06 - descriptive-feature-engineering-combined_wt_handling_class_inbalance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61e88c3b",
   "metadata": {},
   "source": [
    "#### Loading data sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c115d967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.python.org/simple, https://cognite.jfrog.io/cognite/api/pypi/snakepit/simple\r\n",
      "Requirement already satisfied: imblearn in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (0.0)\r\n",
      "Requirement already satisfied: imbalanced-learn in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imblearn) (0.10.1)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.9.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (2.2.0)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.0.2)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.2.0)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn->imblearn) (1.21.5)\r\n",
      "Looking in indexes: https://pypi.python.org/simple, https://cognite.jfrog.io/cognite/api/pypi/snakepit/simple\r\n",
      "Requirement already satisfied: imbalanced-learn in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (0.10.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn) (2.2.0)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.9.1)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.21.5)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.2.0)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from imbalanced-learn) (1.0.2)\r\n",
      "Looking in indexes: https://pypi.python.org/simple, https://cognite.jfrog.io/cognite/api/pypi/snakepit/simple\r\n",
      "Requirement already satisfied: patsy in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (0.5.2)\r\n",
      "Requirement already satisfied: six in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from patsy) (1.16.0)\r\n",
      "Requirement already satisfied: numpy>=1.4 in /home/priyanka/etc/anaconda3/lib/python3.9/site-packages (from patsy) (1.21.5)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn\n",
    "!pip install imbalanced-learn\n",
    "!pip install patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "7eb156bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# This Jupyter Notebook does feature engineering joining all features in all tables.\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, r2_score\n",
    "from datetime import datetime\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score,classification_report,roc_curve,roc_auc_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd93ab9",
   "metadata": {},
   "source": [
    "#### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "64e6388a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We start analysis for the below period where attendance system is properly implemented at the given organization.\n",
    "analysis_period_start_time = pd.to_datetime('2020-02-01 00:00:00')\n",
    "analysis_period_end_time = pd.to_datetime('2022-12-31 23:59:59')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252c1432",
   "metadata": {},
   "source": [
    "#### Load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "357655d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read employees dataset\n",
    "# df_employee_raw = pd.read_csv('data/employees4.csv', na_values=['\\\\N'], on_bad_lines='skip', infer_datetime_format=True, parse_dates=['Date_Joined', 'Date_Resigned', 'Inactive_Date'])\n",
    "df_employee_raw = pd.read_csv('data/employees4.csv', na_values=['\\\\N'], on_bad_lines='skip', infer_datetime_format=True, parse_dates=['Date_Joined', 'Date_Resigned', 'Inactive_Date'])\n",
    "df_leaves = pd.read_csv('data/leaves_clean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6706a8e3",
   "metadata": {},
   "source": [
    "##### Preprocess df_employee table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d1fc73d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "    Employee_No Date_of_Birth Gender Marital_Status Date_Joined Date_Resigned  \\\n58          426    1962-05-10   Male        Married  2020-07-03    2021-10-26   \n73          450    1976-05-27   Male        Married  2020-12-03    2021-04-09   \n78          457    1967-03-20   Male        Married  2020-06-01           NaN   \n79          460    1955-03-10   Male        Married  2020-03-01    0000-00-00   \n\n      Status Status_Reason Inactive_Date Employment_Category Employment_Type  \\\n58  Inactive        RESIGN    2021-10-26               Staff  Contarct Basis   \n73  Inactive        RESIGN    2021-04-09              Labour  Contarct Basis   \n78  Inactive        Resign    2020-09-25              Labour  Contarct Basis   \n79  Inactive        Resign    2020-03-31              Labour  Contarct Basis   \n\n           Designation  \n58         Sociologist  \n73     Unskilled Labor  \n78     Unskilled Labor  \n79  Semi Skilled Labor  ",
      "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>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>Date_Resigned</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>58</th>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>2021-10-26</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>2021-04-09</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n    </tr>\n    <tr>\n      <th>78</th>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>NaN</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>0000-00-00</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean employee table\n",
    "# Drop Employee_Code because it's not referenced in other tables.\n",
    "# Other fields are dropped\n",
    "df_employee = df_employee_raw.drop(['Employee_Code', 'Name', 'Address_Line_1', 'Address_Line_2', 'Address_Line_3',\n",
    "                                    'Reporting_emp_1', 'Reporting_emp_2', 'Title', 'Religion', 'Religion_ID', 'Designation_ID'], axis=1)\n",
    "\n",
    "date_range = (df_employee['Date_Joined'] >= analysis_period_start_time) & (df_employee['Date_Joined'] <= analysis_period_end_time)\n",
    "df_employee = df_employee.loc[date_range]\n",
    "df_employee.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "1018405b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1261 entries, 0 to 1260\n",
