Adult_Income_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5ea8692",
   "metadata": {},
   "source": [
    "# GROUP 6\n",
    "\n",
    "## Group Members\n",
    "\n",
    "   * **K M JIAUL ISLAM JIBON (L)     -> 21190201123**\n",
    "   * RATNA KHATUN              -> 21190201158\n",
    "   * MD EKRAM                  -> 21190201179\n",
    "   * SHIKHA AKTER              -> 21180300975\n",
    "   * MD NOBEL RANA NELOY       -> 21190201160\n",
    "   * MD TANVIR ISLAM           -> 21180300995\n",
    "    \n",
    "# High Salary Prediction using Machine Learning\n",
    "    \n",
    "The purpose of this project is to use data transformation and machine learning to create a model that will predict a high salary when given sex, education, marital status etc.\n",
    "The data for this model is fairly simplified as it has very few missing pieces. The raw data consists of a training dataset with the features listed above and their corresponding salaries.\n",
    "\n",
    "\n",
    "This model can be used as a guide when determining salaries since it shows reasonable predictions when given information varioud fields like workclass, education, marital.status, etc.\n",
    "\n",
    "## Objectives\n",
    "We will try to build a machine learning model to accurately predict whether or not the salary of a category person salary is high or not?\n",
    "\n",
    "\n",
    "><font color=black>__Methods Used__</font>\n",
    "\n",
    "        -Data Analysis and Visualization\n",
    "        -Linear Regression\n",
    "        -Polynomial Transformation\n",
    "        -Decision Tree\n",
    "        -Random Forest\n",
    "\n",
    "><font color=black>__Technologies/Libraries Used__</font>\n",
    "\n",
    "        1.Python 3\n",
    "        2.Pandas\n",
    "        3.NumPy\n",
    "        4.Seaborn\n",
    "        5.Scikit-learn\n",
    "        6.Matplotlib\n",
    "        7.Jupyter\n",
    "        \n",
    "><font color=black>__Dataset Name:__</font>\n",
    "\n",
    "        - adult.csv\n",
    "        \n",
    "## Details about the dataset:\n",
    "\n",
    "The datasets consists of several medical predictor variables and one target variable, `High income`. Predictor variables includes the number of high or low salary based on `age`, `workclass`, `education`, `marital.status` and so on.\n",
    "\n",
    "\n",
    "- **age**: continuous.\n",
    "- **workclass**: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.\n",
    "- **fnlwgt**: continuous.\n",
    "- **education**: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.\n",
    "- **education-num**: continuous.\n",
    "- **marital-status**: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.\n",
    "- **occupation**: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.\n",
    "- **relationship**: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.\n",
    "- **race**: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.\n",
    "- **sex**: Female, Male.\n",
    "- **capital-gain**: continuous.\n",
    "- **capital-loss**: continuous.\n",
    "- **hours-per-week**: continuous.\n",
    "- **High income**: Class Variable(0,1)\n",
    "\n",
    "**Number of Observation Units: 32561**\n",
    "\n",
    "**Variable Number: 14**\n",
    "\n",
    "**GitHub Link**: [Adult-Machine-Learning](https://github.com/jiaulislam/adult-machine-learning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ea4456e",
   "metadata": {},
   "source": [
    "# Import Libraries and Load Data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e1b59e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e4b4d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('adult.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc2a6c22",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>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>High 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",
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       "    <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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>216864</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>150601</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>74</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>88638</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3683</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>422013</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3683</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>70037</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3004</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
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      "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",
       "4   41      Private  264663  Some-college             10      Separated   \n",
       "5   34      Private  216864       HS-grad              9       Divorced   \n",
       "6   38      Private  150601          10th              6      Separated   \n",
       "7   74    State-gov   88638     Doctorate             16  Never-married   \n",
       "8   68  Federal-gov  422013       HS-grad              9       Divorced   \n",
       "9   41      Private   70037  Some-college             10  Never-married   \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",
       "4     Prof-specialty       Own-child  White  Female             0   \n",
       "5      Other-service       Unmarried  White  Female             0   \n",
       "6       Adm-clerical       Unmarried  White    Male             0   \n",
       "7     Prof-specialty  Other-relative  White  Female             0   \n",
       "8     Prof-specialty   Not-in-family  White  Female             0   \n",
       "9       Craft-repair       Unmarried  White    Male             0   \n",
       "\n",
       "   capital.loss  hours.per.week  High income  \n",
       "0          4356              40            0  \n",
       "1          4356              18            0  \n",
       "2          4356              40            0  \n",
       "3          3900              40            0  \n",
       "4          3900              40            0  \n",
       "5          3770              45            0  \n",
       "6          3770              40            0  \n",
       "7          3683              20            1  \n",
       "8          3683              40            0  \n",
       "9          3004              60            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db92322",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (Pre Phase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "224fbbab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbb552a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  73\n",
       "workclass             9\n",
       "fnlwgt            21648\n",
       "education            16\n",
       "education.num        16\n",
       "marital.status        7\n",
       "occupation           15\n",
       "relationship          6\n",
       "race                  5\n",
       "sex                   2\n",
       "capital.gain        119\n",
       "capital.loss         92\n",
       "hours.per.week       94\n",
       "High income           2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for each unique value in each column/features\n",
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e41dd859",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                int64\n",
       "workclass         object\n",
       "fnlwgt             int64\n",
       "education         object\n",
       "education.num      int64\n",
       "marital.status    object\n",
       "occupation        object\n",
       "relationship      object\n",
       "race              object\n",
       "sex               object\n",
       "capital.gain       int64\n",
       "capital.loss       int64\n",
       "hours.per.week     int64\n",
       "High income        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for data types for each features\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44d307b9",
   "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",
