attrition-rate-of-employees.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/Test.csv\n",
      "/kaggle/input/Train.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Importing DataSets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df_train= pd.read_csv('/kaggle/input/Train.csv')\n",
    "df_test=pd.read_csv('/kaggle/input/Test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Employee_ID', 'Gender', 'Age', 'Education_Level',\n",
       "       'Relationship_Status', 'Hometown', 'Unit', 'Decision_skill_possess',\n",
       "       'Time_of_service', 'Time_since_promotion', 'growth_rate', 'Travel_Rate',\n",
       "       'Post_Level', 'Pay_Scale', 'Compensation_and_Benefits',\n",
       "       'Work_Life_balance', 'VAR1', 'VAR2', 'VAR3', 'VAR4', 'VAR5', 'VAR6',\n",
       "       'VAR7', 'Attrition_rate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Relationship_Status</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Decision_skill_possess</th>\n",
       "      <th>Time_of_service</th>\n",
       "      <th>Time_since_promotion</th>\n",
       "      <th>...</th>\n",
       "      <th>Compensation_and_Benefits</th>\n",
       "      <th>Work_Life_balance</th>\n",
       "      <th>VAR1</th>\n",
       "      <th>VAR2</th>\n",
       "      <th>VAR3</th>\n",
       "      <th>VAR4</th>\n",
       "      <th>VAR5</th>\n",
       "      <th>VAR6</th>\n",
       "      <th>VAR7</th>\n",
       "      <th>Attrition_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EID_23371</td>\n",
       "      <td>F</td>\n",
       "      <td>42.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>IT</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.7516</td>\n",
       "      <td>1.8688</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.1841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EID_18000</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Logistics</td>\n",
       "      <td>Analytical</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EID_3891</td>\n",
       "      <td>F</td>\n",
       "      <td>58.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>Quality</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EID_17492</td>\n",
       "      <td>F</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Single</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>Human Resource Management</td>\n",
       "      <td>Behavioral</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.8176</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EID_22534</td>\n",
       "      <td>F</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Logistics</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>type3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.7516</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1827</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee_ID Gender   Age  Education_Level Relationship_Status     Hometown  \\\n",
       "0   EID_23371      F  42.0                4             Married     Franklin   \n",
       "1   EID_18000      M  24.0                3              Single  Springfield   \n",
       "2    EID_3891      F  58.0                3             Married      Clinton   \n",
       "3   EID_17492      F  26.0                3              Single      Lebanon   \n",
       "4   EID_22534      F  31.0                1             Married  Springfield   \n",
       "\n",
       "                        Unit Decision_skill_possess  Time_of_service  \\\n",
       "0                         IT             Conceptual              4.0   \n",
       "1                  Logistics             Analytical              5.0   \n",
       "2                    Quality             Conceptual             27.0   \n",
       "3  Human Resource Management             Behavioral              4.0   \n",
       "4                  Logistics             Conceptual              5.0   \n",
       "\n",
       "   Time_since_promotion  ...  Compensation_and_Benefits  Work_Life_balance  \\\n",
       "0                     4  ...                      type2                3.0   \n",
       "1                     4  ...                      type2                4.0   \n",
       "2                     3  ...                      type2                1.0   \n",
       "3                     3  ...                      type2                1.0   \n",
       "4                     4  ...                      type3                3.0   \n",
       "\n",
       "   VAR1    VAR2    VAR3  VAR4  VAR5  VAR6  VAR7  Attrition_rate  \n",
       "0     4  0.7516  1.8688   2.0     4     5     3          0.1841  \n",