      "Data columns (total 23 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Employee_No          1261 non-null   int64         \n",
      " 1   Employee_Code        1261 non-null   int64         \n",
      " 2   Name                 1261 non-null   object        \n",
      " 3   Title                1261 non-null   object        \n",
      " 4   Address_Line_1       1261 non-null   object        \n",
      " 5   Address_Line_2       1181 non-null   object        \n",
      " 6   Address_Line_3       952 non-null    object        \n",
      " 7   Date_of_Birth        1261 non-null   object        \n",
      " 8   Gender               1261 non-null   object        \n",
      " 9   Religion_ID          1261 non-null   int64         \n",
      " 10  Marital_Status       1170 non-null   object        \n",
      " 11  Designation_ID       1261 non-null   int64         \n",
      " 12  Date_Joined          1261 non-null   datetime64[ns]\n",
      " 13  Date_Resigned        955 non-null    object        \n",
      " 14  Status               1261 non-null   object        \n",
      " 15  Status_Reason        1028 non-null   object        \n",
      " 16  Inactive_Date        1134 non-null   object        \n",
      " 17  Reporting_emp_1      76 non-null     float64       \n",
      " 18  Reporting_emp_2      1 non-null      float64       \n",
      " 19  Employment_Category  1261 non-null   object        \n",
      " 20  Employment_Type      1261 non-null   object        \n",
      " 21  Religion             1261 non-null   object        \n",
      " 22  Designation          1261 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(4), object(16)\n",
      "memory usage: 226.7+ KB\n",
      "Total employees 1261\n",
      "Earliest join date 1988-01-27 00:00:00\n",
      "Latest join date 2022-12-20 00:00:00\n"
     ]
    }
   ],
   "source": [
    "df_employee_raw.info()\n",
    "\n",
    "print(f'Total employees {str(df_employee_raw[\"Employee_Code\"].nunique())}')\n",
    "print(f'Earliest join date {str(df_employee_raw[\"Date_Joined\"].min())}')\n",
    "print(f'Latest join date {str(df_employee_raw[\"Date_Joined\"].max())}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d31d460",
   "metadata": {},
   "source": [
    "##### Preprocess df_leaves table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "f914804a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Employee_No  Leave_Count\n0          347         52.5\n1          348         21.5\n2          351         57.0\n3          352        105.5\n4          354         79.5",
      "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>Employee_No</th>\n      <th>Leave_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>347</td>\n      <td>52.5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>348</td>\n      <td>21.5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>351</td>\n      <td>57.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>352</td>\n      <td>105.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>354</td>\n      <td>79.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_leaves = df_leaves.rename(columns={'leave_date': 'Leave_Date',\n",
    "                                      'Type': 'Leave_Factor',\n",
    "                                      'Applied Date': 'Applied_Date',\n",
    "                                      'leave_leave_date': 'Lieu_Leave_Date',\n",
    "                                      'Remarks': 'Remarks',\n",
    "                                      'apply_type': 'Leave_Category'})\n",
    "df_leaves_type_map = {'Half Day': 0.5, 'Full Day': 1}\n",
    "df_leaves['Leave_Factor'] = df_leaves['Leave_Factor'].map(df_leaves_type_map)\n",
    "df_leaves_legit = pd.DataFrame(df_leaves.loc[(df_leaves['Leave_Category'] != 'Liue Leave') & (df_leaves['Leave_Category'] != 'Curfew') & (df_leaves['Leave_Category'] != 'Sat Holiday')])\n",
    "df_leaves_summary_temp = df_leaves.groupby('Employee_No')['Leave_Factor'].sum()\n",
    "df_leaves_summary = pd.DataFrame({'Employee_No': df_leaves_summary_temp.index, 'Leave_Count':  df_leaves_summary_temp.values})\n",
    "df_leaves_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "043af85a",
   "metadata": {},
   "source": [
    "#### Data processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "abf0bb9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_35920/3760665125.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  df_employee['Date_Joined'].describe()\n"
     ]
    },
    {
     "data": {
      "text/plain": "count                     796\nunique                    369\ntop       2021-02-01 00:00:00\nfreq                       23\nfirst     2020-02-01 00:00:00\nlast      2022-12-20 00:00:00\nName: Date_Joined, dtype: object"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee['Date_Joined'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a9d8ce",
   "metadata": {},
   "source": [
    "##### Combining datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "81ce123c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Employee_No Date_of_Birth Gender Marital_Status Date_Joined Date_Resigned  \\\n0          426    1962-05-10   Male        Married  2020-07-03    2021-10-26   \n1          450    1976-05-27   Male        Married  2020-12-03    2021-04-09   \n2          457    1967-03-20   Male        Married  2020-06-01           NaN   \n3          460    1955-03-10   Male        Married  2020-03-01    0000-00-00   \n4          471    1992-08-16   Male         Single  2021-04-01    2022-02-28   \n\n     Status Status_Reason Inactive_Date Employment_Category Employment_Type  \\\n0  Inactive        RESIGN    2021-10-26               Staff  Contarct Basis   \n1  Inactive        RESIGN    2021-04-09              Labour  Contarct Basis   \n2  Inactive        Resign    2020-09-25              Labour  Contarct Basis   \n3  Inactive        Resign    2020-03-31              Labour  Contarct Basis   \n4  Inactive        RESIGN    2022-02-28              Labour  Contarct Basis   \n\n          Designation  Leave_Count  \n0         Sociologist         11.0  \n1     Unskilled Labor         60.0  \n2     Unskilled Labor          0.0  \n3  Semi Skilled Labor          0.0  \n4       Skilled Labor         73.5  ",
