5wFhJXyfdvO4IgIiYKmksMI30wKCR8f59+E8i3Y+oG+nOnU3eCbYj3+smfDHWinwx1qrJF2PX+seJZb7XjbtuzMxKzl03ZmZA91eXN5+pKT2az9Je3KI3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5JzoDczKzkHejOzknOgNzMrOQd6M7OSc6A3M2sDkjaQ9ISkZyVNlXROTj9b0iuSnsnTsMI6Z0maKWmGpCGF9N0kTc7LfilJTW27S+vtlpmZFSwD9ouIxZLWAx6WdHte9vOIuKCYWdJgYDiwA9AfuEfSJyKiHrgUGAE8BowHhgK30wi36M3M2kAki/PL9fIUTaxyKDAmIpZFxCxgJrCHpH5Az4iYGBEBXAMc1tS2HejNzFqApBGSJhWmEVXydJb0DPAacHdEPJ4XjZL0nKQrJfXOaQOAlwurz81pA/J8ZXqjHOjNzFpARIyOiN0L0+gqeeojYhdgIKl1viOpG+bjwC7AfODCnL1av3s0kd4o99GbmQGvrnh2rdbvz341542ItyTdDwwt9s1Luhy4Nb+cC2xeWG0gMC+nD6yS3ii36M3M2oCkTSVtlOe7AQcAz+c+9waHA1Py/DhguKSukrYGBgFPRMR8YJGkPfNom+OBW5ratlv0ZmZtox9wtaTOpEb22Ii4VdL/SdqF1P0yGzgRICKmShoLTAPqgJF5xA3AScBVQDfSaJtGR9yAA72ZWZuIiOeAXaukH9fEOucB51VJnwTsWOu23XVjZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXn2xS3o/r6eo477jg222wzfvGLX3DppZfywAMP0KlTJ3r37s3ZZ5/NpptuSl1dHeeeey7PP/889fX1HHTQQXz1q19lyZIlfPOb31xZ3oIFCxg2bBinn356O+6VtYRl773H8b+6jOV1ddSvWMHnd/4ko4Z+buXy3933IBf8eTwP/+SH9N6wO8vr6jjnDzcz9eW5SOKsww9hj20+3o57YB2JA307uv7669l6661ZsmQJAMcddxwnnXQSAGPGjOHyyy/nBz/4Affccw/Lly/nhhtuYOnSpRxxxBEMGTKE/v37c911160s79/+7d/Yd99922VfrGWt36ULV377m3Tv2pX36us57uLf8JnttmXnrbZg/sK3ePSFF+nXe6OV+W987EkA/vT9U3lj0WK+dfnvuOGUkXTq5JN2c9dNu1mwYAGPPPIIhx122Mq0DTfccOX8u+++S3pKWLJ06VLq6upYunQp6623Ht27d1+lvDlz5rBw4UJ23fUDzzWwdZAkunftCkBdfT119fU0fBz++5ZbOf3gA1d5QvRfFyxgz0HbALBxjw3p0W0Dprz8ShvX2jqqVmvRS9oOOBQYQHpE1jxgXERMb61trksuvPBCvvvd765szTf49a9/zfjx4+nevTuXXXYZAAcccAAPPPAAQ4cOZenSpZx22mn06tVrlfXuvPNOPve5z63y5WDrtvoVKzjiZxcz5+9vcPSn92KnLbfg3inT6NurJ9sN6L9K3m379+PeqdM4cNedePWtfzDt5Vd49a232GnLzRsp3T5MWqVFL+kMYAwg4AngyTx/vaQzW2Ob65KHHnqIPn36sP32239g2ciRI7nttts48MADGTt2LABTpkyhc+fO3HHHHYwbN45rr72WuXPnrrLeXXfdxZAhQ9qk/tY2OnfqxE3fO5l7f3wWk+e8zIx58xl9z32MGvr5D+T94h6707dXT478+a84