       "1     3 -0.9612 -0.4537   2.0     3     5     3          0.0670  \n",
       "2     4 -0.9612 -0.4537   3.0     3     8     3          0.0851  \n",
       "3     3 -1.8176 -0.4537   NaN     3     7     3          0.0668  \n",
       "4     1  0.7516 -0.4537   2.0     2     8     2          0.1827  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7000 entries, 0 to 6999\n",
      "Data columns (total 24 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                7000 non-null   object \n",
      " 1   Gender                     7000 non-null   object \n",
      " 2   Age                        6588 non-null   float64\n",
      " 3   Education_Level            7000 non-null   int64  \n",
      " 4   Relationship_Status        7000 non-null   object \n",
      " 5   Hometown                   7000 non-null   object \n",
      " 6   Unit                       7000 non-null   object \n",
      " 7   Decision_skill_possess     7000 non-null   object \n",
      " 8   Time_of_service            6856 non-null   float64\n",
      " 9   Time_since_promotion       7000 non-null   int64  \n",
      " 10  growth_rate                7000 non-null   int64  \n",
      " 11  Travel_Rate                7000 non-null   int64  \n",
      " 12  Post_Level                 7000 non-null   int64  \n",
      " 13  Pay_Scale                  6991 non-null   float64\n",
      " 14  Compensation_and_Benefits  7000 non-null   object \n",
      " 15  Work_Life_balance          6989 non-null   float64\n",
      " 16  VAR1                       7000 non-null   int64  \n",
      " 17  VAR2                       6423 non-null   float64\n",
      " 18  VAR3                       7000 non-null   float64\n",
      " 19  VAR4                       6344 non-null   float64\n",
      " 20  VAR5                       7000 non-null   int64  \n",
      " 21  VAR6                       7000 non-null   int64  \n",
      " 22  VAR7                       7000 non-null   int64  \n",
      " 23  Attrition_rate             7000 non-null   float64\n",
      "dtypes: float64(8), int64(9), object(7)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Employee_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Relationship_Status</th>\n",
       "      <th>Hometown</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Decision_skill_possess</th>\n",
       "      <th>Time_of_service</th>\n",
       "      <th>Time_since_promotion</th>\n",
       "      <th>...</th>\n",
       "      <th>Pay_Scale</th>\n",
       "      <th>Compensation_and_Benefits</th>\n",
       "      <th>Work_Life_balance</th>\n",
       "      <th>VAR1</th>\n",
       "      <th>VAR2</th>\n",
       "      <th>VAR3</th>\n",
       "      <th>VAR4</th>\n",
       "      <th>VAR5</th>\n",
       "      <th>VAR6</th>\n",
       "      <th>VAR7</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EID_22713</td>\n",
       "      <td>F</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Single</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>-0.4537</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EID_9658</td>\n",
       "      <td>M</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>IT</td>\n",
       "      <td>Directive</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.9612</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EID_22203</td>\n",
       "      <td>M</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Directive</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>type3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EID_7652</td>\n",
       "      <td>M</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Single</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Analytical</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>type0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.1048</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EID_6516</td>\n",
       "      <td>F</td>\n",
       "      <td>44.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Married</td>\n",
       "      <td>Franklin</td>\n",
       "      <td>R&amp;D</td>\n",
       "      <td>Conceptual</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>type2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.6081</td>\n",
       "      <td>0.7075</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Employee_ID Gender   Age  Education_Level Relationship_Status     Hometown  \\\n",
       "0   EID_22713      F  32.0                5              Single  Springfield   \n",
       "1    EID_9658      M  65.0                2              Single      Lebanon   \n",