      "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>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>Date_Resigned</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n      <th>Leave_Count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>2021-10-26</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>2021-04-09</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>60.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>NaN</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>0000-00-00</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>471</td>\n      <td>1992-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2021-04-01</td>\n      <td>2022-02-28</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2022-02-28</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Skilled Labor</td>\n      <td>73.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group two datasets\n",
    "\n",
    "df_employee = pd.merge(df_employee, df_leaves_summary, on='Employee_No', how='left')\n",
    "df_employee['Leave_Count'].fillna(0, inplace=True)\n",
    "df_employee.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb03e72",
   "metadata": {},
   "source": [
    "#### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f46b6cb5",
   "metadata": {},
   "source": [
    "##### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "1e6991ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Employee_No            0.000000\nDate_of_Birth          0.000000\nGender                 0.000000\nMarital_Status         0.096734\nDate_Joined            0.000000\nDate_Resigned          0.175879\nStatus                 0.000000\nStatus_Reason          0.192211\nInactive_Date          0.104271\nEmployment_Category    0.000000\nEmployment_Type        0.000000\nDesignation            0.000000\nLeave_Count            0.000000\ndtype: float64"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee.isna().sum()/len(df_employee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "a7311309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0    Married\nName: Marital_Status, dtype: object"
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee[\"Marital_Status\"].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "9b0a8661",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_employee[\"Marital_Status\"] = df_employee[\"Marital_Status\"].fillna(\"Married\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "174dc819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Employee_No            0.000000\nDate_of_Birth          0.000000\nGender                 0.000000\nMarital_Status         0.000000\nDate_Joined            0.000000\nDate_Resigned          0.175879\nStatus                 0.000000\nStatus_Reason          0.192211\nInactive_Date          0.104271\nEmployment_Category    0.000000\nEmployment_Type        0.000000\nDesignation            0.000000\nLeave_Count            0.000000\ndtype: float64"
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee.isna().sum()/len(df_employee)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8579b766",
   "metadata": {},
   "source": [
    "##### Duplicated values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d9466e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(0, 13)"
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicateRows = df_employee[df_employee.duplicated()]\n",
    "duplicateRows.shape   ### No duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81230977",
   "metadata": {},
   "source": [
    "##### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2246e4b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "boxplot = df_employee.boxplot(column=[\"Leave_Count\"]) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86751e34",
   "metadata": {},
   "source": [
    "#### Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "315ad5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee['Employment_Type'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee['Employment_Type'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "b129a77b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee['Status'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee['Status'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "cf0bb2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df_employee['Status'].value_counts(), labels=df_employee['Status'].unique(), autopct='%1.1f%%')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4a088556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.PairGrid at 0x7f8e55cdf880>"
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seaborn.pairplot(df_employee)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "cb02ddb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count                            641\nmean     205 days 04:36:19.095163806\nstd      184 days 18:07:16.742888928\nmin                  1 days 00:00:00\n25%                 68 days 00:00:00\n50%                158 days 00:00:00\n75%                291 days 00:00:00\nmax                933 days 00:00:00\nName: Employment_Duration, dtype: object"
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based = pd.DataFrame(df_employee.loc[(df_employee['Status'] == 'Inactive') & (df_employee['Employment_Type'] == 'Contarct Basis')])\n",
    "df_employee_inactive_contract_based['Date_Joined'] = pd.to_datetime(df_employee_inactive_contract_based['Date_Joined'])\n",
    "df_employee_inactive_contract_based['Date_Resigned'] = pd.to_datetime(df_employee_inactive_contract_based['Date_Resigned'], errors='coerce')\n",
    "df_employee_inactive_contract_based['Employment_Duration'] = df_employee_inactive_contract_based['Date_Resigned'].fillna(df_employee_inactive_contract_based['Inactive_Date']) - df_employee_inactive_contract_based['Date_Joined'] + pd.Timedelta(days=1)\n",
    "df_employee_inactive_contract_based = df_employee_inactive_contract_based.drop(df_employee_inactive_contract_based[df_employee_inactive_contract_based['Employment_Duration'] < pd.Timedelta(days=0)].index)\n",