       "High income       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if any null value in each features\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2bb952f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>mean</th>\n",
       "      <th>std</th>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>38.581647</td>\n",
       "      <td>13.640433</td>\n",
       "      <td>17.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>90.0</td>\n",
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       "    <tr>\n",
       "      <th>fnlwgt</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>189778.366512</td>\n",
       "      <td>105549.977697</td>\n",
       "      <td>12285.0</td>\n",
       "      <td>117827.0</td>\n",
       "      <td>178356.0</td>\n",
       "      <td>237051.0</td>\n",
       "      <td>1484705.0</td>\n",
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       "    <tr>\n",
       "      <th>education.num</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>10.080679</td>\n",
       "      <td>2.572720</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
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       "    <tr>\n",
       "      <th>capital.gain</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>1077.648844</td>\n",
       "      <td>7385.292085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital.loss</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>87.303830</td>\n",
       "      <td>402.960219</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4356.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours.per.week</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>40.437456</td>\n",
       "      <td>12.347429</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>32561.0</td>\n",
       "      <td>0.240810</td>\n",
       "      <td>0.427581</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count           mean            std      min       25%  \\\n",
       "age             32561.0      38.581647      13.640433     17.0      28.0   \n",
       "fnlwgt          32561.0  189778.366512  105549.977697  12285.0  117827.0   \n",
       "education.num   32561.0      10.080679       2.572720      1.0       9.0   \n",
       "capital.gain    32561.0    1077.648844    7385.292085      0.0       0.0   \n",
       "capital.loss    32561.0      87.303830     402.960219      0.0       0.0   \n",
       "hours.per.week  32561.0      40.437456      12.347429      1.0      40.0   \n",
       "High income     32561.0       0.240810       0.427581      0.0       0.0   \n",
       "\n",
       "                     50%       75%        max  \n",
       "age                 37.0      48.0       90.0  \n",
       "fnlwgt          178356.0  237051.0  1484705.0  \n",
       "education.num       10.0      12.0       16.0  \n",
       "capital.gain         0.0       0.0    99999.0  \n",
       "capital.loss         0.0       0.0     4356.0  \n",
       "hours.per.week      40.0      45.0       99.0  \n",
       "High income          0.0       0.0        1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's get some understaning of data grid\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eaad042",
   "metadata": {},
   "source": [
    "####  ***I observed that i have some invalid column data in data-grid. Let's find out the total counts of `?` mark data for each feature***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "20a88b6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private             22696\n",
       "Self-emp-not-inc     2541\n",
       "Local-gov            2093\n",
       "?                    1836\n",
       "State-gov            1298\n",
       "Self-emp-inc         1116\n",
       "Federal-gov           960\n",
       "Without-pay            14\n",
       "Never-worked            7\n",
       "Name: workclass, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['workclass'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8ed8a518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HS-grad         10501\n",
       "Some-college     7291\n",
       "Bachelors        5355\n",
       "Masters          1723\n",
       "Assoc-voc        1382\n",
       "11th             1175\n",
       "Assoc-acdm       1067\n",
       "10th              933\n",
       "7th-8th           646\n",
       "Prof-school       576\n",
       "9th               514\n",
       "12th              433\n",
       "Doctorate         413\n",
       "5th-6th           333\n",
       "1st-4th           168\n",
       "Preschool          51\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4cce291b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married-civ-spouse       14976\n",
       "Never-married            10683\n",
       "Divorced                  4443\n",
       "Separated                 1025\n",
       "Widowed                    993\n",
       "Married-spouse-absent      418\n",
       "Married-AF-spouse           23\n",
       "Name: marital.status, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital.status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c570da0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prof-specialty       4140\n",
       "Craft-repair         4099\n",
       "Exec-managerial      4066\n",
       "Adm-clerical         3770\n",
       "Sales                3650\n",
       "Other-service        3295\n",
       "Machine-op-inspct    2002\n",
       "?                    1843\n",
       "Transport-moving     1597\n",
       "Handlers-cleaners    1370\n",
       "Farming-fishing       994\n",
       "Tech-support          928\n",
       "Protective-serv       649\n",
       "Priv-house-serv       149\n",
       "Armed-Forces            9\n",
       "Name: occupation, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['occupation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "77a8b486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Husband           13193\n",
       "Not-in-family      8305\n",
       "Own-child          5068\n",
       "Unmarried          3446\n",
       "Wife               1568\n",
       "Other-relative      981\n",
       "Name: relationship, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['relationship'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2baf5b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "White                 27816\n",
       "Black                  3124\n",
       "Asian-Pac-Islander     1039\n",
       "Amer-Indian-Eskimo      311\n",
       "Other                   271\n",
       "Name: race, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['race'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ef552a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      21790\n",
       "Female    10771\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee0520b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40    15217\n",
       "50     2819\n",
       "45     1824\n",
       "60     1475\n",
       "35     1297\n",
       "      ...  \n",
       "94        1\n",
       "82        1\n",
       "92        1\n",
       "87        1\n",
       "74        1\n",
       "Name: hours.per.week, Length: 94, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hours.per.week'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f8a11bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24720\n",
       "1     7841\n",
       "Name: High income, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['High income'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e91720e0",
   "metadata": {},
   "source": [
    "# Replace `?` values with Mode value in its self column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3322e2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# So far there are 2 features column that are having ? marks as value [workclass, occupation].\n",
    "# I don't want to remove those value as their count is much higher and it would make our model bad prediction\n",
    "# Let's replace them with their mode value\n",
    "df['workclass'].replace('?', 'Private', inplace=True)\n",