       "2   EID_22203      M  52.0                3             Married  Springfield   \n",
       "3    EID_7652      M  50.0                5              Single   Washington   \n",
       "4    EID_6516      F  44.0                3             Married     Franklin   \n",
       "\n",
       "        Unit Decision_skill_possess  Time_of_service  Time_since_promotion  \\\n",
       "0        R&D             Conceptual              7.0                     4   \n",
       "1         IT              Directive             41.0                     2   \n",
       "2      Sales              Directive             21.0                     3   \n",
       "3  Marketing             Analytical             11.0                     4   \n",
       "4        R&D             Conceptual             12.0                     4   \n",
       "\n",
       "   ...  Pay_Scale  Compensation_and_Benefits  Work_Life_balance  VAR1    VAR2  \\\n",
       "0  ...        4.0                      type2                1.0     3 -0.9612   \n",
       "1  ...        1.0                      type2                1.0     4 -0.9612   \n",
       "2  ...        8.0                      type3                1.0     4 -0.1048   \n",
       "3  ...        2.0                      type0                4.0     3 -0.1048   \n",
       "4  ...        2.0                      type2                4.0     4  1.6081   \n",
       "\n",
       "     VAR3  VAR4  VAR5  VAR6  VAR7  \n",
       "0 -0.4537   2.0     1     8     4  \n",
       "1  0.7075   1.0     2     8     2  \n",
       "2  0.7075   2.0     1     9     3  \n",
       "3  0.7075   2.0     2     8     3  \n",
       "4  0.7075   2.0     2     7     4  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                3000 non-null   object \n",
      " 1   Gender                     3000 non-null   object \n",
      " 2   Age                        2839 non-null   float64\n",
      " 3   Education_Level            3000 non-null   int64  \n",
      " 4   Relationship_Status        3000 non-null   object \n",
      " 5   Hometown                   3000 non-null   object \n",
      " 6   Unit                       3000 non-null   object \n",
      " 7   Decision_skill_possess     3000 non-null   object \n",
      " 8   Time_of_service            2948 non-null   float64\n",
      " 9   Time_since_promotion       3000 non-null   int64  \n",
      " 10  growth_rate                3000 non-null   int64  \n",
      " 11  Travel_Rate                3000 non-null   int64  \n",
      " 12  Post_Level                 3000 non-null   int64  \n",
      " 13  Pay_Scale                  2997 non-null   float64\n",
      " 14  Compensation_and_Benefits  3000 non-null   object \n",
      " 15  Work_Life_balance          2995 non-null   float64\n",
      " 16  VAR1                       3000 non-null   int64  \n",
      " 17  VAR2                       2783 non-null   float64\n",
      " 18  VAR3                       3000 non-null   float64\n",
      " 19  VAR4                       2702 non-null   float64\n",
      " 20  VAR5                       3000 non-null   int64  \n",
      " 21  VAR6                       3000 non-null   int64  \n",
      " 22  VAR7                       3000 non-null   int64  \n",
      "dtypes: float64(7), int64(9), object(7)\n",
      "memory usage: 539.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Searching for missing values and then analysing\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age 0.0589 % missing values\n",
      "Time_of_service 0.0206 % missing values\n",
      "Pay_Scale 0.0013 % missing values\n",
      "Work_Life_balance 0.0016 % missing values\n",
      "VAR2 0.0824 % missing values\n",
      "VAR4 0.0937 % missing values\n"
     ]
    }
   ],
   "source": [
    "features_with_na= [features for features in df_train.columns if df_train[features].isnull().sum()>1]\n",
    "features_with_na_test= [features for features in df_test.columns if df_test[features].isnull().sum()>1]\n",
    "\n",
    "for feature in features_with_na:\n",
    "    print( feature, np.round(df_train[feature].isnull().mean(),4),'% missing values')"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Numeric values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Education_Level', 'Time_of_service', 'Time_since_promotion', 'growth_rate', 'Travel_Rate', 'Post_Level', 'Pay_Scale', 'Work_Life_balance', 'VAR1', 'VAR2', 'VAR3', 'VAR4', 'VAR5', 'VAR6', 'VAR7', 'Attrition_rate']\n"
     ]
    }
   ],
   "source": [
    "features_with_num= [feature for feature in df_train.columns if df_train[feature].dtypes!='O']\n",