    "df_employee_inactive_contract_based['Unavailability_Factor'] = df_employee_inactive_contract_based['Leave_Count'] / df_employee_inactive_contract_based['Employment_Duration'].dt.days\n",
    "\n",
    "# Annotating attrition or not. If the resignation date is a multiple of 1 year contract, we don't consider that as attrition.\n",
    "df_employee_inactive_contract_based.loc[df_employee_inactive_contract_based['Employment_Duration'].dt.days % 365 == 0, 'Is_Attrition'] = 0\n",
    "df_employee_inactive_contract_based.loc[df_employee_inactive_contract_based['Employment_Duration'].dt.days % 365 != 0, 'Is_Attrition'] = 1\n",
    "df_employee_inactive_contract_based['Employment_Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "73b62006",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Too little permanent employees.\n",
    "\n",
    "# df_employee_inactive = pd.DataFrame(df_employee.loc[(df_employee['Status'] == 'Inactive')])\n",
    "# df_employee_inactive['Date_Joined'] = pd.to_datetime(df_employee_inactive['Date_Joined'])\n",
    "# df_employee_inactive['Date_Resigned'] = pd.to_datetime(df_employee_inactive['Date_Resigned'], errors='coerce')\n",
    "# df_employee_inactive['Employment_Duration'] = df_employee_inactive['Date_Resigned'] - df_employee_inactive['Date_Joined'] + pd.Timedelta(days=1)\n",
    "# df_employee_inactive['Unavailability_Factor'] = df_employee_inactive['Leave_Count'] / df_employee_inactive['Employment_Duration'].dt.days\n",
    "# df_employee_inactive['Employment_Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "1acba580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count                            152\nmean     458 days 21:09:27.421052632\nstd      258 days 03:59:08.662802828\nmin                 12 days 23:59:59\n25%                296 days 17:59:59\n50%                368 days 11:59:59\n75%                537 days 11:59:59\nmax               1065 days 23:59:59\nName: Employment_Duration, dtype: object"
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Active contract base employee analysis.\n",
    "\n",
    "df_employee_active_contract_based = pd.DataFrame(df_employee.loc[(df_employee['Status'] == 'Active') & (df_employee['Employment_Type'] == 'Contarct Basis')])\n",
    "df_employee_active_contract_based['Date_Joined'] = pd.to_datetime(df_employee_active_contract_based['Date_Joined'])\n",
    "df_employee_active_contract_based['Employment_Duration'] = analysis_period_end_time - df_employee_active_contract_based['Date_Joined'] + pd.Timedelta(days=1)\n",
    "df_employee_active_contract_based['Unavailability_Factor'] = df_employee_active_contract_based['Leave_Count'] / df_employee_active_contract_based['Employment_Duration'].dt.days\n",
    "# Annotating active employees as non-attrition.\n",
    "df_employee_active_contract_based['Is_Attrition'] = 0\n",
    "df_employee_active_contract_based['Employment_Duration'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2c0c9176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    641.000000\nmean      38.219969\nstd       33.486809\nmin        0.000000\n25%       10.000000\n50%       32.000000\n75%       58.500000\nmax      163.000000\nName: Leave_Count, dtype: float64"
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based['Leave_Count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "15837e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    152.000000\nmean      55.019737\nstd       50.304565\nmin        0.000000\n25%       12.500000\n50%       49.250000\n75%       82.375000\nmax      305.000000\nName: Leave_Count, dtype: float64"
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_active_contract_based['Leave_Count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "1c90abd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    641.000000\nmean       0.512923\nstd        1.840379\nmin        0.000000\n25%        0.059783\n50%        0.215827\n75%        0.363222\nmax       20.000000\nName: Unavailability_Factor, dtype: float64"
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_inactive_contract_based['Unavailability_Factor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "84547c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "count    152.000000\nmean       0.127464\nstd        0.112425\nmin        0.000000\n25%        0.045296\n50%        0.108324\n75%        0.190642\nmax        0.626283\nName: Unavailability_Factor, dtype: float64"
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_active_contract_based['Unavailability_Factor'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "ecdf938d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 500x500 with 6 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Joining tables for attrition.\n",
    "df_employee_for_modeling = pd.concat([df_employee_inactive_contract_based, df_employee_active_contract_based])\n",
    "df_employee_for_modeling['Employment_Duration'] = df_employee_for_modeling['Employment_Duration'].dt.days\n",
    "temp_plot = df_employee_for_modeling['Is_Attrition'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Is_Attrition'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31c00c1",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "49cc885c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d98e752b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4a1b009b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "d411bedb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d02c7262",
   "metadata": {},
   "source": [