    "df['occupation'].replace('?', 'Prof-specialty', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87723c32",
   "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>High income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>Private</td>\n",
       "      <td>77053</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>186061</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Prof-specialty</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>0</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>216864</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>150601</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3770</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>74</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>88638</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3683</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>68</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>422013</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3683</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>70037</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>3004</td>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age    workclass  fnlwgt     education  education.num marital.status  \\\n",
       "0   90      Private   77053       HS-grad              9        Widowed   \n",
       "1   82      Private  132870       HS-grad              9        Widowed   \n",
       "2   66      Private  186061  Some-college             10        Widowed   \n",
       "3   54      Private  140359       7th-8th              4       Divorced   \n",
       "4   41      Private  264663  Some-college             10      Separated   \n",
       "5   34      Private  216864       HS-grad              9       Divorced   \n",
       "6   38      Private  150601          10th              6      Separated   \n",
       "7   74    State-gov   88638     Doctorate             16  Never-married   \n",
       "8   68  Federal-gov  422013       HS-grad              9       Divorced   \n",
       "9   41      Private   70037  Some-college             10  Never-married   \n",
       "\n",
       "          occupation    relationship   race     sex  capital.gain  \\\n",
       "0     Prof-specialty   Not-in-family  White  Female             0   \n",
       "1    Exec-managerial   Not-in-family  White  Female             0   \n",
       "2     Prof-specialty       Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct       Unmarried  White  Female             0   \n",
       "4     Prof-specialty       Own-child  White  Female             0   \n",
       "5      Other-service       Unmarried  White  Female             0   \n",
       "6       Adm-clerical       Unmarried  White    Male             0   \n",
       "7     Prof-specialty  Other-relative  White  Female             0   \n",
       "8     Prof-specialty   Not-in-family  White  Female             0   \n",
       "9       Craft-repair       Unmarried  White    Male             0   \n",
       "\n",
       "   capital.loss  hours.per.week  High income  \n",
       "0          4356              40            0  \n",
       "1          4356              18            0  \n",
       "2          4356              40            0  \n",
       "3          3900              40            0  \n",
       "4          3900              40            0  \n",
       "5          3770              45            0  \n",
       "6          3770              40            0  \n",
       "7          3683              20            1  \n",
       "8          3683              40            0  \n",
       "9          3004              60            1  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d8e35f",
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0694acf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One more observation found in data that for object columns there are many redundant features/category. We have to do some\n",
    "# feature engineering for simplifing data\n",
    "\n",
    "# For education column there are 17 categories. let's simplify first that column\n",
    "df['education'].replace(['10th', '7th-8th', '9th', '5th-6th', '1st-4th', 'Preschool'], 'School', inplace=True)\n",
    "df['education'].replace(['Some-college', '12th', '11th','HS-grad'], 'College', inplace=True)\n",
    "df['education'].replace(['Assoc-voc', 'Assoc-acdm'], 'Prof-school', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b9663b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "College        19400\n",
       "Bachelors       5355\n",
       "Prof-school     3025\n",
       "School          2645\n",
       "Masters         1723\n",
       "Doctorate        413\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5aa625d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For maretial status there are 7 types of category. Let's simplify that\n",
    "df['marital.status'].replace(['Married-civ-spouse', 'Married-spouse-absent', 'Married-AF-spouse'], 'Married', inplace=True)\n",
    "df['marital.status'].replace('Seperated', 'Divorced', inplace=True)\n",
    "df['marital.status'].replace('Never-married', 'Unmarried', inplace=True)\n",
    "df['marital.status'].replace(['Separated', 'Widowed'], 'Others', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "92f2a401",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married      15417\n",
       "Unmarried    10683\n",
       "Divorced      4443\n",
       "Others        2018\n",
       "Name: marital.status, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['marital.status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d75c22da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['race'].replace(['Asian-Pac-Islander', 'Amer-Indian-Eskimo'], 'Other', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e0ed561f",
   "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>High income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>90</td>\n",
       "      <td>Private</td>\n",
       "      <td>77053</td>\n",
       "      <td>College</td>\n",
       "      <td>9</td>\n",
       "      <td>Others</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>4356</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>82</td>\n",
       "      <td>Private</td>\n",
       "      <td>132870</td>\n",
       "      <td>College</td>\n",
       "      <td>9</td>\n",
       "      <td>Others</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>66</td>\n",
       "      <td>Private</td>\n",
       "      <td>186061</td>\n",
       "      <td>College</td>\n",
       "      <td>10</td>\n",
       "      <td>Others</td>\n",
       "      <td>Prof-specialty</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>140359</td>\n",
       "      <td>School</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>264663</td>\n",
       "      <td>College</td>\n",
       "      <td>10</td>\n",
       "      <td>Others</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>3900</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age workclass  fnlwgt education  education.num marital.status  \\\n",
       "0   90   Private   77053   College              9         Others   \n",
       "1   82   Private  132870   College              9         Others   \n",
       "2   66   Private  186061   College             10         Others   \n",
       "3   54   Private  140359    School              4       Divorced   \n",
       "4   41   Private  264663   College             10         Others   \n",
       "\n",
       "          occupation   relationship   race     sex  capital.gain  \\\n",
       "0     Prof-specialty  Not-in-family  White  Female             0   \n",
       "1    Exec-managerial  Not-in-family  White  Female             0   \n",
       "2     Prof-specialty      Unmarried  Black  Female             0   \n",
       "3  Machine-op-inspct      Unmarried  White  Female             0   \n",
       "4     Prof-specialty      Own-child  White  Female             0   \n",
       "\n",
       "   capital.loss  hours.per.week  High income  \n",
       "0          4356              40            0  \n",