    "print(features_with_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Uwtvf7uCVhVnsOVxE7/Zx3DasK6P6JBAdZoMLNuw+zF1vr2DD7iP86NwOPDyml/0hUcH09GweeGcV79w2mAEd7eFj/rLkESHJY8e+Y/xm9jo+W7+Hzq0b8+jYZIb3aON2WGGvqKSU95bv5KUFWWTtPUqnVo249cKuXNk/kfoxoV0SvqxMeW3RVp78eCNxDWN4cnw/RvS0juHKCopKGPi7zxjbrz1/GN/P7XDChiWPME8ex0+U8uL8TF76MouYKOGOEd256fxOIf+LLdyUlimfrvXNFVm98xBtmtbnf87vzLXndgh6WfrTkXvwOPfNWMniLfu4JLktT1zZl1Z1ZAju6Xhg5ko+XLWLbx++2Ob++MmSR5gmD1Vlzurd/O7DdeQeKmRcSnseGtWLds3Cr0x2OFFVFm/Zx4vzM1mUuY+mDWK4YXBHfjKkc8jMj3h/xU5+/d4aSsqUx8Ymk+atW0NwT0fGtv2Mn7qEJ8f3I82KJfrFkkcYJo+Nu48wedZalmTtIzkhjsfH9Sa1k7XV1raV2QeZumALH6/dTb3oKCamerj5gi54WrpTgfjQsWJ+/f4aZq3MpX8H3xBc6+/yj6py0TMLaNmoHjNvs2KJ/rBJgmHk0PFinp27ibe+3k7TBjH89oo+XDuwQ9h14EaKsz3NmXLdALbkF/DSgi3869sd/OObHYztl8CkYV1rdS7N4sy93DtjJflHirj3krO4bVjXOj0EN1AiwkSvh99/tIEt+QV0tWKJp83uPEJIWZkyY2k2T368kf3HTnDtwA7cd2mPOltSI1TtOnScvy7cyj+/3cGxE6Vc1LMNtw3rijeId4WFxaX88ZON/PWrrXSJb8xzE1Pol2RDcE9HebHEn17QmYdG9XI7nJBnzVYhnjxWZB/ksffXsDLnEN6OLZh8eW97yFGIO3jsBG8u2c7ri7Zy4FgxqZ1acNuwrgzv0aZG+x7W7/INwd245wjXD+rIr0b3omE9GyhxJm5+M4PlOw6y5KERYfdIgtpmySNEk0f+kSKe/HgDM5bm0KZpfR4a3ZMrUhKt4zOMHDtRwrT0bF75MovcQ4X0bNeU24Z1ZUzfhDNqUiorU179KounPtlEs0axPDm+nw3LriFz1+3h5jczeOUGb52oLHwmLHmEWPIoLi3jzSXbeW7uJgpLSrnp/M7cMaI7TWz4YNgqLi1j1opcpi7Ywua8ApJaNOTWoV2Y4PUE/Ez4nAPHuG/GSr7O2s/I3u34vyv70tKaL2tMSWkZg5/4nBRPc1654Xu/F00F1mEeQhZn7uWxWWvZnFfAhWfF8+jYZOu4iwCx0VFcNSCJH56TyLwNebw4P5Nfv7+W5z7bzE3nd+a6QR1p1vDUc0VUlfdW7OTR99ZSpsofx/dj/IAkuxOtYTHRUVzZP5FXF24l70ghbZra0PdA2Z1HLco5cIz/m7OeOat306FlI359WTIX96rZ9nETOlSVb7buZ8r8LSzYlE+T+jH8aFAH/mdIZ9rEff+X1cFjJ3j4vTV8uGoX3o4teCYthQ6t3BkOXBdsyS/goqcX8OConkyyYoknZc1WLiaPwuJSXlqQxZQFmQD8fFg3bh7aJeCmDBO+1uYeYuqCLD5clUtMlO8O5dahXejklEj/avNe7puxkr0FRdx9yVlMurCrDc2uBeOnLGb/sRPMs2KJJ2XJw4Xkoap8um4Pv529jpwDxxnTN4FfjelFYh16EJH5b9v2HuXlhVnMzMihpKyMUX0TaNmoHm99vZ1ubZrw3MQUG2VXi6ZnZPPAzFXMnDQ4qEOtw5klj1pOHpl5BTz+wVoWbt7LWW2bMHlsb87r1rrWzm9CW97hQl5btI2/f72dgqISfnxeJx4c1dPuRmvZUadY4ph+CTw5/my3wwlJ1mFeS44UFvPneZt5fdE2GtaL5rGxyVw3qKONJTf/pU1cAx4c1ZOfDe/KwaPF1rfhksb1Y7isX3s+WJXLo2N722jHANiVqiFlZcq7y3fyxMcb2FtQRNoAD/eP7BEyRfRMaIprEEtcCFbrrUvSUpOYlpHNnFW7SEu1Yon+suRRA9bsPMSj769h2Y6DnO1pzqs3eDnbnt5mTFjo36EFXeMbMy0j25JHACx5nIH9R0/wx0828nb6Dlo1rseT4/sxvn8SUTZKxpiwISJMTPXwf3M2kJlXQLc2NufKH9YQfxpKSst4c8k2hj81n+kZ2fzkvM7Mu3cYaV6PJQ5jwtAPz0kiJkqYkZHtdihhw+48AvRN1j4em7WWDbuPcF7XVky+vDdntW3qdljGmDMQ37Q+I3q24Z1lO7nvBz1sgIsfLHn4adeh4/x+zgZmrcwlsXlDXvxRf0b1aWcTi4yJEGleD5+u28MXG/K4tHc7t8MJeZY8qlFUUsqrC7fywheZlJQpd47oxm3DullJbGMizLAe8cQ3rc/0jBxLHn6w5FGNm99cypeb8rk0uS2PjEm28fjGRKiY6Ciu6p/EKwuzyDtcWGX9MfMf1rBXjVuHduGNmwby8g1eSxzGRLg0bxKlZco7y3a6HUrIC3ryEJGRIrJRRDJF5MEq1vcUkSUiUiQi91WxPlpElovI7ArLWorIXBHZ7HxtEaz4h3RrzYVnxQfr8MaYENIlvgmpnVowIyObulK66XQFNXmISDTwAjAKSAauEZHkSpvtB+4EnjrJYX4BrK+07EFgnqp2B+Y5n40x5oyleT1k7T1KxvYDbocS0oJ95zEQyFTVLFU9AbwNjKu4garmqWo6UFx5ZxFJAsYAr1ZaNQ54w3n/BnBFTQdujKmbRvdNoHG9aKan25yPUwl28kgEKv4L5DjL/PUc8ABQVml5W1XdBeB8rfLBziJyi4hkiEhGfn5+AKc1xtRVjevHMPbs9ny4ehcFRSVuhxOygp08qpoE4VdDoohcBuSp6tLTPbmqvqyqXlX1xsdbv4Uxxj9pqR6OnSjlw1W5bocSsvxOHiLSUUQudt43FBF/plXnABUrjSUB/v5rDAEuF5Ft+Jq7RojI3511e0QkwYklAcjz85jGGFOtczzN6damCdOs6eqk/EoeInIzMBN4yVmUBLznx67pQHcR6Swi9YCrgVn+nFNVH1LVJFXt5Oz3uape56yeBdzovL8ReN+fYxpjjD9EhIleD8t2HCQz74jb4YQkf+88fo7vTuAwgKpu5iT9DBWpaglwO/AJvhFT01V1rYhMEpFJACLSTkRygHuAR0QkR0Tiqjn0E8AlIrIZuMT5bIwxNeaH/ROJiRKmZ+S4HUpI8neGeZGqniiv4yQiMfjZd6Gqc4A5lZZNrfB+N747mVMdYz4wv8LnfcBF/oVujDGBa92kPhf1asO/l+VwvxVL/B5/r8YCEfkV0FBELgFmAB8ELyxjjHFfmtfD3oITfL7BulUr8zd5PAjkA6uBW4E5qvpw0KIyxpgQcOFZ8bRpWt/mfFTB3+Rxh6q+oqoTVHW8qr4iIr8IamTGGOOymOgorhqQxBcb89hzuNDtcEKKv8njxiqW/bgG4zDGmJCU5vVQpvDOMus4r+iUHeYicg1wLdBZRCoOsW0K7AtmYMYYEwo6t27MwE4tmZGRw20XdrUHwDmqG221GNgFtAaerrD8CLAqWEEZY0woSUv1cN+MlaRvO8DAzi3dDicknLLZSlW3q+p8VR2sqgsqvJY5cziMMSbije7bjib1Y5ieYR3n5fydYT5IRNJFpEBETohIqYgcDnZwxhgTChrVi2Hs2Ql8uGoXRwq/VwC8TvK3w/wvwDXAZqAh8FPg+WAFZYwxoSbN6+F4cSkfrtrldighwe8pk6qaCUSraqmqvg4MD15YxhgTWlI8zenepgnTrOkK8D95HHMKG64QkSdF5G6gcRDjMsaYkCIiTEz1sHzHQTbvsWKJ/iaP651tbweO4iuzflWwgjLGmFB0xTnlxRLt7qPa5OE8h/x3qlqoqodV9XFVvcdpxjLGmDqjdZP6XNyrLf9etpPi0soPOK1bqk0eqloKxDvNVsYYU6elpSax7+gJ5q2v28US/S3Jvg1Y5MwyP1q+UFWfCUZQxhgTqoZ2j6dtXH2mZ2Qzsk87t8Nxjb99HrnAbGf7phVexhhTp8RER3FV/yTm1/FiiX7deajq46daLyLPq+odNROSMcaEtjSvhxfnb2Hm0hx+Pryb2+G4oqYejTWkho5jjDEhr1Prxgzs3JIZGdmo+vVQ1Yhjz1U0xpjTMNHrYdu+Y3y7db/bobjCkocxxpyGUd8VS6ybz/moqeRhBe6NMXWKr1hie+asrpvFEmsqefypho5jjDFhY2Kqr1ji7DpYLNHfkuxnicgrIvKpiHxe/ipfr6p/C1qExhgTos5OasZZbZswLb3ulSvxd5LgDGAq8ApQGrxwjDEmfIgIaV4P//vhejbtOcJZbevO9Dd/m61KVHWKqn6rqkvLX0GNzBhjwsAPz0kkNlqYXsfuPvxNHh+IyM9EJEFEWpa/ghqZMcaEgVZOscR3l+/kREndKZbob/K4EbgfWAwsdV4ZwQrKGGPCSZrXw76jJ/h8wx63Q6k1/pYn6RzsQIwxJlwNPSuednENmJaezcg+CW6HUyv8HW0VKyJ3ishM53W7iMQGOzhjjAkH0VHCVQMSWbApn92H6kaxRH+braYAA4AXndcAZ5kxxhhgwgAPZQrvLKsbM879TR6pqnqjqn7uvH4CpPqzo4iMFJGNIpIpIg9Wsb6niCwRkSIRua/C8gYi8q2IrBSRtSLyeIV1k0Vkp4iscF6j/fw+jDEmKDq1bsy5nVsyvY4US/Q3eZSKSNfyDyLSBT/meziPsH0BGAUkA9eISHKlzfYDdwJPVVpeBIxQ1bOBFGCkiAyqsP5ZVU1xXnP8/D6MMSZoJqZ62L7vGN/UgWKJ/iaP+4EvRGS+iCwAPgfu9WO/gUCmqmap6gngbWBcxQ1UNU9V04HiSstVVQucj7HOK/LTuTEmbI3qk0DT+jFMz4j8OR9+JQ9VnQd0x3eHcCfQQ1W/8GPXRKDiVcxxlvlFRKJFZAWQB8xV1W8qrL5dRFaJyGsi0sLfYxpjTLA0rBfN2BRfscTDEV4s8ZTJQ0RGOF+vBMYA3YCuwBhnWXWqqrbr992DqpaqagqQBAwUkT7OqilOHCnALuDpk8R/i4hkiEhGfn6+v6c1xpjTNtHrobC4jNkrI7tYYnV3Hhc6X8dW8brMj+PnAJ4Kn5PwPQ89IKp6EJgPjHQ+73ESSxm+elsDT7Lfy6rqVVVvfHx8oKc1xpiA9UtqRo+2TZkW4U1Xp5wkqKqPOW9/o6pbK64TEX8mDqYD3Z1tdwJXA9f6E5iIxAPFqnpQRBoCFwN/cNYlqGp5Wv8hsMafYxpjTLCJCGmpHn47ex0bdx+hR7vILJbob4f5O1Usm1ndTqpaAtwOfAKsB6ar6loRmSQikwBEpJ2I5AD3AI+ISI6IxAEJ+DrpV+FLQnNVdbZz6CdFZLWzbjhwt5/fhzHGBN13xRIj+O7jlHceItIT6A00q9THEQc08OcEzjDaOZWWTa3wfje+5qzKVgHnnOSY1/tzbmOMcUPLxvW4JNlXLPGXI3tSLybynvhd3XfUA1/fRnP+u7+jP3BzcEMzxpjwNcHrYf/RE8xbH5nFEqvr83gfeF9EBqvqklqKyRhjwt7Q7k6xxIxsRvWNvGKJ1TVbPaCqTwLXisg1lder6p1Bi8wYY8JYdJQwfkASL87PZNeh4yQ0a+h2SDWqumar9c7XDP7zHI+KL2OMMScxwZvkK5a4NPKKJVbXbPWBU5+qj6reX0sxGWNMROjYqjGDurRkekYOPxvWjaioquZNh6dqhwCoaim+EuzGGGMCNDHVw479kVcs0d/xY8tFZJaIXC8iV5a/ghqZMcZEgFF9EmjaIIYZETbnw9/k0RLYB4wgsPIkxhhTpzWIjebys9szZ01kFUv06xnmwKuquqjiAhEZEoR4jDEm4kxM9fCPb3bwwcpcfnRuR7fDqRH+3nk87+cyY4wxlfRNbEbPdk2Znh45TVfVzfMYDJwHxIvIPRVWxQHRwQzMGGMihYiQ5vXwm9nr2LD7MD3bxbkd0hmr7s6jHtAEX5JpWuF1GBgf3NCMMSZyXFFeLDE9MuZ8VDfPYwGwQESOOzPNvyMiE4DNwQzOGGMiRcvG9bg0uQm6yNYAABC3SURBVB3vLs/hl6N6UD8mvBtv/O3zuLqKZQ/VZCDGGBPpJniTOHCsmHnr89wO5YxV1+cxChgNJIrInyusagpEzpgzY4ypBRd0jyehWQOmpWczOsyLJVZ355GLr4ZVIf9d02orvnpXxhhj/FReLPHLzfnkHjzudjhn5JTJQ1VXqurfgG7ASnwPhnoc39P71p9iV2OMMVWYMMCDRkCxxFMmDxE5S0QexfdUv78A2YCo6nBV/UttBGiMMZGkQ6tGnNe1FTOW5lBWpm6Hc9qqa7baAFwEjFXV81X1eaA0+GEZY0zkSvP6iiV+vXWf26GctuqSx1XAbuALEXlFRC4CIqemsDHGuGBkn3ZOscTwbbqqrs/jXVWdCPQE5gN3A21FZIqIXFoL8RljTMRpEBvNuJT2zFm9i0PHw3Pgql/zPFT1qKr+Q1UvA5KAFcCDQY3MGGMi2ERvB4pKyvhgZa7boZwWfycJfkdV96vqS6o6IhgBGWNMXdAnMc5XLDFMn/MRcPIwxhhz5kSEiakeVuUcYv2uw26HEzBLHsYY45IrUhKpFx0VlncfljyMMcYlLRrX45LebXl3+U6KSsJrFoQlD2OMcVGa18PBY8V8ti68iiVa8jDGGBed36017Zs1YFqYNV1Z8jDGGBeVF0tcGGbFEi15GGOMyyZ4fcUSZ4ZRsURLHsYY4zJPy0YM6daKGUuzw6ZYYtCTh4iMFJGNIpIpIt+blS4iPUVkiYgUich9FZY3EJFvRWSliKwVkccrrGspInNFZLPztUWwvw9jjAmmNK+H7P3H+TorPIolBjV5iEg08AIwCkgGrhGR5Eqb7QfuBJ6qtLwIGKGqZwMpwEgRGeSsexCYp6rdgXlYqRRjTJj7Qe92xDWICZs5H8G+8xgIZKpqlqqeAN4GxlXcQFXzVDWdSo+1VZ8C52Os8yq/nxsHvOG8fwO4IkjxG2NMrfAVS0zkozW7w6JYYrCTRyK+B0iVy3GW+UVEokVkBZAHzFXVb5xVbVV1F4Dztc1J9r9FRDJEJCM/P/+0vgFjjKktE1M9FJWUMSsMiiUGO3lU9ewPv3uDVLVUVVPwVfIdKCJ9Ajm5qr6sql5V9cbHxweyqzHG1Lre7ePolRDH9PTQb7oKdvLIATwVPicBAadUVT2I73kiI51Fe0QkAcD5Gl5TM40xpgoiwkRvEqt3HmJdbmgXSwx28kgHuotIZxGpB1wNzPJnRxGJF5HmzvuGwMX4HouLc4wbnfc3Au/XaNTGGOOScWFSLDGoyUNVS4DbgU+A9cB0VV0rIpNEZBKAiLQTkRzgHuAREckRkTggAd/jb1fhS0JzVXW2c+gngEtEZDNwifPZGGPCXovG9bi0d1veWxHaxRJjgn0CVZ0DzKm0bGqF97vxNWdVtgo45yTH3AdcVINhGmNMyEjzepi9ahdz1+3hsn7t3Q6nSjbD3BhjQsz53VqT2Lwh00K449yShzHGhJgop1jiV5l72RmixRIteRhjTAgaP8DXmj8zIzSLJVryMMaYEORp2YghXVuHbLFESx7GGBOiJniTyDlwnCUhWCzRkocxxoSoUC6WaMnDGGNCVIPYaK44xymWeCy0iiVa8jDGmBCW5vVwoqSMWSt3uh3Kf7HkYYwxIaxPYjOSE+KYFmJNV5Y8jDEmxE1M9bBm52HW5h5yO5TvWPIwxpgQNy6lPfViopgRQnM+LHkYY0yIa96oHj/o3Y53l++ksDg0iiVa8jDGmDCQ5k3i0PFi5q7b43YogCUPY4wJC0O6+oolhsqcD0sexhgTBqKihAleX7HEnAPH3A7HkocxxoSL74olLnW/49yShzHGhImkFo04v1trZmTkuF4s0ZKHMcaEkQleDzsPHmfxFneLJVryMMaYMHJpcluaNYx1vePckocxxoSRBrHRXJHSno/Xulss0ZKHMcaEmbRUX7HE910slmjJwxhjwkzv9s3o3T6OaenuNV1Z8jDGmDA0MdXD2tzDrNnpTrFESx7GGBOGxp2d6BRLdOfuw5KHMcaEoWaNYhnZux3vrch1pViiJQ9jjAlTE1M9HDpezKcuFEu05GGMMWFqcJdWJLVoyHQXOs4teRhjTJiKihImDPCwaMtesvfXbrFESx7GGBPGxnvdKZZoycMYY8JYYvOGnN+tNTOX1m6xREsexhgT5tKcYomLtuyttXMGPXmIyEgR2SgimSLyYBXre4rIEhEpEpH7Kiz3iMgXIrJeRNaKyC8qrJssIjtFZIXzGh3s78MYY0LVpb3b0rxRLNMzaq/pKiaYBxeRaOAF4BIgB0gXkVmquq7CZvuBO4ErKu1eAtyrqstEpCmwVETmVtj3WVV9KpjxG2NMOKgfE80VKYn889sdHDx2guaN6gX9nMG+8xgIZKpqlqqeAN4GxlXcQFXzVDUdKK60fJeqLnPeHwHWA4lBjtcYY8JSmtdXLPG95bVTLDHYySMRqDgAOYfTSAAi0gk4B/imwuLbRWSViLwmIi1Ost8tIpIhIhn5+fmBntYYY8JGcvs4+iTG1VrTVbCTh1SxLKDhACLSBHgHuEtVDzuLpwBdgRRgF/B0Vfuq6suq6lVVb3x8fCCnNcaYsDPR62Hdrtoplhjs5JEDeCp8TgJy/d1ZRGLxJY5/qOq/y5er6h5VLVXVMuAVfM1jxhhTp13uFEusjacMBjt5pAPdRaSziNQDrgZm+bOjiAjwV2C9qj5TaV1ChY8/BNbUULzGGBO2mjWKZVSfdry3fGfQiyUGNXmoaglwO/AJvg7v6aq6VkQmicgkABFpJyI5wD3AIyKSIyJxwBDgemBEFUNynxSR1SKyChgO3B3M78MYY8LFRK+Hw4UlfLJ2d1DPE9ShugCqOgeYU2nZ1Arvd+NrzqrsK6ruM0FVr6/JGI0xJlIM6tIKT8uGTM/IZlxK8Aao2gxzY4yJIN8VS8zcF9RiiZY8jDEmwlw1IAkRmBHEYomWPIwxJsIkNm/IBd3jmZmRTWmQiiVa8jDGmAiU5k0i91AhizKDUyzRkocxxkSgS5LLiyUGZ86HJQ9jjIlA5cUSP127hwNHT9T48S15GGNMhJqY6qFnQlP2HCms8WMHfZ6HMcYYd/RKiGPW7ecH5dh252GMMSZgljyMMcYEzJKHMcaYgFnyMMYYEzBLHsYYYwJmycMYY0zALHkYY4wJmCUPY4wxARPV4FRcDDUikg9sP83dWwPBqS52ZiyuwFhcgbG4AhOpcXVU1fjKC+tM8jgTIpKhql6346jM4gqMxRUYiyswdS0ua7YyxhgTMEsexhhjAmbJwz8vux3ASVhcgbG4AmNxBaZOxWV9HsYYYwJmdx7GGGMCZsnDGGNMwCx5OETkNRHJE5E1J1kvIvJnEckUkVUi0j9E4homIodEZIXzerSW4vKIyBcisl5E1orIL6rYptavmZ9x1fo1E5EGIvKtiKx04nq8im3cuF7+xOXKz5hz7mgRWS4is6tY58r/ST/icuv/5DYRWe2cM6OK9TV7vVTVXr5+n6FAf2DNSdaPBj4CBBgEfBMicQ0DZrtwvRKA/s77psAmINnta+ZnXLV+zZxr0MR5Hwt8AwwKgevlT1yu/Iw5574H+GdV53fr/6Qfcbn1f3Ib0PoU62v0etmdh0NVvwT2n2KTccCb6vM10FxEEkIgLleo6i5VXea8PwKsBxIrbVbr18zPuGqdcw0KnI+xzqvyaBU3rpc/cblCRJKAMcCrJ9nElf+TfsQVqmr0elny8F8ikF3hcw4h8EvJMdhpdvhIRHrX9slFpBNwDr6/Wity9ZqdIi5w4Zo5TR0rgDxgrqqGxPXyIy5w52fsOeABoOwk6936+aouLnDneinwqYgsFZFbqlhfo9fLkof/pIplofAX2jJ8tWfOBp4H3qvNk4tIE+Ad4C5VPVx5dRW71Mo1qyYuV66ZqpaqagqQBAwUkT6VNnHlevkRV61fLxG5DMhT1aWn2qyKZUG9Xn7G5db/ySGq2h8YBfxcRIZWWl+j18uSh/9yAE+Fz0lArkuxfEdVD5c3O6jqHCBWRFrXxrlFJBbfL+h/qOq/q9jElWtWXVxuXjPnnAeB+cDISqtc/Rk7WVwuXa8hwOUisg14GxghIn+vtI0b16vauNz6+VLVXOdrHvAuMLDSJjV6vSx5+G8WcIMzYmEQcEhVd7kdlIi0ExFx3g/E92+6rxbOK8BfgfWq+sxJNqv1a+ZPXG5cMxGJF5HmzvuGwMXAhkqbuXG9qo3Ljeulqg+papKqdgKuBj5X1esqbVbr18ufuFz6+WosIk3L3wOXApVHaNbo9Yo57WgjjIj8C98oidYikgM8hq/zEFWdCszBN1ohEzgG/CRE4hoP3CYiJcBx4Gp1hlYE2RDgemC1014O8CugQ4XY3Lhm/sTlxjVLAN4QkWh8v0ymq+psEZlUIS43rpc/cbn1M/Y9IXC9/InLjevVFnjXyVkxwD9V9eNgXi8rT2KMMSZg1mxljDEmYJY8jDHGBMyShzHGmIBZ8jDGGBMwSx7GGGMCZsnDGGNMwCx5mIghIs+KyF0VPn8iIq9W+Py0iNzj57Hmi4jXz223VZ5BLCKXi8iDzvt4EflGfCW8L/Dvuzn5savZ/sci8pdAzmHM6bDkYSLJYuA8ABGJAloDFYvSnQcsqu4gzoS5M6Kqs1T1CefjRcAGVT1HVRee6bGNCQWWPEwkWYSTPPAljTXAERFpISL1gV74ylAvF99Dc15zlpf/hf+oiHwFTCg/oIhEicgbIvK/gQRSfgcgIinAk8Bo8T2kp6GIXCoiS0RkmYjMEF8Rx1O5X3wPbPpWRLo5xx9b4W7mMxFpW0UMVW4jIpOd732+iGSJyJ0V9rlBfA8KWikibznL4kXkHRFJd15DArkWJjJZ8jARwykMVyIiHfAlkSX4yrEPBrz4Hgz1KjBRVfviK+NwW4VDFKrq+ar6tvM5BvgHsElVHznNmFYAjwLTnMq1jYFHgIudCqgZ+B4sdCqHVXUg8Bd85cABvsL30KZz8BXoe6CK/U61TU/gB/iK5z0mIrHiKx3+MDDCqQhb/hTGPwHPqmoqcBXh9xwLEwRW28pEmvK7j/OAZ/A9r+A84BCwE1+C2ORs+wbwc/7zC3lapWO9hK/W0+9qML5BQDKwyKlDVA9fkjuVf1X4+qzzPgmYJr6H+dQDtlax36m2+VBVi4AiEcnDVxtpBDBTVfcCqGr5Q8guBpKdeAHiRKSp87AtU0fZnYeJNOX9Hn3xNVt9je/O4zx8z1k4laNVHGu4iDSowfgE3wOXUpxXsqr+TzX7aBXvnwf+4txB3QpUFeOptimq8L4U3x+SQtXPd4gCBleIOdESh7HkYSLNIuAyYL/zkKP9QHN8CeR1oFN5vwG+6rsLTnGsv+KrRDpDRGrqLv1rYEiFvotGInJWNftMrPC1/C6lGb47KYAbT7KfP9tUNA9IE5FWTmwtneWfAreXb+T045g6zpKHiTSr8Y2y+rrSskOqmoOvDPUMEVmN7zGiU091MOeZIMuAt5wRXCezSkRynNfJnm+CquYDPwb+JSKrnDh7VvM91ReRb/D1QdztLJvsfB8Lgb0n2c+fbSrGthb4HbBARFbia/YDuBPwOh3p64BJ1R3LRD4ryW6MMSZgdudhjDEmYDbayhg/OU1H9Sstvl5VV9fAsd8FOlda/EtV/eRMj21MMFizlTHGmIBZs5UxxpiAWfIwxhgTMEsexhhjAmbJwxhjTMD+H7orF8V9P85QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in features_with_num:\n",
    "    df_train.groupby(feature)['Attrition_rate'].median().plot()\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel(\"Attrition_rate\")\n",
    "  \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "Discrete Var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gender', 'Education_Level', 'Relationship_Status', 'Unit', 'Decision_skill_possess', 'Time_since_promotion', 'Travel_Rate', 'Post_Level', 'Compensation_and_Benefits', 'Work_Life_balance', 'VAR1', 'VAR2', 'VAR3', 'VAR4', 'VAR5', 'VAR6', 'VAR7']\n"
     ]
    }
   ],
   "source": [
    "discrete_feature=[feature for feature in features_with_num and df_train if len(df_train[feature].unique())<10 or df_train[feature].dtypes=='O']\n",
    "discrete_feature.remove('Hometown')\n",
    "discrete_feature.remove('Employee_ID')\n",
    "print(discrete_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bJX1R0lEDnyoNJV0uaa2k5W3qnClphaTlkuZL2q4sP70sWyHpjKY2T0paJulBSRs99dkuPknTJT0qaaWks9odx/Yq23M2No6IiNg4VRPZTsDzwKcpluHPoliWX8UVwPRWOyWNAU4DptieBIwAjpU0iWKJ//7AvsDM8sHFjQ62/THbUwY57mhJOzaVja8aXzmleiFwGDARmC1poqR9JN3U9Bnd6vwiIqK3qr7G5W9t39lYIOnAKg1t3yFpbIU4tpf0OjASWAPsDdxt+6Wyv9uBI4HzK8Z8EPBlSTNsvyJpbtl+RsX49gdW2l5V9r8AOML2N6mexN9G0ixg1vjxg+XTiIjYGFVHZN+tWNY1288C3waeBp4D1tm+FVgOTJO0s6SRFAlot8amwK2SlkiaN8hxrwYWAwskHQ+cBBzTRWhjgGcatleXZYMq47wYmCzp7MHq2F5oe96oUaO6CCMiItrpdB/ZJ4BPAu+X9J8bdv0GxRTgJpP0XuAIYA/gl8DVkr5g+0pJ5wG3AeuBpcCGhqYH2l5TTuvdJukR23c0Htv2+eVI6iJgnO313YQ2SJlbVbb9PHByF8ePiIgh0GlEti3wHoqEt2PD51fA54cohkOBJ2z/zPbrFK+G+SSA7cts72d7GvAC8NhAI9tryu+1wPUUU4FvI2kqMKncf07z/g5W8/YR4K4UU54RETGMdLqP7Hbgdkkvl0/4eIuko2lILJvgaeCAcvrwZeAQyhuwJY22vVbSh4CjgE+U5TsA77L9YvnzZ4BvNMU3GbgUOBx4ArhS0rm2/6xiXPcBE8rX1TwLHAsct2mnGhERQ63qNbJjBykb9DpQM0nzgbuAvSStljSnLF8kaRfb91C8EuYBYFkZ0yVl82slPQQsBE6x/Yuy/APATyQtBe4Fbra9uKnrkcDRth+3/SZwAvBU1fhsbwBOBW6heMLJVbZXVDnniIjYfDpdIzuMYpHFGEkXNOzaEXi9Sge23/GMxrJ8RsPP5zDI1J/tqS3arqJYkt+u3zubtl+nGKFViq/ctwhY1K6fiIjor07L79cAS4DPlt8Ddgde