    "#### Creating Dummy Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "40912565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Employee_No Date_of_Birth Gender Marital_Status Date_Joined  \\\n0          426    1962-05-10   Male        Married  2020-07-03   \n1          450    1976-05-27   Male        Married  2020-12-03   \n2          457    1967-03-20   Male        Married  2020-06-01   \n3          460    1955-03-10   Male        Married  2020-03-01   \n4          471    1992-08-16   Male         Single  2021-04-01   \n\n         Date_Resigned    Status Status_Reason Inactive_Date  \\\n0  2021-10-26 00:00:00  Inactive        RESIGN    2021-10-26   \n1  2021-04-09 00:00:00  Inactive        RESIGN    2021-04-09   \n2                  NaT  Inactive        Resign    2020-09-25   \n3                  NaT  Inactive        Resign    2020-03-31   \n4  2022-02-28 00:00:00  Inactive        RESIGN    2022-02-28   \n\n  Employment_Category Employment_Type         Designation  Leave_Count  \\\n0               Staff  Contarct Basis         Sociologist         11.0   \n1              Labour  Contarct Basis     Unskilled Labor         60.0   \n2              Labour  Contarct Basis     Unskilled Labor          0.0   \n3              Labour  Contarct Basis  Semi Skilled Labor          0.0   \n4              Labour  Contarct Basis       Skilled Labor         73.5   \n\n   Employment_Duration  Unavailability_Factor  Is_Attrition  \n0                  481               0.022869           1.0  \n1                  128               0.468750           1.0  \n2                  117               0.000000           1.0  \n3                   31               0.000000           1.0  \n4                  334               0.220060           1.0  ",
      "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>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>Date_Resigned</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n      <th>Leave_Count</th>\n      <th>Employment_Duration</th>\n      <th>Unavailability_Factor</th>\n      <th>Is_Attrition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>2021-10-26 00:00:00</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n      <td>11.0</td>\n      <td>481</td>\n      <td>0.022869</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>2021-04-09 00:00:00</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>60.0</td>\n      <td>128</td>\n      <td>0.468750</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>NaT</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>0.0</td>\n      <td>117</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>NaT</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n      <td>0.0</td>\n      <td>31</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>471</td>\n      <td>1992-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2021-04-01</td>\n      <td>2022-02-28 00:00:00</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2022-02-28</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Skilled Labor</td>\n      <td>73.5</td>\n      <td>334</td>\n      <td>0.220060</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c4e886",
   "metadata": {},
   "source": [
    "##### Checking categories in each variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "74c3e5c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Male      735\nFemale     58\nName: Gender, dtype: int64"
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Gender'].value_counts()  # need to create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9ffc5a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Married    546\nSingle     247\nName: Marital_Status, dtype: int64"
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Marital_Status'].value_counts()  # need to create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5bcdf9c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Inactive    641\nActive      152\nName: Status, dtype: int64"
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Status'].value_counts()  # need to create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "76c6a171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "RESIGN                                                          442\nResign                                                          114\nResigned                                                         22\nresign                                                           18\nVOP                                                              14\nresigned                                                          9\nLeft                                                              5\nDiscontinuation of Service on Fixed Term Employment Contract      2\nBulk Termination                                                  2\nRESIGNED                                                          2\n resign                                                           2\nSERVICE DISCONTINUATION                                           2\nvOP                                                               1\nBulk Terminaion                                                   1\nResigend                                                          1\nAllocate to staff category                                        1\nBUlk Termination                                                  1\nChange the Temp No to EPF No\\r\\\\nRESIGN                           1\nLEFT                                                              1\nName: Status_Reason, dtype: int64"
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Status_Reason'].value_counts()  ## cant have dummy variables - too many categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9d2d4ce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Labour        532\nStaff         241\nManagement     20\nName: Employment_Category, dtype: int64"
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Employment_Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "d6da48a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Contarct Basis    793\nName: Employment_Type, dtype: int64"
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Employment_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c6bbc594",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Unskilled Labor               317\nMason                          33\nSkilled Labor                  28\nTechnical Officer              26\nSemi Skilled Labor             24\n                             ... \nIT Consultant                   1\nData Entry Executive            1\nPlanning Engineer cum QAQC      1\nTrainee Safety Officer          1\nSoftware Developer              1\nName: Designation, Length: 107, dtype: int64"