       "1          4356              18            0  \n",
       "2          4356              40            0  \n",
       "3          3900              40            0  \n",
       "4          3900              40            0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a154c0",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (Post Phase)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a74c8b03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis (Pre Phase)# Histogram and density graphs of all variables(numerics) were accessed.\n",
    "df.hist(figsize=(12,12), layout=(4,2),);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a4142d",
   "metadata": {},
   "source": [
    "### Correlation Matrix Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "207d68f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix graph of the data set\n",
    "f, ax = plt.subplots(figsize= [20,15])\n",
    "sns.heatmap(df.corr(), annot=True, fmt=\".2f\", ax=ax, cmap = \"magma\" )\n",
    "ax.set_title(\"Correlation Matrix\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1e73fde1",
   "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>fnlwgt</th>\n",
       "      <th>education.num</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>High income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.076646</td>\n",
       "      <td>0.036527</td>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.057775</td>\n",
       "      <td>0.068756</td>\n",
       "      <td>0.234037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fnlwgt</th>\n",
       "      <td>-0.076646</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>-0.009463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education.num</th>\n",
       "      <td>0.036527</td>\n",
       "      <td>-0.043195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.335154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital.gain</th>\n",
       "      <td>0.077674</td>\n",
       "      <td>0.000432</td>\n",
       "      <td>0.122630</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.223329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>capital.loss</th>\n",
       "      <td>0.057775</td>\n",
       "      <td>-0.010252</td>\n",
       "      <td>0.079923</td>\n",
       "      <td>-0.031615</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>0.150526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hours.per.week</th>\n",
       "      <td>0.068756</td>\n",
       "      <td>-0.018768</td>\n",
       "      <td>0.148123</td>\n",
       "      <td>0.078409</td>\n",
       "      <td>0.054256</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.229689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High income</th>\n",
       "      <td>0.234037</td>\n",
       "      <td>-0.009463</td>\n",
       "      <td>0.335154</td>\n",
       "      <td>0.223329</td>\n",
       "      <td>0.150526</td>\n",
       "      <td>0.229689</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age    fnlwgt  education.num  capital.gain  capital.loss  \\\n",
       "age             1.000000 -0.076646       0.036527      0.077674      0.057775   \n",
       "fnlwgt         -0.076646  1.000000      -0.043195      0.000432     -0.010252   \n",
       "education.num   0.036527 -0.043195       1.000000      0.122630      0.079923   \n",
       "capital.gain    0.077674  0.000432       0.122630      1.000000     -0.031615   \n",
       "capital.loss    0.057775 -0.010252       0.079923     -0.031615      1.000000   \n",
       "hours.per.week  0.068756 -0.018768       0.148123      0.078409      0.054256   \n",
       "High income     0.234037 -0.009463       0.335154      0.223329      0.150526   \n",
       "\n",
       "                hours.per.week  High income  \n",
       "age                   0.068756     0.234037  \n",
       "fnlwgt               -0.018768    -0.009463  \n",
       "education.num         0.148123     0.335154  \n",
       "capital.gain          0.078409     0.223329  \n",
       "capital.loss          0.054256     0.150526  \n",
       "hours.per.week        1.000000     0.229689  \n",
       "High income           0.229689     1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45ea32a6",
   "metadata": {},
   "source": [
    "### Distribution Pie Chart of High Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "667046e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The distribution of the High income variable in the data examined and visualized.\n",
    "f,ax=plt.subplots(1,2,figsize=(18,8))\n",
    "df['High income'].value_counts().plot.pie(explode=[0,0.1],autopct='%1.1f%%',ax=ax[0],shadow=True)\n",
    "ax[0].set_title('target')\n",
    "ax[0].set_ylabel('')\n",
    "sns.countplot(x='High income',data=df,ax=ax[1])\n",
    "ax[1].set_title('High income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9b3daf",
   "metadata": {},
   "source": [
    "### High Income Count Plot Against Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c894519c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['marital.status'],hue='High income', data=df, palette='magma');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd38740",
   "metadata": {},
   "source": [
    "### High Income Plot against Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1592404",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['sex'],hue='High income', data=df, palette='magma');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebac89f6",
   "metadata": {},
   "source": [
    "### High Income Plot against Race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "50c44396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEGCAYAAACkQqisAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAeNUlEQVR4nO3df5xVdb3v8dcbBDFRQR29xOiBasz4IaMgUl6NpBR5eEV9dBT7IahIppx+eS36YZpFl9vxR2pFoSJwHgp6jplcQ5FISzOEQVEQ9YCKOkRAYGooKvS5f6zv4BJnhmE5e2/GeT8fj/2YtT/ru9b67pnZ85611nevpYjAzMysiA6V7oCZmbVdDhEzMyvMIWJmZoU5RMzMrDCHiJmZFbZbpTtQbvvvv3/06tWr0t0wM2tTFi9e/LeIqNq+3u5CpFevXtTV1VW6G2ZmbYqk5xur+3CWmZkV5hAxM7PCHCJmZlZYuzsnYmYG8NZbb1FfX8/mzZsr3ZVdSpcuXaiurqZTp04tau8QMbN2qb6+nr322otevXohqdLd2SVEBBs2bKC+vp7evXu3aBkfzjKzdmnz5s3st99+DpAcSey33347tXfmEDGzdssB8m47+z1xiJiZWWEOETOzpGvXru94Pm3aNMaPHw/AL3/5S2bMmNHs8vn2zRk7dizLly8v3tFdiE+sN+OC/t+sdBd22i+W/qTSXTB7Xzr//PNbbV033HBDq62r0rwnYmbWApdddhlXXHEFAIsWLeKwww6jtraWiy++mH79+m1r95e//IXhw4dTU1PDN7/Z+D+iQ4cO3Xb5pa5du/Ld736XAQMGMGTIENauXQvA2rVrOfXUUxkwYAADBgzgoYceAuCqq66iX79+9OvXj5/+9KcArFq1ikMPPZQxY8ZwyCGH8PnPf57f/e53HH300dTU1LBw4UIANm3axDnnnMPgwYM5/PDDufPOO9/z98UhYmaWvP7669TW1m57fP/732+03dlnn82vfvUrlixZQseOHd8xb8mSJdx6660sXbqUW2+9lRdffLHZbW7atIkhQ4bw2GOPceyxx3L99dcD8JWvfIVPfvKTPPbYYzzyyCP07duXxYsXc9NNN/Hwww+zYMECrr/+eh599FEAVq5cyUUXXcRTTz3FU089xS233MKDDz7IFVdcwY9//GMAJk6cyHHHHcfChQu57777uPjii9m0adN7+p45RMzMkj322IMlS5Zse1x++eXvavP3v/+dV199lY9//OMAfO5zn3vH/GHDhrHPPvvQpUsX+vTpw/PPN3rdwm06d+7MSSedBMDAgQNZtWoVAL///e/58pe/DEDHjh3ZZ599ePDBBzn11FPZc8896dq1K6eddhoPPPAAAL1796Z///506NCBvn37MmzYMCTRv3//beu89957mTRpErW1tQwdOpTNmzfzwgsvFP5+QQlDRNJBku6TtFzSE5K+mur7SponaUX62j3VJelaSSslPS7piNy6Rqf2KySNztUHSlqalrlWHq9nZhW2++67b5vu2LEjW7ZsabZ9p06dtg2rbUn7lmy3Q4cO25536NBh2zojgttvv31bSL7wwgt87GMfK7S9bdt6T0s3bwtwUUT0AYYAF0rqA0wA5kdEDTA/PQc4EahJj3HAZMhCB7gUOAoYDFzaEDypzXm55YaX8PWYmdGtWzf22msvHn74YQBmzZpVku0MGzaMyZMnA7B161ZefvlljjnmGH7zm9/w2muvsWnTJu644w6OOeaYFq/zhBNO4LrrriMiALYdCnsvShYiEbEmIh5J068CTwI9gZHA9NRsOnBKmh4JzIjMAqCbpB7ACcC8iNgYES8B84Dhad7eEbEgsu/IjNy6zMxK5sYbb+S8886jtraWTZs2sc8++7T6Nq655hruu+8++vfvz8CBA1m+fDlHHHEEY8aMYfDgwRx11FGMHTuWww8/vMXrvOSSS3jrrbc47LDD6Nu3L5dccsl77qcaEqmUJPUC/gj0A16IiG6pLuCliOgm6S5gUkQ8mObNB74FDAW6RMSPUv0S4HXg/tT+06l+DPCtiDipke2PI9u74eCDDx64o2OUDTzE1+z968knnyx8KOcf//jHts+UTJo0iTVr1nDNNde0ZvcqqrHvjaTFETFo+7YlP7EuqStwO/C1iHglPy/tQZQ8xSJiSkQMiohBVVXvurujmdlO+e1vf0ttbS39+vXjgQce4Hvf+16lu1QxJf2woaROZAFyc0T8OpXXSuoREWvSIal1qb4aOCi3eHWqrSbbG8nX70/16kbam5mV1BlnnMEZZ5xR6W7sEko5OkvAjcCTEXFVbtZsoGGE1Wjgzlz9rDRKawjwckSsAeYCx0vqnk6oHw/MTfNekTQkbeus3LrMzKwMSrkncjTwRWCppCWp9h1gEnCbpHOB54HT07w5wAhgJfAacDZARGyU9ENgUWp3eURsTNMXANOAPYC708PMzMqkZCGSTpA39bmNYY20D+DCJtY1FZjaSL2O7GS9mZlVgD+xbmZmhfkqvmZm70FrfxSgpcP077