6lVQERERVbWdWrS91PYVwHiKVYMfBb4OHMy/PVA4IiKibzpNLX6Y4vrYbIone/wjINsHb4bYIiIiOuo0tfgI8GNglu2VUDwXsedRRUREVNRp1eLngH8FfijpUkmHMPiNwhEREX3R6RrZ9bZ/H/gI8CPgTOADki6S9JnNEF9ERERble4js/1r2/9geybFEy4eBNq+1iQiImJzqHpD9Ftsv2D7b2x/uhcBRUREdKPrRBYRETGcJJFFREStJZFFREStJZFFREStJZFFREStJZFFREStJZFFREStJZFFREStJZFFREStJZFFREStJZENEUl7SrpM0jX9jiUiYmvS10Qm6XJJayUtb1PnTEkrJC2XNF/SdmX56WXZCkln9CIOSdMlPSpppaS2D0m2vcr2nE2JIyIiutfvEdkVwPRWOyWNAU4DptieBIwAjpU0CZgL7A/sC8yUNKGp7WhJOzaVja8ah6QRwIXAYcBEYLakieW+fSTd1PQZXfGcIyJiCPU1kdm+A3ihQ7VtgO0lbQOMBNYAewN3237J9gbgduDIpnYHATc0jODmAhd0Ecf+wMpypPUasAA4oqy/zPbMps/aTucraZakS9atW9epakREVNTvEVlbtp8Fvg08DTwHrLN9K7AcmCZpZ0kjgRnAbk1trwYWAwskHQ+cBBzTRfdjgGcatleXZYMqY7kYmCzp7Bbns9D2vFGjRnURRkREtLNNvwNoR9J7KUZBewC/BK6W9AXbV0o6D7gNWA8sBTY0t7d9vqQFwEXAONvru+l+kDK3qmz7eeDkLo4fERFDYFiPyIBDgSds/8z268B1wCcBbF9mez/b0yimBR9rbixpKjAJuB44p8u+V/P2Ud6uFNOaERExjAz3RPY0cICkkZIEHAI8DMVijvL7Q8BRwPzGhpImA5dSjOhOBHaSdG4Xfd8HTJC0h6RtgWOBGzfxfCIiYoj1e/n9fOAuYC9JqyXNKcsXSdrF9j3ANcADwDKKeC8pm18r6SFgIXCK7V80HX4kcLTtx22/CZwAPFU1jnIRyanALRTJ8yrbK4bu7CMiYijIbnnZJ3pkypQpvv/++/sdRkRtjT3r5n6HsEV58luH9zuESiQtsT2luXy4Ty1GRES0lUQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQWERG1lkQ2RCTtKekySdf0O5aIiK1JzxOZpMslrZW0vE2dMyWtkLRc0nxJ27UrL/c9KWmZpAcl3d+L+CRNl/SopJWSzmp3HNurbM/Z2DgiImLjbI4R2RXA9FY7JY0BTgOm2J4EjACObVXe1Pxg2x+zPWWQ446WtGNT2fiq8UkaAVwIHAZMBGZLmihpH0k3NX1Gtzq/iIjorW163YHtOySNrRDH9pJeB0YCazqUV3EQ8GVJM2y/ImkucCQwo2J8+wMrba8CkLQAOML2N4GZXcQRERE91PdrZLafBb4NPA08B6yzfWur8samwK2SlkiaN8hxrwYWAwskHQ+cBBzTRWhjgGcatleXZYOStLOki4HJks5uUWeWpEvWrVvXRRgREdFO3xOZpPcCRwB7ALsAO0j6QqvyhqYH2t6PYurvFEnTmo9t+3zgFeAi4LO213cT2iBlblXZ9vO2T7Y9rhy1DVZnoe15o0aN6iKMiIhop++JDDgUeML2z2y/DlwHfLJNOQC215Tfa4HrKaYC30bSVGBSuf+cLuNaDezWsL0r3U1tRkTEZjAcEtnTwAGSRkoScAjwcJtyJO0wsJBD0g7AZ4C3rTqUNBm4lGJUdyKwk6Rzu4jrPmCCpD0kbUux0OTGTTjPiIjogc2x/H4+cBewl6TVkuaU5Ysk7WL7HuAa4AFgWRnTJa3Ky8N+APiJpKXAvcDNthc3dT0SONr247bfBE4Anqoan+0NwKnALRQJ9CrbK4bmXyUiIoaK7JaXfaJHpkyZ4vvv3+hb3yK2emPPurnfIWxRnvzW4f0OoRJJSwa73Wo4TC1GRERstCSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotSSyiIiotW36HUAMX3nn09Cpy/ueIuooI7KIiKi1JLKIiKi1JLKIiKi1JLIhImlPSZdJuqbfsUREbE36msgkXS5praTlbeqcKWmFpOWS5kvarl35UMYhabqkRyWtlHRWu2PYXmV7zsbGEBERG6ffI7IrgOmtdkoaA5wGTLE9CRgBHNuqvKntaEk7NpWNrxqHpBHAhcBhwERgtqSJ5b59JN3U9Bld8ZwjImII9XX5ve07JI3tUG0bYHtJrwMjgTUdygccBHxZ0gzbr0iaCxwJzKgYx/7ASturACQtAI4AHrK9DJhZ6SQbSJoFzBo/vlU+jYiIbvV7RNaW7WeBbwNPA88B62zf2qq8qe3VwGJggaTjgZOAY7rofgzwTMP26rJsUJJ2lnQxMFnS2S3OZ6HteaNGjeoijIiIaGdYJzJJ76UYBe0B7ALsIOkLrcqb29s+H3gFuAj4rO313XQ/SJlbVbb9vO2TbY+z/c0u+omIiE0w3J/scSjwhO2fAUi6Dvgk8GqL8isbG0uaCkwCrgfOAU7tou/VwG4N27vyzunLjbJkyZKfS3pqKI4VALwP+Hm/g2hH5/U7guiTYf+7CbX6/dx9sMLhnsieBg6QNBJ4GTgEuL9N+VskTQYuBQ4HngCulHSu7T+r2Pd9wARJewDPUiwmOW7TTwlsv38ojhMFSffbntLvOCKa5Xdz8+j38vv5wF3AXpJWS5pTli+StIvte4BrgAeAZRTxXtKqvOnwI4GjbT9u+03gBGDQUdBgcdjeQDGCuwV4GLjK9oqhPP+IiNh0slte9omohfxfbwxX+d3cPIb1Yo+IippH4xHDRX43N4OMyCIiotYyIouIiFpLIouIiFpLIouIiFpLIouIiFob7jdER7yNpKPa7bd93eaKJaIVSbsDE2x/X9L2wDa2X+x3XFuqJLKom1nl92iKx5L9oNw+GPgRkEQWfVW+aWMesBMwjuLxdhdTPIEoeiCJLGrF9okAkm4CJtp+rtz+IMX74yL67RSK10DdA2D7sbyvsLdyjSzqauxAEiv9P+DD/QomosGrtl8b2JC0DW3enBGbLiOyqKsfSboFmE/xR+JY4If9DSkCgNsl/QnFi39/F/hDYGGfY9qi5ckeUVuSjgSmlZt32L6+n/FEAEh6FzAH+AzFew1vAf7W+WPbM0lkUVtNK8NGAiOyMixi65OpxailQVaGjSErw6KPJC2j/Vvkf2szhrNVSSKLusrKsBhuZvY7gK1VElnU1au2X5MEZGVY9J/tQV/cG72X5fdRV80rw64mK8NiGJD0oqRfNX2ekXS9pD37Hd+WKIs9opayMiyGK0lfB9YA/5vid/NY4N8DjwJftv2p/kW3ZUoii4gYQpLusf07TWV32z5A0lLb+/Yrti1VrpFFrUi6yvYxrVaIZWVYDANvSjoGuKbc/nzDvowceiAjsqgVSR+0/Vx5D9k75IJ79Ft5Hew7wCcoEtfdwJnAs8DHbf+kj+FtkZLIonYkjQBusX1ov2OJiP7L1GLUju03JL0kaZTtdf2OJ6KRpPcDc4GxNPyNtX1Sv2La0iWRRV29AiyTdBvw64FC26f1L6QIAG4Afgx8H3ijz7FsFZLIoq5uLj8Rw81I2/+l30FsTZLIonbKa2RfzDWyGKZukjTD9qJ+B7K1yGKPqCVJN1Iks1wji2FF0ovADsCrwOsUN0Xb9m/0NbAtWEZkUVe5RhbDku0d+x3D1iaJLOoq18hiWJH0EduPSNpvsP22H9jcMW0tMrUYETEEJF1ie56kHzYUv/UH1van+xDWViGJLGpJ0gTgm8BEYLuBctt5unj0haT9gadt/2u5fQLwOeBJ4Gu2X+hjeFu0vMYl6urvgIuADcDBwP8C/r6vEcXW7mLgNQBJ0yj+R+t7wDrgkj7GtcVLIou62t72P1PMKjxl+2tApm6in0Y0jLp+H7jE9rW2/yswvo9xbfGy2CPq6pXynWSPSTqV4oGso/scU2zdRkjaxvYG4BBgXsO+/K3tofzjRl2dAYwETgP+O8Vo7IS+RhRbu/kUby7/OfAyxWOqkDSeYnoxeiSLPSIihoikA4APArfa/nVZ9mHgPVl+3ztJZFEr5RM9WrL92c0VS0QMD5lajLr5BPAMxTTOPRSP/4mIrVhGZFEr5QODfxeYDfwWxdM95tte0dfAIqJvsvw+asX2G7YX2z4BOABYCfxI0lf6HFpE9EmmFqN2JP074HCKUdlY4ALgun7GFBH9k6nFqBVJ3wMmAf8ELLC9vM8hRUSfJZFFrUh6k397bUvjL2/e+RSxlUoii4iIWstij4iIqLUksoiIqLUksoiIqLUksoghJOkNSQ9KWi5poaTf7FD/a5L+qEOd35M0sWH7G5IOHcKYPyXpphb7/rax7y6OeYCke8p/i4clfa2hr09WjKljvQhIIosYai/b/pjtScALwClDcMzfo3gTNgC2/5vt7w/BcTuy/SXbD21E0+8B82x/jOJ2iavK8k8BVRJU1XoRSWQRPXQXMAZA0jhJiyUtkfRjSR9prixprqT7JC2VdK2kkeWo5LPA/yxHN+MkXSHp82WbQyT9VNIySZeXN4sj