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_for_modeling['Designation'].value_counts() ## need to remove - too many categories"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ec4a872",
   "metadata": {},
   "source": [
    "##### Creating dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9b5eb72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender_Female  Gender_Male  Marital_Status_Married  Marital_Status_Single  \\\n0              0            1                       1                      0   \n1              0            1                       1                      0   \n2              0            1                       1                      0   \n3              0            1                       1                      0   \n4              0            1                       0                      1   \n\n   Status_Active  Status_Inactive  Employment_Category_Labour  \\\n0              0                1                           0   \n1              0                1                           1   \n2              0                1                           1   \n3              0                1                           1   \n4              0                1                           1   \n\n   Employment_Category_Management  Employment_Category_Staff  \n0                               0                          1  \n1                               0                          0  \n2                               0                          0  \n3                               0                          0  \n4                               0                          0  ",
      "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>Gender_Female</th>\n      <th>Gender_Male</th>\n      <th>Marital_Status_Married</th>\n      <th>Marital_Status_Single</th>\n      <th>Status_Active</th>\n      <th>Status_Inactive</th>\n      <th>Employment_Category_Labour</th>\n      <th>Employment_Category_Management</th>\n      <th>Employment_Category_Staff</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dummy = pd.get_dummies(df_employee_for_modeling[[\"Gender\",\"Marital_Status\", \"Status\", \"Employment_Category\"]])\n",
    "df_dummy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "5776a3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing one level from each\n",
    "\n",
    "df_dummy.drop([\"Gender_Female\",\"Marital_Status_Single\",\"Status_Active\",\"Employment_Category_Management\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "9ea13277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender_Male  Marital_Status_Married  Status_Inactive  \\\n0            1                       1                1   \n1            1                       1                1   \n2            1                       1                1   \n3            1                       1                1   \n4            1                       0                1   \n\n   Employment_Category_Labour  Employment_Category_Staff  Employee_No  \\\n0                           0                          1          426   \n1                           1                          0          450   \n2                           1                          0          457   \n3                           1                          0          460   \n4                           1                          0          471   \n\n  Date_of_Birth Gender Marital_Status Date_Joined  ...    Status  \\\n0    1962-05-10   Male        Married  2020-07-03  ...  Inactive   \n1    1976-05-27   Male        Married  2020-12-03  ...  Inactive   \n2    1967-03-20   Male        Married  2020-06-01  ...  Inactive   \n3    1955-03-10   Male        Married  2020-03-01  ...  Inactive   \n4    1992-08-16   Male         Single  2021-04-01  ...  Inactive   \n\n  Status_Reason Inactive_Date Employment_Category Employment_Type  \\\n0        RESIGN    2021-10-26               Staff  Contarct Basis   \n1        RESIGN    2021-04-09              Labour  Contarct Basis   \n2        Resign    2020-09-25              Labour  Contarct Basis   \n3        Resign    2020-03-31              Labour  Contarct Basis   \n4        RESIGN    2022-02-28              Labour  Contarct Basis   \n\n          Designation Leave_Count  Employment_Duration  Unavailability_Factor  \\\n0         Sociologist        11.0                  481               0.022869   \n1     Unskilled Labor        60.0                  128               0.468750   \n2     Unskilled Labor         0.0                  117               0.000000   \n3  Semi Skilled Labor         0.0                   31               0.000000   \n4       Skilled Labor        73.5                  334               0.220060   \n\n   Is_Attrition  \n0           1.0  \n1           1.0  \n2           1.0  \n3           1.0  \n4           1.0  \n\n[5 rows x 21 columns]",
      "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>Gender_Male</th>\n      <th>Marital_Status_Married</th>\n      <th>Status_Inactive</th>\n      <th>Employment_Category_Labour</th>\n      <th>Employment_Category_Staff</th>\n      <th>Employee_No</th>\n      <th>Date_of_Birth</th>\n      <th>Gender</th>\n      <th>Marital_Status</th>\n      <th>Date_Joined</th>\n      <th>...</th>\n      <th>Status</th>\n      <th>Status_Reason</th>\n      <th>Inactive_Date</th>\n      <th>Employment_Category</th>\n      <th>Employment_Type</th>\n      <th>Designation</th>\n      <th>Leave_Count</th>\n      <th>Employment_Duration</th>\n      <th>Unavailability_Factor</th>\n      <th>Is_Attrition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>426</td>\n      <td>1962-05-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-07-03</td>\n      <td>...</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-10-26</td>\n      <td>Staff</td>\n      <td>Contarct Basis</td>\n      <td>Sociologist</td>\n      <td>11.0</td>\n      <td>481</td>\n      <td>0.022869</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>450</td>\n      <td>1976-05-27</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-12-03</td>\n      <td>...</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2021-04-09</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>60.0</td>\n      <td>128</td>\n      <td>0.468750</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>457</td>\n      <td>1967-03-20</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-06-01</td>\n      <td>...