nnHr761a+ydetWxo4dy4QJE3a8UAl4T8TMrI3ZunUrF154IXfffTfLly9n5syZFbs/iUPEzKyNWbhwIR/5yEf40Ic+ROfOnRk1alSrXNa9CIeImVkbs3r1ag466O2P1VVXV7N6dWU+JucQMTOzwhwiZmZtTM+ePd9xs6v6+np69uxZkb44RMzM2pgjjzySFStW8Nxzz/Hmm28ya9YsTj755Ir0xUN8zczeg0pcOXu33XbjZz/7GSeccAJbt27lnHPOoW/fvmXvBzhEzMzapBEjRjBixIhKd8OHs8zMrDiHiJmZFeYQMTOzwhwiZmZWmEPEzMwKc4iYmVlhJRviK2kqcBKwLiL6pdqtwEdTk27A3yOiVlIv4Eng6TRvQUScn5YZyNt3L5wDfDUiQtK+wK1AL2AVcHpEvFSq12Nm1pjnLvp6q66v95VX77DNOeecw1133cUBBxzAsmXLWnX7O6uUeyLTgOH5QkScERG1EVEL3A78Ojf7mYZ5DQGSTAbOA2rSo2GdE4D5EVEDzE/Pzcze98aMGcM999xT6W4AJQyRiPgjsLGxeZJEdm/1mc2tQ1IPYO+IWJBunzsDOCXNHglMT9PTc3Uzs/e1Y489ln333bfS3QAqd07kGGBtRKzI1XpLelTSHyQdk2o9gfpcm/pUAzgwItak6b8CBza1MUnjJNVJqlu/fn0rvQQzM6tUiJzJO/dC1gAHR8ThwDeAWyTt3dKVpb2UaGb+lIgYFBGDqqqqivbZzMy2U/ZrZ0naDTgNGNhQi4g3gDfS9GJJzwCHAKuB6tzi1akGsFZSj4hYkw57rStH/83M7G2V2BP5NPBURGw7TCWpSlLHNP0hshPoz6bDVa9IGpLOo5wFNNwDcjYwOk2PztXNzKxMSjnEdyYwFNhfUj1waUTcCIzi3SfUjwUul/QW8E/g/IhoOCl/AW8P8b07PQAmAbdJOhd4nuxEvZlZWbVkSG5rO/PMM7n//vv529/+RnV1NT/4wQ8499xzy94PKGGIRMSZTdTHNFK7nWzIb2Pt64B+jdQ3AMPeWy/NzNqemTObHdhaVv7EupmZFeYQMTOzwhwiZtZuZZ8OsLyd/Z44RMysXerSpQsbNmxwkOREBBs2bKBLly4tXsb3WDezdqm6upr6+np8FYt36tKlC9XV1TtumDhEzKxd6tSpE7179650N9o8H84yM7PCHCJmZlaYQ8TMzApziJiZWWEOETMzK8whYmZmhTlEzMysMIeImZkV5hAxM7PCHCJmZlZYyUJE0lRJ6yQty9Uuk7Ra0pL0GJGb921JKyU9LemEXH14qq2UNCFX7y3p4VS/VVLnUr0WMzNrXCn3RKYBwxupXx0RtekxB0BSH7Lb5vZNy/xCUsd03/WfAycCfYAzU1uA/5vW9RHgJaAy94Y0M2vHShYiEfFHYOMOG2ZGArMi4o2IeA5YCQxOj5UR8WxEvAnMAkZKEnAc8F9p+enAKa3ZfzMz27FKnBMZL+nxdLire6r1BF7MtalPtabq+wF/j4gt29UbJWmcpDpJdb7ss5lZ6yl3iEwGPgzUAmuAK8ux0YiYEhGDImJQVVVVOTZpZtYulPV+IhGxtmFa0vXAXenpauCgXNPqVKOJ+gagm6Td0t5Ivr2ZmZVJWfdEJPXIPT0VaBi5NRsYJWl3Sb2BGmAhsAioSSOxOpOdfJ8d2f0s7wM+m5YfDdxZjtdgZmZvK9meiKSZwFBgf0n1wKXAUEm1QACrgC8BRMQTkm4DlgNbgAsjYmtaz3hgLtARmBoRT6RNfAuYJelHwKPAjaV6LWZm1riShUhEnNlIuck/9BExEZjYSH0OMKeR+rNko7fMzKxC/Il1MzMrzCFiZmaFOUTMzKwwh4iZmRXmEDEzs8IcImZmVphDxMzMCnOImJlZYQ4RMzMrzCFiZmaFOUTMzKwwh4iZmRXmEDEzs8IcImZmVphDxMzMCnOImJlZYSULEUlTJa2TtCxX+3dJT0l6XNIdkrqlei9Jr0takh6/zC0zUNJSSSslXStJqb6vpHmSVqSv3Uv1WszMrHGl3BOZBgzfrjYP6BcRhwH/DXw7N++ZiKhNj/Nz9cnAeWT3Xa/JrXMCMD8iaoD56bmZmZVRyUIkIv4IbNyudm9EbElPFwDVza1DUg9g74hYEBEBzABOSbNHAtPT9PRc3czMyqSS50TOAe7OPe8t6VFJf5B0TKr1BOpzbepTDeDAiFiTpv8KHNjUhiSNk1QnqW79+vWt1H0zM6tIiEj6LrAFuDmV1gAHR8ThwDeAWyTt3dL1pb2UaGb+lIgYFBGDqqqq3kPPzcwsb7dyb1DSGOAkYFj6409EvAG8kaYXS3oGOARYzTsPeVWnGsBaST0iYk067LWuTC/BzMySsu6JSBoOfBM4OSJey9WrJHVM0x8iO4H+bDpc9YqkIWlU1lnAnWmx2cDoND06VzczszIp2Z6IpJnAUGB/SfXApWSjsXYH5qWRugvSSKxjgcslvQX8Ezg/IhpOyl9ANtJrD7JzKA3nUSYBt0k6F3geOL1Ur8XMzBrXohCRND8ihu2olhcRZzZSvrGJtrcDtzcxrw7o10h9A9Dk9s3MrPSaDRFJXYAPkO1NdAeUZu3N26OkzMysndrRnsiXgK8BHwQW83aIvAL8rHTdMjOztqDZEImIa4BrJP1bRFxXpj6ZmVkb0aJzIhFxnaRPAL3yy0TEjBL1y8zM2oCWnlj/D+DDwBJgayo3XIbEzMzaqZYO8R0E9Gn4cKCZmRm0/MOGy4D/UcqOmJlZ29PSPZH9geWSFpIuTwIQESeXpFdmZtYmtDRELitlJ8zMrG1q6eisP5S6I2Zm1va0dHTWq7x9qfXOQCdgU0S0+HLtZmb2/tPSPZG9GqbT1XRHAkNK1SkzM2sbdvpS8JH5DXBC63fHzMzakpYezjot97QD2edGNpekR2Zm1ma0dHTW/8pNbwFWkR3SMjOzdqyl50TOLnVHzMys7WnRORFJ1ZLukLQuPW6XVN2C5aam9stytX0lzZO0In3tnuqSdK2klZIel3REbpnRqf0KSaNz9YGSlqZlrk0n/c3MrExaemL9JrJ7mn8wPf5fqu3INGD4drUJwPyIqAHmp+cAJ5LdW70GGAdMhix0yG6texQwGLi0IXhSm/Nyy22/LTMzK6GWhkhVRNwUEVvSYxpQtaOFIuKPwMbtyiOB6Wl6OnBKrj4jjf5aAHST1INsFNi8iNgYES8B84Dhad7eEbEgXRhyRm5dZmZWBi0NkQ2SviCpY3p8AdhQcJsHRsSaNP1X4MA03RN4MdeuPtWaq9c3Un8XSeMk1UmqW79+fcFum5nZ9loaIucAp5P90V8DfBYY8143nvYgSn55+YiYEhGDImJQVdUOd6DMzKyFWhoilwOjI6IqIg4gC5UfFNzm2nQoivR1XaqvBg7KtatOtebq1Y3UzcysTFoaIoel8xEARMRG4PCC25wNNIywGg3cmauflUZpDQFeToe95gLHS+qeTqgfD8xN816RNCSNyjorty4zMyuDln7YsIOk7g1BkkZM7XBZSTOBocD+kurJRllNAm6TdC7wPNlhMoA5wAhgJfAacDZkgSXph8Ci1O7yFGIAF5CNANsDuDs9zMysTFoaIlcCf5b0n+n5vwITd7RQRJzZxKxhjbQN4MIm1jMVmNpIvQ7ot6N+mJlZabT0E+szJNUBx6XSaRGxvHTdMjOztqCleyKk0HBwmJnZNjt9KXgzM7MGDhEzMyvMIWJmZoU5RMzMrDCHiJmZFeYQMTOzwhwiZmZWmEPEzMwKc4iYmVlhDhEzMyvMIWJmZoU5RMzMrDCHiJmZFeYQMTOzwsoeIpI+KmlJ7vGKpK9JukzS6lx9RG6Zb0taKelpSSfk6sNTbaWkCeV+LWZm7V2L7yfSWiLiaaAWQFJHYDVwB9ntcK+OiCvy7SX1AUYBfYEPAr+TdEia/XPgM0A9sEjSbN8sy8ysfMoeItsZBjwTEc9LaqrNSGBWRLwBPCdpJTA4zVsZEc8CSJqV2jpEzMzKpNLnREYBM3PPx0t6XNJUSd1TrSfwYq5Nfao1VX8XSeMk1UmqW79+fev13sysnatYiEjqDJwM/GcqTQY+THaoaw1wZWttKyKmRMSgiBhUVVXVWqs1M2v3Knk460TgkYhYC9DwFUDS9cBd6elq4KDcctWpRjN1MzMrg0oezjqT3KEsST1y804FlqXp2cAoSbtL6g3UAAuBRUCNpN5pr2ZUamtmZmVSkT0RSXuSjar6Uq78E0m1QACrGuZFxBOSbiM7Yb4FuDAitqb1jAfmAh2BqRHxRLlew67quYu+Xuku7JTeV15d6S6Y2XtQkRCJiE3AftvVvthM+4nAxEbqc4A5rd5BMzNrkUqPzjIzszbMIWJmZoU5RMzMrDCHiJmZFeYQMTOzwhwiZmZWmEPEzMwKc4iYmVlhDhEzMyvMIWJmZoU5RMzMrDCHiJmZFeYQMTOzwhwiZmZWmEPEzMwKc4iYmVlhFQsRSaskLZW0RFJdqu0raZ6kFelr91SXpGslrZT0uKQjcusZndqvkDS6Uq/HzKw9qvSeyKciojYiBqXnE4D5EVEDzE/PAU4ku7d6DTAOmAxZ6ACXAkcBg4FLG4LHzMxKr9Ihsr2RwPQ0PR04JVefEZkFQDdJPYATgHkRsTEiXgLmAcPL3Gczs3arkiESwL2SFksal2oHRsSaNP1X4MA03RN4Mbdsfao1VX8HSeMk1UmqW79+fWu+BjOzdm23Cm77f0bEakkHAPMkPZWfGREhKVpjQxExBZgCMGjQoFZZp5mZVXBPJCJWp6/rgDvIzmmsTYepSF/XpeargYNyi1enWlN1MzMrg4qEiKQ9Je3VMA0cDywDZgMNI6xGA3em6dnAWWmU1hDg5XTYay5wvKTu6YT68almZmZlUKnDWQcCd0hq6MMtEXGPpEXAbZLOBZ4HTk/t5wAjgJXAa8DZABGxUdIPgUWp3eURsbF8L8PMrH2rSIhExLPAgEbqG4BhjdQDuLCJdU0FprZ2H83MbMd2tSG+ZmbWhjhEzMysMIeImZkV5hAxM7PCHCJmZlaYQ8TMzApziJiZWWEOETMzK8whYmZmhTlEzMysMIeImZkV5hAxM7PCHCJmZlaYQ8TMzApziJiZWWEOETMzK6zsN6WSdBAwg+zuhgFMiYhrJF0GnAesT02/ExFz0jLfBs4FtgJfiYi5qT4cuAboCNwQEZPK+Vps13ZB/29Wugs75RdLf1LpLpjttErc2XALcFFEPJLus75Y0rw07+qIuCLfWFIfYBTQF/gg8DtJh6TZPwc+A9QDiyTNjojlZXkVZmZW/hCJiDXAmjT9qqQngZ7NLDISmBURbwDPSVoJDE7zVqZb7SJpVmrrEDEzK5OKnhOR1As4HHg4lcZLelzSVEndU60n8GJusfpUa6re2HbGSaqTVLd+/frGmpiZWQEVCxFJXYHbga9FxCvAZODDQC3ZnsqVrbWtiJgSEYMiYlBVVVVrrdbMrN2rxDkRJHUiC5CbI+LXABGxNjf/euCu9HQ1cFBu8epUo5m6mZmVQdn3RCQJuBF4MiKuytV75JqdCixL07OBUZJ2l9QbqAEWAouAGkm9JXUmO/k+uxyvwczMMpXYEzka+CKwVNKSVPsOcKakWrJhv6uALwFExBOSbiM7Yb4FuDAitgJIGg/MJRviOzUinijfyzAzs0qMznoQUCOz5jSzzERgYiP1Oc0tZ2ZmpeVPrJuZWWEOETMzK8whYmZmhTlEzMysMIeImZkV5hAxM7PCKvKJdTOz5rS1y/hD+72Uv/dEzMysMIeImZkV5hAxM7PCHCJmZlaYQ8TMzApziJiZWWEOETMzK8whYmZmhfnDhma7iOcu+nqlu7DTel95daW7sMtoaz+/1vrZtfk9EUnDJT0taaWkCZXuj5lZe9KmQ0RSR+DnwIlAH7Jb7PapbK/MzNqPNh0iwGBgZUQ8GxFvArOAkRXuk5lZu6GIqHQfCpP0WWB4RIxNz78IHBUR47drNw4Yl55+FHi6rB0tr/2Bv1W6E1aIf3Zt2/v95/cvEVG1fbFdnFiPiCnAlEr3oxwk1UXEoEr3w3aef3ZtW3v9+bX1w1mrgYNyz6tTzczMyqCth8gioEZSb0mdgVHA7Ar3ycys3WjTh7MiYouk8cBcoCMwNSKeqHC3Kq1dHLZ7n/LPrm1rlz+/Nn1i3czMKqutH84yM7MKcoiYmVlhDpFdkKSrJX0t93yupBtyz6+U9A1JdzWx/A0Nn9yX9J2Sd9gaJWmrpCWSHpP0iKRPpHovScsKrvN+Se1uGGm5SaqWdKekFZKekXSNpM6SaiWNyLW7TNL/rmRfK80hsmv6E9DwB6cD2YeY+ubmfwLo3NTCETE2Ipanpw6Rynk9ImojYgDwbeD/VLpDtmOSBPwa+E1E1ACHAF2BiUAtMKLppXd6Wx1ba12V4hDZNT0EfDxN9wWWAa9K6i5pd+BjwCNAV0n/JekpSTenX/5t/61KmgTskf4bvjnN+4Kkhan2q/fDL3EbsTfw0vbFtFfyQNpT2ba3kuZ9S9LStCczabvlOkiaJulHZeh7e3McsDkibgKIiK3A14GxwE+AM9L754zUvk96zz0r6SsNK2nqvSbpH+lowmO8/T5vs9r0EN/3q4j4i6Qtkg4m2+v4M9CT7BfuZWAp8CZwOFnI/IVs7+Vo4MHceiZIGh8RtQCSPgacARwdEW9J+gXweWBGuV5bO7OHpCVAF6AH2R+n7a0DPhMRmyXVADOBQZJOJLsO3FER8ZqkfXPL7AbcDCyLiIklfQXtU19gcb4QEa9IWgXcBBzScGklSZcBhwKfAvYCnpY0GfgITb/X9gQejoiLyvNySsshsut6iCxAPgFcRRYinyALkT+lNgsjoh4g/bHqRS5EGjEMGAgsSjste5D9EbPSeD0X4B8HZkjqt12bTsDPJNUCW8kOnQB8GrgpIl4DiIiNuWV+BdzmANll/DYi3gDekLQOOJDm32tbgdsr0dFScIjsuhrOi/QnO5z1InAR8ArZf0MAb+Tab2XHP08B0yPi263bVduRiPizpP2B7S9g93VgLTCA7PDy5has7iHgU5KujIiWtLedsxz4bL4gaW/gYGBLI+0bex82917bnA6RvS/4nMiu6yHgJGBjRGxN/4l2Izuk9dBOrOctSZ3S9Hzgs5IOAJC0r6R/acU+WxMkHUp2VYUN283aB1gTEf8EvpjaAMwDzpb0gbR8/nDWjcAc4DZJ/kew9c0HPiDpLNh28vtKYBpZ4O/VwnW0i/eaQ2TXtZRsVNaC7WovR8TOXG56CvC4pJvTiK3vAfdKepzsD1WP1uqwvUvDoIYlwK3A6Eb+A/0FMDqdZD0U2AQQEfeQXQeuLi3/jmGkEXEV8CjwH2kEn7WSyC7jcSrwr5JWAP9Ntof4HeA+shPp+RPrja2j3bzXfNkTMzMrzP/BmJlZYQ4RMzMrzCFiZmaFOUTMzKwwh4iZmRXmEDEzs8IcImZlpIzfd/a+4V9msxJLV+p9WtIMskvY3CipTtITkn6Qa3ekpIfSVXsXStpLUkdJ/y5pkaTHJX2pcq/E7N18yQSz8qgh+8T6Akn7RsTGdDmN+ZIOA54i+1T7GRGxKF2r6XXgXLKrFByZbgPwJ0n3RsRzFXslZjkOEbPyeD4iGi5hc7qkcWTvvx5AHyDIrqG1CLJLjwNIOh44TFLDBQH3IQskh4jtEhwiZuWxCUBSb7LrYB0ZES9JmkZ2v5GmCPi3iJhb+i6a7TyfEzErr73JAuVlSQcCJ6b600APSUcCpPMhuwFzgS83XIlZ0iGS9qxAv80a5T0RszKKiMckPUp2DuRF0g3GIuLNdFXY6yTtQXY+5NPADWQ3G3tE2d2N1gOnVKDrZo3yVXzNzKwwH84yM7PCHCJmZlaYQ8TMzApziJiZWWEOETMzK8whYmZmhTlEzMyssP8PG5FTeNq2d6QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['race'],hue='High income', data=df, palette='magma');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544f3ca9",
   "metadata": {},
   "source": [