/09/ZpettqRLZ5+wW9JaLfqvAztExHvFREk/A6YC51dbSdIIYATAZZddxtFHH91K1Wtfzz77LA8++CCPPPIIy5cvZ/Hixfzwhz/k3HPPXZln6NChnHzyyZx44onceeed7LXXXnTp0oU+ffqw8847M336dAYOHAjACy+8QH19fdUvDlv39ezWjT22+Rj3TpnGK2++yRcv+AUAC/7xNl/+2S8Zc8ooNu3ZgzMPO2TlOsf+8hK22GSTdqqxdTStFehXAP2BlyrS++VlVUXEaGB0w8tFixa1Tu3a2ahRoxg1ahQAkyZN4tprr+Xcc89lzpw5bLHFFgA88MADbLXVVgD07duXSZMmMWzYMJYuXcqUKVM45phjVpZ35513ujVfMm8uXkyXzp3p2a0bS5e/x8QXZvL1/T7LQz/54co8nzv3fMae+h16b9idd5cvJwI+0nV9Hp3xIp07dWKbj/Ztxz2wjqS1Av0pwARJLwIv57QtgG2AUa20zXXexRdfzEsvvUSnTp3o168fZ511FgBHHnkk55xzDkcddRQRwSGHHMKgQYNWrnfPPfdw0UUXtVe1rRW8/vYifnD9WFasCFZEMGTnT7LPDo2fsb25eDEjLruSThKb9erF+ccc1Ya1tY5OEdE6BUudgD1IF2MFzAWejIj6GosobYve1kyPHj0AqLvt5nauiXUkXQ46HGCtRyHMm3HvWgXD/tvu12FHQrTaqJuIWAE81lrlm5lZbXxZ3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozczagKQNJD0h6VlJUyWdk9P7SLpb0ov5b+/COmdJmilphqQhhfTdJE3Oy36pZm5b6wePmJkB/bft1XymtbMM2C8iFktaD3hY0u3AF4EJEXF+vrvvmcAZkgYDw4EdSPcOu0fSJ/LdBS4l3QDyMWA8MBS4vbENu0VvZtYGIlmcX66XpyA9t+PqnH41cFiePxQYExHLImIWMBPYQ1I/oGdETIx0D5trCutU5UBvZtZGJHWW9AzwGnB3RDwO9I2I+QD572Y5+wDevykkpPuFDcjT3CrpjXKgNzNrAZJGSJpUmEZU5omI+ojYBRhIap3v2FSRVdKiifRGuY/ezKwFVDxPo7m8b0m6n9S3vkBSv4iYn7tlXsvZ5gLFZ0EOJD2SdW6er0xvlFv0ZmZtQNKmkjbK892AA4DngXHACTnbCcAteX4cMFxSV0lbA4OAJ3L3ziJJe+bRNscX1qnKLXozs7bRD7haUmdSI3tsRNwqaSIwVtLXgTnAEQARMVXSWGAaUAeMLDzP4yTgKqAbabRNoyNuoBUfPNIC/OARW4UfPGLVtNSDR+CptQyGu3XYB4+468bMrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQc6M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQafTi4pEVAwzMUG56FGHk+IqJnK9fNzMxaQKOBPiJ6tGVFzMysddTUdSPpXyV9Nc9vImnr1q2WmZm1lGYDvaQfA2cAZ+Wk9YFrW7NSZmbWcmpp0R8OfAFYAhAR8wB365iZrSNqCfTLIyLIF2YldW/dKpmZWUuqJdCPlXQZsJGkbwL3AJe3brXMzKylNDrqpkFEXCDpc8DbwCeAH0XE3a1eMzMzaxHNBvpsMtCN1H0zufWqY2ZmLa3ZQC/pG8CPgHtJP5a6WNJPIuLK1q6cmVlbiRdeWKv19YndWqgmLa+WFv2/A7