</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-09-25</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Unskilled Labor</td>\n      <td>0.0</td>\n      <td>117</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>460</td>\n      <td>1955-03-10</td>\n      <td>Male</td>\n      <td>Married</td>\n      <td>2020-03-01</td>\n      <td>...</td>\n      <td>Inactive</td>\n      <td>Resign</td>\n      <td>2020-03-31</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Semi Skilled Labor</td>\n      <td>0.0</td>\n      <td>31</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>471</td>\n      <td>1992-08-16</td>\n      <td>Male</td>\n      <td>Single</td>\n      <td>2021-04-01</td>\n      <td>...</td>\n      <td>Inactive</td>\n      <td>RESIGN</td>\n      <td>2022-02-28</td>\n      <td>Labour</td>\n      <td>Contarct Basis</td>\n      <td>Skilled Labor</td>\n      <td>73.5</td>\n      <td>334</td>\n      <td>0.220060</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_final = pd.concat([df_dummy, df_employee_for_modeling],axis=1)\n",
    "df_employee_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "036bed05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "Index(['Gender_Male', 'Marital_Status_Married', 'Status_Inactive',\n       'Employment_Category_Labour', 'Employment_Category_Staff',\n       'Employee_No', 'Date_of_Birth', 'Gender', 'Marital_Status',\n       'Date_Joined', 'Date_Resigned', 'Status', 'Status_Reason',\n       'Inactive_Date', 'Employment_Category', 'Employment_Type',\n       'Designation', 'Leave_Count', 'Employment_Duration',\n       'Unavailability_Factor', 'Is_Attrition'],\n      dtype='object')"
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "f8a84da2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender_Male  Marital_Status_Married  Employment_Category_Labour  \\\n0            1                       1                           0   \n1            1                       1                           1   \n2            1                       1                           1   \n3            1                       1                           1   \n4            1                       0                           1   \n\n   Employment_Category_Staff  Leave_Count  Employment_Duration  \\\n0                          1         11.0                  481   \n1                          0         60.0                  128   \n2                          0          0.0                  117   \n3                          0          0.0                   31   \n4                          0         73.5                  334   \n\n   Unavailability_Factor  Is_Attrition  \n0               0.022869           1.0  \n1               0.468750           1.0  \n2               0.000000           1.0  \n3               0.000000           1.0  \n4               0.220060           1.0  ",
      "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>Gender_Male</th>\n      <th>Marital_Status_Married</th>\n      <th>Employment_Category_Labour</th>\n      <th>Employment_Category_Staff</th>\n      <th>Leave_Count</th>\n      <th>Employment_Duration</th>\n      <th>Unavailability_Factor</th>\n      <th>Is_Attrition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>481</td>\n      <td>0.022869</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>60.0</td>\n      <td>128</td>\n      <td>0.468750</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>117</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>31</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>73.5</td>\n      <td>334</td>\n      <td>0.220060</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rmove original categorical variables\n",
    "\n",
    "df_employee_final.drop([\"Employee_No\",\"Date_of_Birth\",\"Gender\",\"Marital_Status\",\"Date_Joined\",\"Date_Resigned\",\"Status\",\"Status_Reason\",\"Inactive_Date\",\"Employment_Category\",\"Employment_Type\",\"Designation\",\"Status_Inactive\"],axis=1,inplace=True)\n",
    "df_employee_final.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f4d4c1",
   "metadata": {},
   "source": [
    "#### Checking multicolinearity of independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "460f0077",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#creating the correlation matrix\n",
    "matrix = df_employee_final.corr()\n",
    "sns.heatmap(matrix, annot=True)\n",
    "plt.title(\"Correlation matrix \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "88a5885c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from patsy import dmatrices\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "#find design matrix for regression model using 'rating' as response variable \n",
    "y, X = dmatrices('Is_Attrition ~ Gender_Male+Marital_Status_Married+Employment_Category_Labour+Employment_Category_Staff+Leave_Count+Employment_Duration+Unavailability_Factor', data=df_employee_final, return_type='dataframe')\n",
    "\n",
    "#create DataFrame to hold VIF values\n",
    "vif_df = pd.DataFrame()\n",
    "vif_df['variable'] = X.columns \n",
    "\n",
    "#calculate VIF for each predictor variable \n",
    "vif_df['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "6bf454af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                     variable        VIF\n0                   Intercept  55.793791\n1                 Gender_Male   1.090641\n2      Marital_Status_Married   1.086338\n3  Employment_Category_Labour   9.356976\n4   Employment_Category_Staff   9.339695\n5                 Leave_Count   1.341343\n6         Employment_Duration   1.369480\n7       Unavailability_Factor   1.045966",