    "### High Income Plot against Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a1540f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=df['education'],hue='High income', data=df, palette='seismic');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ae0ad14",
   "metadata": {},
   "source": [
    "# Encoding(Using `LabelEncoder`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc60cdac",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoded_df = df.copy()\n",
    "encoded_df = encoded_df.apply(LabelEncoder().fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "eddc3d7f",
   "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>High income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>72</td>\n",
       "      <td>3</td>\n",
       "      <td>2649</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>3</td>\n",
       "      <td>6514</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>11175</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>3</td>\n",
       "      <td>7009</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>16850</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  workclass  fnlwgt  education  education.num  marital.status  \\\n",
       "0   72          3    2649          1              8               2   \n",
       "1   65          3    6514          1              8               2   \n",
       "2   49          3   11175          1              9               2   \n",
       "3   37          3    7009          5              3               0   \n",
       "4   24          3   16850          1              9               2   \n",
       "\n",
       "   occupation  relationship  race  sex  capital.gain  capital.loss  \\\n",
       "0           9             1     2    0             0            91   \n",
       "1           3             1     2    0             0            91   \n",
       "2           9             4     0    0             0            91   \n",
       "3           6             4     2    0             0            90   \n",
       "4           9             3     2    0             0            90   \n",
       "\n",
       "   hours.per.week  High income  \n",
       "0              39            0  \n",
       "1              17            0  \n",
       "2              39            0  \n",
       "3              39            0  \n",
       "4              39            0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "32a87cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 14 columns):\n",
      " #   Column          Non-Null Count  Dtype\n",
      "---  ------          --------------  -----\n",
      " 0   age             32561 non-null  int64\n",
      " 1   workclass       32561 non-null  int32\n",
      " 2   fnlwgt          32561 non-null  int64\n",
      " 3   education       32561 non-null  int32\n",
      " 4   education.num   32561 non-null  int64\n",
      " 5   marital.status  32561 non-null  int32\n",
      " 6   occupation      32561 non-null  int32\n",
      " 7   relationship    32561 non-null  int32\n",
      " 8   race            32561 non-null  int32\n",
      " 9   sex             32561 non-null  int32\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  High income     32561 non-null  int64\n",
      "dtypes: int32(7), int64(7)\n",
      "memory usage: 2.6 MB\n"
     ]
    }
   ],
   "source": [
    "encoded_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd49ff5",
   "metadata": {},
   "source": [
    "# Preprocessing (Using `sklearn.preprocessing.StandardScaler`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25a23da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from numpy import ndarray\n",
    "\n",
    "AREA_OF_TRAIN = encoded_df.drop(['High income'], axis=1)\n",
    "\n",
    "ss = StandardScaler().fit(AREA_OF_TRAIN)\n",
    "X = ss.transform(AREA_OF_TRAIN) # returning numpy.ndarray\n",
    "y = df['High income']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d38e2bfd",
   "metadata": {},
   "source": [
    "### Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b7974c83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "TEST_SIZE = 0.3 # 70/30 \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74259ec5",
   "metadata": {},
   "source": [
    "# Classifier Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d8f4c223",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Common Utility Function Required Later\n",
    "\n",
    "### IMPORTS FOR ALL MODELS HERE:\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from numpy import ndarray\n",
    "\n",
    "\n",
    "def get_confusion_mt_values(matrix: ndarray) -> dict:\n",
    "    try:\n",
    "        tn = matrix[0][0]\n",
    "        tp = matrix[1][1]\n",
    "        fn = matrix[1][0]\n",
    "        fp = matrix[0][1]\n",
    "        return {\"tp\": tp, \"tn\": tn, \"fp\": fp, \"fn\": fn}\n",
    "    except Exception as e:\n",
    "        return e\n",
    "    \n",
    "def get_plotly_fig_cm(cm_values: dict) -> None:\n",
    "    fig = go.Figure(data=[go.Pie(labels=['True Positive','True Negative','False Positive','False Negative'],\n",
    "                             values=[v for k,v in cm_values.items()])])\n",
    "    fig.update(layout_title_text=\"Confusion Matrix Evaluation:\")\n",
    "    fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=14,insidetextorientation='radial',\n",
    "                      marker=dict(colors=colors, line=dict(color='#000000', width=2)))\n",
    "    fig.show()\n",
    "    fig.write_image(\"fig1.jpg\")\n",
    "\n",
    "def get_pie_chart_cm(cm_values: dict) -> None:\n",
    "    labels=['True Positive','True Negative','False Positive','False Negative']\n",
    "    colors = sns.color_palette('bright')[0:5]\n",
    "    values = [v for k, v in cm_values.items()]\n",
    "    #create pie chart\n",
    "    plt.pie(values, labels = labels, colors = colors, autopct='%.0f%%')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f87626a5",
   "metadata": {},
   "source": [
    "## RandomForestClassifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6325d28e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max-Depth : 1   ||   Accuracy : 0.775104923738356\n",
      "Max-Depth : 2   ||   Accuracy : 0.8031528303818201\n",
      "Max-Depth : 3   ||   Accuracy : 0.8309960077797113\n",
      "Max-Depth : 4   ||   Accuracy : 0.8428703040229297\n",
      "Max-Depth : 5   ||   Accuracy : 0.8474767120483161\n",
      "Max-Depth : 6   ||   Accuracy : 0.8534138601699253\n",
      "Max-Depth : 7   ||   Accuracy : 0.8537209540382844\n",
      "Max-Depth : 8   ||   Accuracy : 0.8533114955471389\n",
      "Max-Depth : 9   ||   Accuracy : 0.8550516941345071\n",
      "Max-Depth : 10   ||   Accuracy : 0.8567918927218753\n",
      "Max-Depth : 11   ||   Accuracy : 0.8580202681953117\n",
      "Max-Depth : 12   ||   Accuracy : 0.8566895280990889\n",
      "Max-Depth : 13   ||   Accuracy : 0.8584297266864571\n",
      "Max-Depth : 14   ||   Accuracy : 0.8598628314054663\n",
      "Max-Depth : 15   ||   Accuracy : 0.8608864776333299\n",
      "Max-Depth : 16   ||   Accuracy : 0.8615006653700481\n",
      "Max-Depth : 17   ||   Accuracy : 0.8607841130105436\n",
      "Max-Depth : 18   ||   Accuracy : 0.8608864776333299\n",
      "Max-Depth : 19   ||   Accuracy : 0.8618077592384072\n"
     ]
    }
   ],
   "source": [
    "### Search for tuned max_depth [RandomForestClassifier Model]\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "for max_depth in range(1,20):\n",
    "    rfc = RandomForestClassifier(criterion='gini',max_depth=max_depth)\n",
    "    rfc = rfc.fit(X_train, y_train)\n",
    "    rfc_prediction = rfc.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, rfc_prediction)\n",
    "    print(f\"Max-Depth : {max_depth}   ||   Accuracy : {accuracy}\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0b4b1074",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfc = RandomForestClassifier(criterion='gini',max_depth=17)\n",
    "rfc = rfc.fit(X_train, y_train)\n",
    "rfc_prediction = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "27400f5a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1457\n",