tGxBsAkjYGHgUc6M3M1gG1XIydCywqvF4EvNw61TEzs5bW1L1uTsuzrwCPS7qF1Ed/KPBEG9TNzMxaQFMt+h55+ivwJ96/wdktwPzWrZaZWblI2lzSfZKmS5oq6eScfrakVyQ9k6dhhXXOkjRT0gxJQwrpu0manJf9UpKqbbNBUzc1O6clds7MzACoA06PiKcl9QCektQwVP3nEXFBMbOkwcBwYAegP3CPpE9ERD1wKTACeAwYDwwFbm9sw7WMutkU+H7e2AYN6RGxX+37Z2b24RYR88m9IRGxSNJ0YEATqxwKjImIZcAsSTOBPSTNBnpGxEQASdcAh9FEoK/lYuzvgeeBrYFzgNnAkzWsZ2b2oSFphKRJhWlEE3m3AnYFHs9JoyQ9J+lKSb1z2gBWHfgyN6cNyPOV6Y2qJdBvHBFXAO9FxAMR8TVgzxrWMzP70IiI0RGxe2EaXS2fpA2BPwKnRMTbpG6YjwO7kFr8FzZkrbaZJtIbVcs4+vfy3/mSDgLmAQNrWM/MzAokrUcK8r+PiJsAImJBYfnlwK355Vxg88LqA0nxdy6rxuCG9EbV0qL/qaRewOnA94DfAqfWsJ6ZmWV5ZMwVwPSI+FkhvV8h2+HAlDw/DhguqWt+2NMg4Inc179I0p65zONJoyEbVctNzRq+Xf4B7FvjPpmZ2ao+DRwHTJb0TE77AXC0pF1I3S+zgRMBImKqpLHANNKInZF5xA3AScBVpHuQ3U4TF2Kh6R9MXUwT/T4R8d2m98nMzBpExMNU718f38Q65wHnVUmfBOxY67abatFPqrUQMzPruJr6wdTVbVkRMzNrHbVcjDUzs3WYA72ZWcnV+oSpdtGjR4/2roJ1QF0OOry9q2C2TvGoGzOzkuvQo27qbru5vatgHUhDS96fCyvyGV7zPOrGzKzkar1N8RnAYHybYjOzdU6ttymejm9TbGa2TvJtis3MSs63KTYzK7laAn3xNsUXAz3xbYrNzNYZvk2xmVnJ1TLq5ndU+eFU7qs3M7MOrpaum1sL8xuQnoDS5GOrzMys46il6+aPxdeSrgfuabUamZlZi1qTu1cOArZo6YqYmVnrqKWPfhGr9tG/SvqlrJmZrQNq6brxvYLNzNZhzXbdSJpQS5qZmXVMTd2PfgPgI8Amknrz/tPLewL926BuZmbWAprqujkROIUU1J/i/UD/NvDr1q2WmZm1lKbuR38RcJGk70TExW1YJzOzNjf77b3Xav2tW6geraGW4ZUrJG3U8EJSb0nfbr0qmZlZS6ol0H8zIt5qeBERC4FvtlqNzMysRdUS6DtJauifR1JnYP3Wq5KZmbWkWu51cycwVtJvSD+c+hZwR6vWyszMWkwtgf4MYARwEmnkzV3A5a1ZKTMzaznNdt1ExIqI+E1EfDkivgRMJT2AxMzM1gG1tOiRtAtwNHAUMAu4qRXrZGZmLajRFr2kT0j6kaTpwK+AuYAiYl+PqzczWz2SNpd0n6TpkqZKOjmn95F0t6QX89/ehXXOkjRT0gxJQwrpu0manJf9sjhgppqmum6eB/YHDomIf83BvX7tdtXM7EOrDjg9IrYH9gRGShoMnAlMiIhBwIT8mrxsOLADMBS4JI96BLiUdO10UJ6GNrXhpgL9l0i3JL5P0uWS9uf92yCYmdlqiIj5EfF0nl8ETAcGAIcCV+dsVwOH5flDgTERsSwiZgEzgT0k9QN6RsTEiAjgmsI6VTUa6CPi5og4CtgOuB84Fegr6VJJn1+THTUzM5C0FbAr8DjQNyLmQ/oyADbL2QYALxdWm5vTBuT5yvRG1TLqZklE/D4iDgYGAs+QTy3MzCyRNELSpMI0opF8GwJ/BE6JiLebKrJKWjSR3qiaRt2sLCniTeCyPJmZWRYRo4HRTeWRtB4pyP8+IhpGLy6Q1C8i5udumddy+lxg88LqA4F5OX1glfRGrckzY83MbDXlkTFXANMj4meFReOAE/L8CcAthfThkrpK2pp00fWJ3L2zSNKeuczjC+tUtVotejMzW2OfBo4DJkt6Jqf9ADifdJuZrwNzgCMAImKqpLHANNKInZER0TDy8STgKqAbcHueGuVAb2bWBiLiYRofubh/I+ucB5xXJX0SsGOt23bXjZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnB8ObmYGvLnVS2u1/tYMaKGatDy36M3MSs6B3sys5BzozcxKzoHezKzkHOjNzErOgd7MrOQ8vLID+Ny559O9a1c6depEl06dGHvadwD4/UOPcN3DE+ncqRN7D96O7x0yjFuf+gtX3vfgynVfmP8qfzjtO2w/oH97Vd9awazXXuf0a65b+XruG28yaujn+KdtPsZP/nAzy+rq6NKpE//xpcPYacvNee6llzn7DzcBEBGMHHIAB+y0Y3tV3zoYB/oO4nffHkHvDbuvfP34i3/l3inTufnfT2H9Ll14Y9FiAA7ebVcO3m1XAF6Y9yrfufIaB/kS2nqzTbnpeycDUL9iBfue858c8Mkd+PHYm/j2kAP4zPbb8uC05/nZreO5auSJDOrXl7GnjqJL5868/vbbfPGCi9hnh+3p0rlzO++JdQQO9B3UDY8+xjf2/yzrd0lv0cY9NvxAnvF/eYZhn9q5ratmbeyxF2ey+cYb079PbxAsXroUgEVLl7Jpz54AdFt//ZX5l71Xh1C71NU6Jgf6DkAS37zsCiRxxF57cORe/8zs1//OU3+bzUXj76Jrly587wvD+OQWm6+y3h3PPMfFXzu+nWptbeX2vzzLsF3TF/qZhx3CiMuu4II/j2fFiuD33z1pZb7nXprDf4y5kXkL3+L8Y450a95WavOLsZK+2tbb7Oiu/c5J3Hj6d/nNN7/K9Q9PZNJf/0b9ihW8/c67XH/ytzn9kGGcfs11RMTKdZ57aQ4brLceg/p9tB1rbq1teV0d902dzpBdPgnADY88xhmHHsyEH53FGYcdzA9v+OPKvDttuQXjzjiNG04dxeUT7mfZe++1V7WtCklXSnpN0pRC2tmSXpH0TJ6GFZadJWmmpBmShhTSd5M0OS/7paRmT9/aY9TNOY0tkDRC0iRJk0aPHt2WdWpXm/VKp98b99iQAz65A5PnzKVvr14csNMOSGKnLTenk8TCJUtWrjP+L88y7FO7tFONra08/PwMBg8YwCY9egBwy6Sn+Fy+yDpk508yec7LH1jn4303o9v66/PiqwvatK7WrKuAoVXSfx4Ru+RpPICkwcBwYIe8ziWSGk7RLgVGAIPyVK3MVbRKoJf0XCPTZKBvY+tFxOiI2D0idh8xYkRrVK3DeWfZcpYsXbZy/tEXXmSbj/Zl/08O5vEX/wrA7Nde5736enp3TxdrV6xYwV3PTubAXXdqt3pb2xj/9LOrXIfZrGdPnvzr34B0wX7LTTcB0qicuvp6AOa9uZDZr7/OgN69277C1qiIeBB4s8bshwJjImJZRMwCZgJ7SOoH9IyIiZFO8a8BDmuusNbqo+8LDAEWVqQLeLSVtrlOemPxIr575f8BaXTFQZ/ahc9svy3L6+r44ZgbOfR/fs56nTtz3tFH0HCGNulvs+jbqxebb7xxe1bdWtm7y5fz6Asz+fERX1yZdvaRX+L8P/2Zuvp6uq63HmcfcTgAT8+azW8n3E+Xzp3pJPHDLx22yigu69BGSToemAScHhELgQHAY4U8c3Pae3m+Mr1JKvb7thRJVwC/i4iHqyy7LiKOqaGYqLvt5havm627uhyUgpo/F1aUPxdrPczoqb8/ulbBcPdNP30iqUulweiIWKUPWtJWwK0RsWN+3Rf4OxDAuUC/iPiapF8DEyPi2pzvCmA8MAf4r4g4IKd/Bvh+RBzSVN1apUUfEV9vYlktQd7MbJ2Sg/pqXVyMiJUXUiRdDtyaX84FisPsBgLzcvrAKulN8i0QzMzaSe5zb3A40DAiZxwwXFJXSVuTLro+ERHzgUWS9syjbY4HbmluOx5Hb2bWBiRdD+wDbCJpLvBjYB9Ju5C6bmYDJwJExFRJY4FpQB0wMiLqc1EnkUbwdANuz1OTHOjNzNpARBxdJfmKJvKfB5xXJX0SsFo3MnLXjZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcHzxiZgbMn7PF2hWwScvUozW4RW9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmYl50BvZlZyDvRmZiXnQG9mVnIO9GZmJedAb2ZWcg70ZmZtQNKVkl6TNKWQ1kfS3ZJezH97F5adJWmmpBmShhTSd5M0OS/7pSQ1t20HejOztnEVMLQi7UxgQkQMAibk10gaDAwHdsjrXCKpc17nUmAEMChPlWV+gAO9mVkbiIgHgTcrkg8Frs7zVwOHFdLHRMSyiJgFzAT2kNQP6BkREyMigGsK6zTKgd7MrAVIGiFpUmEaUcNqfSNiPkD+u1lOHwC8XMg3N6cNyPOV6U3yM2PNzFpARIwGRrdQcdX63aOJ9Ca5RW9m1n4W5O4Y8t/XcvpcYPNCvoHAvJw+sEp6kxzozczazzjghDx/AnBLIX24pK6StiZddH0id+8skrRnHm1zfGGdRrnrxsysDUi6HtgH2ETSXODHwPnAWElfB+YARwBExFRJY4FpQB0wMiLqc1EnkUbwdANuz1OTHOjNzNpARBzdyKL9G8l/HnBelfRJwI6rs2133ZiZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJOdCbmZWc73VjZgYMnf/kWpYwsPks7cQtejOzknOgNzMrOQd6M7OSc6A3Mys5B3ozs5Lr0KNuuhx0eHtXwTogfy7MVk9HDvRq7wp0FJJGRMTo9q6HdSz+XFit3HWzbhjR3hWwDsmfC6uJA72ZWck50JuZlZwD/brB/bBWjT8XVhMH+nWAL7hZNf5cWK0c6M3MSs6BvoOTNFTSDEkzJZ3Z3vWx9ifpSkmvSZrS3nWxdYMDfQcmqTPwa+BAYDBwtKTB7Vsr6wCuAoa2dyVs3eFA37HtAcyMiL9FxHJgDHBoO9fJ2llEPAi82d71sHWHA33HNgB4ufB6bk4zM6uZA33HVu02ENHmtTCzdZoDfcc2F9i88HogMK+d6mJm6ygH+o7tSWCQpK0lrQ8MB8a1c53MbA1Jmi1psqRnJE3KaX0k3S3pxfy3dyH/WXnE3QxJQ9Z0uw70HVhE1AGjgDuB6cDYiJjavrWy9ibpemAisK2kuZK+3t51stWyb0TsEhG759dnAhMiYhAwIb8mj7AbDuxAGmV1SR6Jt9o68m2KDYiI8cD49q6HdRwRcXR718Fa1KHAPnn+auB+4IycPiYilgGzJM0kjcSbuLobcIvezKztBHCXpKckNdxmum9EzAfIfzfL6S026s4tejOzFpADd/EZAaOr3I/o0xExT9JmwN2Snm+qyCppazTqzoHezKwF5KDe5I3mImJe/vuapJtJXTELJPWLiPmS+gGv5ewtNurOXTdmZm1AUndJPRrmgc8DU0gj6U7I2U4Absnz44DhkrpK2hoYBDyxJtt2i97MrG30BW6WBCn2XhcRd0h6EhibR0/NAY4AiIipksYC04A6YGRE1K/JhhXhH1pa4yTVA5NJH8zpwAkR8c4alnUVcGtE3Cjpt8DPImJaI3n3AZZHxKOruY3ZwO4R8fda0ivyLI6IDVdjW2cDiyPigtWpo3VMdbfdvFbBsMtBh1frU+8Q3HVjzXk3j/ndEVgOfKu4cE3H9UbENxoL8tk+wL+sSdlmtioHelsdDwHbSNpH0n2SrgMmS+os6X8lPSnpOUknAij5laRpkm7j/WFjSLpf0u55fqikpyU9K2mCpK1IXyin5l8QfkbSppL+mLfxpKRP53U3lnSXpL9IuozqIxVWIelPeXjb1MIQt4ZlF+a6TJC0aU77uKQ78joPSdquRY6mWRtxH73VRFIX0n3x78hJewA7RsSsHCz/ERH/JKkr8Iiku4BdgW2BT5L6J6cBV1aUuylwObB3LqtPRLwp6TcUukXyl8rPI+JhSVuQfi28PfBj4OGI+Imkg1h1eFtjvpa30Q14UtIfI+INoDvwdEScLulHuexRpJEU34qIFyX9M3AJsN8aHEazduFAb83pJumZPP8QcAWpS+WJiJiV0z8P7CTpy/l1L9IIgb2B6/MFpHmS7q1S/p7Agw1lRURj91k/ABicL2QB9MwjGPYGvpjXvU3Swhr26buSDs/zm+e6vgGsAG7I6dcCN0naMO/vHwrb7lrDNsw6DAd6a867EbFLMSEHvCXFJOA7EXFnRb5hNP8DD9WQB1I3414R8W6VutR8ES1f5D0gl/WOpPuBDRrJHnm7b1UeA7N1ifvorSXcCZwkaT0ASZ/I44QfJI0D7px/CLJvlXUnAp/N44SR1CenLwJ6FPLdRepGIefbJc8+CByb0w4EetO0XsDCHOS3I51RNOgENJyVHEPqEnqbdJ+RI/I2JGnnZrZh1qE40FtL+C2p//1ppQdWX0Y6W7wZeJE0PPNS4IHKFSPidVK/+k2SnuX9rpM/A4c3XIwFvgvsni/2TuP90T/nAHtLeprUhTSnmbreAXSR9BxwLvBYYdkSYAdJT5H64H+S048Fvp7rNxU/ztHWMR5Hb2ZWcm7Rm5mVnAO9mVnJOdCbmZWcA72ZWck50JuZlZwDvZlZyTnQm5mVnAO9mVnJ/X/WWw+YD7K8wgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sns.heatmap(cm, annot=True, fmt=\".0f\", linewidths=0.5, square = True, cmap = 'Pastel1')\n",
    "plt.ylabel('Actual label')\n",
    "plt.xlabel('Predicted label')\n",
    "all_sample_title = 'Accuracy Score: {0}'.format(roc_auc_score(y_test, y_pred))\n",
    "plt.title(all_sample_title, size = 15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}