      "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>variable</th>\n      <th>VIF</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Intercept</td>\n      <td>55.793791</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Gender_Male</td>\n      <td>1.090641</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Marital_Status_Married</td>\n      <td>1.086338</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Employment_Category_Labour</td>\n      <td>9.356976</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Employment_Category_Staff</td>\n      <td>9.339695</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Leave_Count</td>\n      <td>1.341343</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Employment_Duration</td>\n      <td>1.369480</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Unavailability_Factor</td>\n      <td>1.045966</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vif_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc0e6bc3",
   "metadata": {},
   "source": [
    "So, need to remove Employment_Category_Labour and Employment_Category_staff before modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "bda1b52c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "   Gender_Male  Marital_Status_Married  Leave_Count  Employment_Duration  \\\n0            1                       1         11.0                  481   \n1            1                       1         60.0                  128   \n2            1                       1          0.0                  117   \n3            1                       1          0.0                   31   \n4            1                       0         73.5                  334   \n\n   Unavailability_Factor  Is_Attrition  \n0               0.022869           1.0  \n1               0.468750           1.0  \n2               0.000000           1.0  \n3               0.000000           1.0  \n4               0.220060           1.0  ",
      "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>Gender_Male</th>\n      <th>Marital_Status_Married</th>\n      <th>Leave_Count</th>\n      <th>Employment_Duration</th>\n      <th>Unavailability_Factor</th>\n      <th>Is_Attrition</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>11.0</td>\n      <td>481</td>\n      <td>0.022869</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>60.0</td>\n      <td>128</td>\n      <td>0.468750</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>117</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0.0</td>\n      <td>31</td>\n      <td>0.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>73.5</td>\n      <td>334</td>\n      <td>0.220060</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_employee_final.drop([\"Employment_Category_Labour\",\"Employment_Category_Staff\"],axis=1,inplace=True)\n",
    "df_employee_final.head() #This data frame can be used for further processes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7390a1df",
   "metadata": {},
   "source": [
    "#### Handling data imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "85fa2a87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_plot = df_employee_for_modeling['Status'].value_counts().plot(kind='bar')\n",
    "temp_plot.bar_label(temp_plot.containers[0], labels=df_employee_for_modeling['Is_Attrition'].value_counts())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecb4fbdf",
   "metadata": {},
   "source": [
    "Data imbalance should be handled as the percentage of inactive employees is 80.5% and active employees is 19.5%.\n",
    "This can be handled using SMOTE technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "305cb0fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_employee_final.iloc[:,:5]\n",
    "y=df_employee_final.iloc[:,5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ad6cad87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "1.0    638\n0.0    638\nName: Is_Attrition, dtype: int64"
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smt=SMOTE(random_state = 10)\n",
    "x_sm,y_sm = smt.fit_resample(x,y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "086078d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "155319e8",
   "metadata": {},
   "source": [
    "#### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "b9488691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "LogisticRegression()"
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x_sm, y_sm, test_size=0.2, random_state=55)\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "78b57be9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7421875\n",
      "Precision: 0.7235772357723578\n",
      "Recall: 0.7355371900826446\n",
      "F1 score: 0.7295081967213114\n",
      "R2 score: 0.7295081967213114\n"
     ]
    }
   ],
   "source": [
    "y_predicted = model.predict(x_test)\n",
    "accuracy = accuracy_score(y_test, y_predicted)\n",
    "print(f'Accuracy: {accuracy}')\n",
    "precision = precision_score(y_test, y_predicted)\n",
    "print(f'Precision: {precision}')\n",
    "recall = recall_score(y_test, y_predicted)\n",
    "print(f'Recall: {recall}')\n",
    "f1 = f1_score(y_test, y_predicted)\n",
    "print(f'F1 score: {f1}')\n",
    "r2 = f1_score(y_test, y_predicted)\n",
    "print(f'R2 score: {r2}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "7d823c18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(y_test,y_predicted),annot=True,fmt=\"g\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "27afdbb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "0.7421875"
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_L=(np.diag(confusion_matrix(y_test,y_predicted)).sum())/len(y_test)\n",
    "accuracy_L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "6d63c339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression: 0.7421875 \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.76      0.75      0.75       135\n",
      "         1.0       0.72      0.74      0.73       121\n",
      "\n",
      "    accuracy                           0.74       256\n",
      "   macro avg       0.74      0.74      0.74       256\n",
      "weighted avg       0.74      0.74      0.74       256\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy of Logistic Regression:\",accuracy_L,'\\n')\n",
    "print(classification_report(y_test,y_predicted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "4b243990",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "9bfb8dc2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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