      "\n",
      "TN => 6972\n",
      "\n",
      "FP => 410\n",
      "\n",
      "FN => 930\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.88231   0.94446   0.91233      7382\n",
      "           1    0.78040   0.61039   0.68500      2387\n",
      "\n",
      "    accuracy                        0.86283      9769\n",
      "   macro avg    0.83135   0.77742   0.79866      9769\n",
      "weighted avg    0.85741   0.86283   0.85678      9769\n",
      "\n",
      "Accuracy Found --> 0.8628314054662709\n",
      "\n",
      "Precision(R0) --> 0.9444594960715254\n",
      "\n",
      "Precision(R1) --> 0.7803963577932512\n",
      "\n",
      "Recall (R0) --> 0.9444594960715254\n",
      "\n",
      "Recall (R1) --> 0.6103896103896104\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,rfc_prediction)\n",
    "accuracy = accuracy_score(y_test, rfc_prediction)\n",
    "cr = classification_report(y_test, rfc_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4dd17ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef80a9f",
   "metadata": {},
   "source": [
    "## Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a52fb1de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 0.8232162964479476\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr = lr.fit(X_train, y_train)\n",
    "lr_prediction = lr.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, lr_prediction)\n",
    "print(f\"Accuracy : {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f240dccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1122\n",
      "\n",
      "TN => 6920\n",
      "\n",
      "FP => 462\n",
      "\n",
      "FN => 1265\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.84545   0.93742   0.88906      7382\n",
      "           1    0.70833   0.47005   0.56510      2387\n",
      "\n",
      "    accuracy                        0.82322      9769\n",
      "   macro avg    0.77689   0.70373   0.72708      9769\n",
      "weighted avg    0.81195   0.82322   0.80990      9769\n",
      "\n",
      "Accuracy Found --> 0.8232162964479476\n",
      "\n",
      "Precision(R0) --> 0.93741533459767\n",
      "\n",
      "Precision(R1) --> 0.7083333333333334\n",
      "\n",
      "Recall (R0) --> 0.93741533459767\n",
      "\n",
      "Recall (R1) --> 0.4700460829493088\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,lr_prediction)\n",
    "accuracy = accuracy_score(y_test, lr_prediction)\n",
    "cr = classification_report(y_test, lr_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e09eaf9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29372025",
   "metadata": {},
   "source": [
    "## DecisionTree Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2afef1e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max-Depth : 1   ||   Accuracy : 0.7556556454089467\n",
      "Max-Depth : 2   ||   Accuracy : 0.8238304841846658\n",
      "Max-Depth : 3   ||   Accuracy : 0.8385709898659024\n",
      "Max-Depth : 4   ||   Accuracy : 0.8418466577950661\n",
      "Max-Depth : 5   ||   Accuracy : 0.8451223257242297\n",
      "Max-Depth : 6   ||   Accuracy : 0.8428703040229297\n",
      "Max-Depth : 7   ||   Accuracy : 0.8483979936533934\n",
      "Max-Depth : 8   ||   Accuracy : 0.8489098167673252\n",
      "Max-Depth : 9   ||   Accuracy : 0.8517760262053434\n",
      "Max-Depth : 10   ||   Accuracy : 0.8502405568635479\n",
      "Max-Depth : 11   ||   Accuracy : 0.8491145460128979\n",
      "Max-Depth : 12   ||   Accuracy : 0.8485003582761798\n",
      "Max-Depth : 13   ||   Accuracy : 0.847169618179957\n",
      "Max-Depth : 14   ||   Accuracy : 0.8427679394001433\n",
      "Max-Depth : 15   ||   Accuracy : 0.8372402497696796\n",
      "Max-Depth : 16   ||   Accuracy : 0.8342716757088751\n",
      "Max-Depth : 17   ||   Accuracy : 0.8348858634455932\n",
      "Max-Depth : 18   ||   Accuracy : 0.8325314771215068\n",
      "Max-Depth : 19   ||   Accuracy : 0.8310983724024977\n"
     ]
    }
   ],
   "source": [
    "### Search for tuned max_depth [DecisionTree Classifier Model]\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "for max_depth in range(1,20):\n",
    "    dt = DecisionTreeClassifier(criterion='entropy',max_depth=max_depth)\n",
    "    dt = dt.fit(X_train, y_train)\n",
    "    dt_prediction = dt.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, dt_prediction)\n",
    "    print(f\"Max-Depth : {max_depth}   ||   Accuracy : {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2470d996",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = DecisionTreeClassifier(criterion='gini',max_depth=9)\n",
    "dt = dt.fit(X_train, y_train)\n",
    "dt_prediction = dt.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f3b3d5ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1302\n",
      "\n",
      "TN => 7007\n",
      "\n",
      "FP => 375\n",
      "\n",
      "FN => 1085\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.86592   0.94920   0.90565      7382\n",
      "           1    0.77639   0.54545   0.64075      2387\n",
      "\n",
      "    accuracy                        0.85055      9769\n",
      "   macro avg    0.82115   0.74733   0.77320      9769\n",
      "weighted avg    0.84404   0.85055   0.84092      9769\n",
      "\n",
      "Accuracy Found --> 0.8505476507319071\n",
      "\n",
      "Precision(R0) --> 0.9492007586020049\n",
      "\n",
      "Precision(R1) --> 0.776386404293381\n",
      "\n",
      "Recall (R0) --> 0.9492007586020049\n",
      "\n",
      "Recall (R1) --> 0.5454545454545454\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,dt_prediction)\n",
    "accuracy = accuracy_score(y_test, dt_prediction)\n",
    "cr = classification_report(y_test, dt_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aaaa7dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862a626a",
   "metadata": {},
   "source": [
    "## GaussianNB Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "637febb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9398913a",
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb = dt.fit(X_train, y_train)\n",
    "gnb_prediction = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "abdb95ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1306\n",
      "\n",
      "TN => 6997\n",
      "\n",
      "FP => 385\n",
      "\n",
      "FN => 1081\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.86618   0.94785   0.90517      7382\n",
      "           1    0.77232   0.54713   0.64051      2387\n",
      "\n",
      "    accuracy                        0.84993      9769\n",
      "   macro avg    0.81925   0.74749   0.77284      9769\n",
      "weighted avg    0.84325   0.84993   0.84051      9769\n",
      "\n",
      "Accuracy Found --> 0.8499334629951889\n",
      "\n",
      "Precision(R0) --> 0.947846112164725\n",
      "\n",
      "Precision(R1) --> 0.7723240685984625\n",
      "\n",
      "Recall (R0) --> 0.947846112164725\n",
      "\n",
      "Recall (R1) --> 0.547130289065773\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,gnb_prediction)\n",
    "accuracy = accuracy_score(y_test, gnb_prediction)\n",
    "cr = classification_report(y_test, gnb_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e15d0ece",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beffc554",
   "metadata": {},
   "source": [
    "## SVM Classifier Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d09d3817",
   "metadata": {},
   "source": [
    "### SVM With  polynomial Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4b08b02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svc_ploy = SVC(kernel='poly')\n",
    "svc_ploy.fit(X_train, y_train)\n",
    "svc_prediction = svc_ploy.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "17dc44b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1254\n",
      "\n",
      "TN => 6982\n",
      "\n",
      "FP => 400\n",
      "\n",
      "FN => 1133\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.86038   0.94581   0.90108      7382\n",
      "           1    0.75816   0.52535   0.62064      2387\n",
      "\n",
      "    accuracy                        0.84308      9769\n",
      "   macro avg    0.80927   0.73558   0.76086      9769\n",
      "weighted avg    0.83541   0.84308   0.83255      9769\n",
      "\n",
      "Accuracy Found --> 0.8430750332685024\n",
      "\n",
      "Precision(R0) --> 0.9458141425088052\n",
      "\n",
      "Precision(R1) --> 0.7581620314389359\n",
      "\n",
      "Recall (R0) --> 0.9458141425088052\n",
      "\n",
      "Recall (R1) --> 0.5253456221198156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,svc_prediction)\n",
    "accuracy = accuracy_score(y_test, svc_prediction)\n",
    "cr = classification_report(y_test, svc_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "83a394c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83583b89",
   "metadata": {},
   "source": [
    "### SVM With rbf kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5122e4b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_rbf = SVC(kernel='rbf')\n",
    "svc_rbf.fit(X_train, y_train)\n",
    "svc_rbf_prediction = svc_rbf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b8cd6ec7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP => 1254\n",
      "\n",
      "TN => 6982\n",
      "\n",
      "FP => 400\n",
      "\n",
      "FN => 1133\n",
      "\n",
      "Classification Report -->                precision    recall  f1-score   support\n",
      "\n",
      "           0    0.86322   0.94812   0.90368      7382\n",
      "           1    0.76942   0.53540   0.63142      2387\n",
      "\n",
      "    accuracy                        0.84727      9769\n",
      "   macro avg    0.81632   0.74176   0.76755      9769\n",
      "weighted avg    0.84030   0.84727   0.83716      9769\n",
      "\n",
      "Accuracy Found --> 0.8472719828027434\n",
      "\n",
      "Precision(R0) --> 0.9458141425088052\n",
      "\n",
      "Precision(R1) --> 0.7581620314389359\n",
      "\n",
      "Recall (R0) --> 0.9458141425088052\n",
      "\n",
      "Recall (R1) --> 0.5253456221198156\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,svc_prediction)\n",
    "accuracy = accuracy_score(y_test, svc_rbf_prediction)\n",
    "cr = classification_report(y_test, svc_rbf_prediction, digits=5)\n",
    "\n",
    "\n",
    "\n",
    "y_true=[\"0\", \"1\"]\n",
    "    \n",
    "fmt_cm = get_confusion_mt_values(cm)\n",
    "\n",
    "\n",
    "for k, v in fmt_cm.items():\n",
    "    print(f\"{k.upper()} => {v}\")\n",
    "    print()\n",
    "\n",
    "print(\"Classification Report --> \", cr)\n",
    "print(f\"Accuracy Found --> {accuracy}\\n\")    \n",
    "print(f\"Precision(R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # Precision0 = tn/tn+fp\n",
    "print(f\"Precision(R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fp'))}\\n\") # Precision1 = tp/tp+fn\n",
    "print(f\"Recall (R0) --> {fmt_cm.get('tn')/(fmt_cm.get('tn')+fmt_cm.get('fp'))}\\n\") # recall0 = tn/tn+fp\n",
    "print(f\"Recall (R1) --> {fmt_cm.get('tp')/(fmt_cm.get('tp')+fmt_cm.get('fn'))}\\n\") # recall1 = tp/tp+fn\n",
    "\n",
    "df_cm = pd.DataFrame(cm, columns=np.unique(y_true), index=np.unique(y_true))\n",
    "\n",
    "# seaborn Confusion matrix plot\n",
    "sns.set(font_scale=2)\n",
    "sns.heatmap(data=df_cm, cmap=\"Blues\", annot=True, annot_kws={\"size\": 24}, fmt=\"d\")\n",
    "plt.savefig('Cm.pdf', format='pdf', dpi=300)\n",
    "plt.savefig('Cm.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7f06d4fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie Chart Confution Matrix Figure\n",
    "get_pie_chart_cm(fmt_cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4116ff23",
   "metadata": {},
   "source": [
    "# Validation of all base models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4573c733",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "\n",
    "models = []\n",
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350fbb77",
   "metadata": {},
   "source": [
    "## Append all the base models to the models list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "2393776c",
   "metadata": {},
   "outputs": [],
   "source": [
    "models.append(('LR', LogisticRegression(random_state = 40)))\n",
    "models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier(criterion='gini',max_depth=9, random_state = 40)))\n",
    "models.append(('RF', RandomForestClassifier(random_state = 40, max_features = 7, min_samples_split = 2, n_estimators = 500)))\n",
    "models.append(('SVM', SVC(gamma='auto', random_state = 40)))\n",
    "models.append(('XGB', GradientBoostingClassifier(random_state = 40, learning_rate = 0.1, max_depth = 5, min_samples_split = 0.1, n_estimators = 100, subsample = 1.0)))\n",
    "models.append((\"LightGBM\", LGBMClassifier(random_state = 40, learning_rate = 0.01,  max_depth = 3, n_estimators = 1000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a80bf65",
   "metadata": {},
   "source": [
    "## Evaluate each model in turn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "9fdde728",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR\t==> \tMean: 0.805\tSTD: 0.086\n",
      "KNN\t==> \tMean: 0.811\tSTD: 0.052\n",
      "CART\t==> \tMean: 0.806\tSTD: 0.064\n",
      "RF\t==> \tMean: 0.806\tSTD: 0.074\n",
      "SVM\t==> \tMean: 0.806\tSTD: 0.101\n",
      "XGB\t==> \tMean: 0.809\tSTD: 0.074\n",
      "LightGBM\t==> \tMean: 0.799\tSTD: 0.084\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        kfold = KFold(n_splits = 10)\n",
    "        cv_results = cross_val_score(model, X, y, cv = 10, scoring= \"accuracy\")\n",
    "        results.append(cv_results)\n",
    "        names.append(name)\n",
    "        msg = f\"{name}\\t==> \\tMean: {cv_results.mean():.3f}\\tSTD: {cv_results.std():.3f}\"\n",
    "        print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b0b740",
   "metadata": {},
   "source": [
    "# Comparison Boxchart for all models accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "71f0cb4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "sns.set(rc = {'figure.figsize':(15,8)})\n",
    "colors = ['#78C850', '#F08030', '#6890F0','#F8D030', '#F85888', '#705898', '#98D8D8']\n",
    "\n",
    "param = {'fontsize': 20,\n",
    "        'color':'indigo'}\n",
    "\n",
    "g = sns.boxplot(data=results, palette=colors)\n",
    "g.set_title(\"Comparison Of Models\", fontdict=param)\n",
    "g.set_xticks(range(len(results)))\n",
    "g.set_xticklabels(names);\n",
    "g.set_xlabel(\"Algorithm Models\", fontsize = 16);\n",
    "g.set_ylabel(\"Accuracy\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "932b1d71",
   "metadata": {},
   "source": [
    "# Reporting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5849119d",
   "metadata": {},
   "source": [
    "The aim of this study was to create classification models for the highest salaried data set and to predict whether a person is able to get high salary by establishing models and to obtain maximum validation scores in the established models. The work done is as follows:\n",
    "\n",
    "1) adult Data Set read.\n",
    "\n",
    "2) With Exploratory Data Analysis; The data set's structural data were checked. The types of variables in the dataset were examined. Size information of the dataset was accessed. The ? values in the data set are missing values. Primarily these 0 values were replaced with the mode value of their column.\n",
    "\n",
    "3) Data Preprocessing section; df for: The ? values missing observations were filled with the mode values of that column highest mode. The X variables were standardized with the StandardScaler method..\n",
    "\n",
    "4) During Model Building; Logistic Regression, KNN, SVM, CART, Random Forests, LightGBM like using machine learning models Cross Validation Score were calculated. Later Random Forests, LightGBM hyperparameter optimizations optimized to increase Cross Validation value.\n",
    "\n",
    "5) **_Result: The model created as a result of XGB-GradientBoostingClassifier became the model with the lowest Cross Validation Score value. (0.85)_**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}