6UlJX5f0QLnvI2X5QeVxHizb7Vh2/x5J10h6RNI/SFJZ/0eSppQ/r5f05+Ux/1nS+9uc+2jgOXjraSwPSRoLnAycWfY/VdKscuT2U0nfl/SBFvXeOueBWMrvD0q6o2EUPHVj/kNFvSWRRfRA+UzIQ4CBp/VfAnzF9seBPwL+epBm19n+bdv7Ag8Dc2z/S3mMr5Yjvccb+tgOuAL4fdv7UDyp58sNx/u57f2Ai8o+Kb9PKUdKUynemwUwmeIdbxOBPYEDB4lvB+CB8pi3A+e0+Sf4S+BRSddL+gNJ28T5PKIAAAJ+SURBVNl+ErgY+MvyXH4M/AQ4wPZkYAHwxy3qtXIccEt5PvsCD7apG1uoJLKIobW9pAeB54GdgNskvYdimuzqct/fULyzqtmkcrS2DDge+GiHvvYCnrD9f8vt7wHTGvYPPLZrCcWjvADuBP5C0mnAb5ZvMwa41/Zq229SJIOxvNObwD+WP18J/IdWgdn+BjAFuJUi2SxuUXVX4JbynL9K53Nudh9wYnkNbh/bL3bZPrYASWQRQ+vlcnSwO7AtxTWydwG/LEcXA5+9B2l7BXBqObr6OrBdh746vcLm1fL7Dcrnqtr+FvAlYHvg7oYpzlcb2r1Vv4O2T1Ow/bjtiyhGpvtK2nmQat8F/qo85z+g9TlvoPx7VU57blv2cQdF8n4W+HtJ/7FC3LGFSSKL6AHb64DTKKbyXgaekHQ0FH+IJe07SLMdgeckvZtiRDbgxXJfs0eAsZLGl9tfpJjya0nSONvLbJ8H3A+841pdG+8CBq5THUcxLdiqn8MHrrMBEyiS4y9557mMokhCACc0lDfXexL4ePnzEcC7y352B9bavhS4DNivi/OJLUQSWUSP2P4psBQ4liIxzZG0FFhB8ce42X+leFnobRRJasAC4KvlgohxDcd/BTiRYspyGcXU38UdwjqjXBSxlCLB/lMXp/Rr4KOSlgCfBr7Rpu4XKa6RPQj8PXC87TeAhcCRA4s4gK+V8f8Y+HlD++Z6lwIHSboX+B3+7XmbnwIelPRT4HPAd7o4n9hC5FmLEVGJpPW239PvOCKaZUQWERG1lhFZRGw0SRfyzqX637H9d/2IJ7ZOSWQREVFrmVqMiIhaSyKLiIhaSyKLiIhaSyKLiIha+/9E5PLdNmbDywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fpLakPSIioqvKrlr8f5IOByZTC7Kf2/5tRyuLiIgooWWQSfqE7R9K+lRD16GSsH1TB2uLiIgY0EAzsuOBHwKnNekztVtGRUREdE3LICs+jAzwP20/Wd8nqSMfko6IiGhH2VWLP2jStnwwC4mIiNgdA10j+xDwR8C4hutkvw/s3cnCIiIiyhjoGtlkat+E/O9463Wyl4B8p1ZERHTdQNfIbgFukfQx2w8MUU0RERGlDXRq8b/avhL4tKS33SHe9sUdqywiIqKEgU4tbih+7vF3j0VERHTCQKcWV0gaBUy1/aUhqikiIqK0AZff234D+OgQ1BIREdG2sjcN/ldJtwLLgJd3NuYWVRER0W1lg2w/4AXgE3VtuUVVRER0Xdkg+yfb99c3SDqmA/VERES0pewtqr5Zsi0iImJIDfQ5so8BHwfeL+kLdV2/D4zqZGFVU3xr9peBcbb/U7friYgYKQaake0FvIda4O1b93gRKPXHWtL1krZKWtdizOclrZe0TtISSXtLOljSjyRtKPouadhms6S1ktZI2u3PubWqT9JMSY9J2ihpYav92N5ke/7u1hEREbtnoM+R3QPcI+nV4g4fvyPpTODxEse4AfgW8N1mnZImABcDU2y/KulGYC5wB/BF26sl7QusknSX7Z/VbX6C7V/1s9/xwKu2X6prO8z2xjL1FZ+fuwr4JNALPFys3BwFXNGwj/Nsb+3vDYiIiM4pe41sbpO2y8psaPte4NcDDBsN7CNpNDAG2GL7Oduri328RO0uIxNK1gu1LwW9RdLeAJLOB77RRn0zgI3FTOt1YCkwx/Za27MbHqVCTNJpkhb39fW18TIiIqKVlkEm6RRJ3wQmSPpG3eM7wPbBKMD2s8BXgaeB54A+23c21DERmAY8WL8pcKekVZIWNNnvMmAlsFTS2cB5wFltlDYBeKbueS8tglTS/pKuAaZJahrytlfYXjBu3Lg2yoiIiFYGWn6/BVgFnF783OmDwCuDUYCk9wJzgEnAvwHLJH3G9veK/vdQ+2LPS22/WLfpMba3FKcQ75L082J29Tu2r5S0FLgaONT2tnZKa9Lm/gbbfgG4oI39R0TEIGg5I7P9qO0bgMOAR6l9yebfASew64bCe+ok4Enbz9veTu1D1h8HkPRuaiH2/ca7iNjeUvzcCtxM7VTgW0g6Fpha9C9qs65e4OC65wdRC/aIiBhGBjq1+IeS/jvwU2oLIp4BZPsE298apBqeBo6WNEaSgBOBDcXv1wEbbH+toa6xxQIQJI0FTgbWNYyZBlxLbbZ3LrCfpMvbqOth4HBJkyTtRe064a279QojIqJjBlrs8XNqwXKa7T+x/U3gjXYOIGkJ8AAwWVKvpPlFe4+kA20/CCwHVgNri5oWA8cAnwU+USyxXyNpVrHbA4D7JD0KPATcbntlw6HHAGfafsL2m8A5wFNl67O9A7iI2urJDcCNtte389ojIqLzBrpG9ufUZiI/krSS2sq9ZteO+mX7bV/IWbTPqvt9EW8/9Xdff8eyvQk4coDj3t/wfDu1GVqp+oq+HqCn1XEiIqK7BrpGdrPtvwA+BNwNfB44QNLVkk4egvoiIiJaKvU5Mtsv2/6+7dnUFj2sAVre6SIiImIolP1A9O/Y/rXtb9v+xMCjIyIiOqvtIIuIiBhOEmQREVFpCbKIiKi0BFlERFRagiwiIiotQRYREZWWIIuIiEpLkEVERKUlyCIiotISZBERUWkJsoiIqLQEWUREVFqCLCIiKi1BFhERlZYgi4iISkuQRUREpSXIIiKi0hJkg0TSIZKuk7S827VERIwkwzLIJF0vaaukdS3GfF7SeknrJC2RtPdgH0vSTEmPSdooaWGr/djeZHv+7tQQERG7b1gGGXADMLO/TkkTgIuB6banAqOAuQ1jxkvat6HtsLLHkjQKuAo4BZgCzJM0RdIRkm5reIxv69VFRMSgGd3tApqxfa+kiQMMGw3sI2k7MAbY0tB/PPA5SbNsvybpfOAMYFbJY80ANtreBCBpKTDH9hXA7PZeUUREdMpwnZG1ZPtZ4KvA08BzQJ/tOxvGLANWAkslnQ2cB5zVxmEmAM/UPe8t2pqStL+ka4Bpki7rZ8xpkhb39fW1UUZERLRSySCT9F5gDjAJOBAYK+kzjeNsXwm8BlwNnG57WzuHadLm/gbbfsH2BbYPLWZtzcassL1g3LhxbZQRERGtVDLIgJOAJ20/b3s7cBPw8cZBko4FpgI3A4vaPEYvcHDd84N4++nLiIjosqoG2dPA0ZLGSBJwIrChfoCkacC11GZu5wL7Sbq8jWM8DBwuaZKkvagtJrl1UKqPiIhBMyyDTNIS4AFgsqReSfOL9h5JB9p+EFgOrAbWUnsdixt2MwY40/YTtt8EzgGeKnss2zuAi4A7qIXkjbbXd+DlRkTEHhiuqxbn9dM+q+73RbQ4XWj7/obn26nN0Eodq+jrAXpKlBwREV0yLGdkERERZSXIIiKi0hJkERFRaQmyiIiotARZRERUWoIsIiIqLUEWERGVliCLiIhKS5BFRESlJcgiIqLSEmQREVFpCbKIiKi0BFlERFRagiwiIiotQRYREZWWIIuIiEpLkEVERKUlyCIiotISZBERUWkJsoiIqLQEWUREVFqCbJBIOkTSdZKWd7uWiIiRZEiCTNL1krZKWtekb7KkNXWPFyVdWqJvs6S1RfsjnahP0kxJj0naKGlhq33Y3mR7/p7UERER7Rs9RMe5AfgW8N3GDtuPAUcBSBoFPAvcPFBf4QTbv2p2QEnjgVdtv1TXdpjtjWXqK453FfBJoBd4WNKtwCjgiobtz7O9tflLj4iIThqSILN9r6SJJYaeCDxh+6k2+5o5HvicpFm2X5N0PnAGMKtkfTOAjbY3AUhaCsyxfQUwu2QNERHRYcPtGtlcYEnJPgN3SlolaUHjYNvLgJXAUklnA+cBZ7VRywTgmbrnvUVbU5L2l3QNME3SZf2MOU3S4r6+vjbKiIiIVoZNkEnaCzgdWFay7xjbHwFOAS6UdFzjdravBF4DrgZOt72tnZKatLm/wbZfsH2B7UOLWVuzMStsLxg3blwbZURERCvDJsioBdJq278s02d7S/FzK7XrZjMaN5J0LDC16F/UZj29wMF1zw8CtrS5j4iI6LDhFGTz6P+04lv6JI2VtO/O34GTgcYVh9OAa4E5wLnAfpIub6Oeh4HDJU0qZoRzgVvb2D4iIobAUC2/XwI8AEyW1CtpftHeI+lASWOorQ68qcm2zfoOAO6T9CjwEHC77ZUNm44BzrT9hO03gXOApgtFmtVnewdwEXAHsAG40fb63X0PIiKiM4Zq1eK8ftrrVxDu38+YVxr7ipWERw5wzPsbnm+nNkNrp74eoKfVcSIioruG06nFiIiItiXIIiKi0hJkERFRaQmyiIiotARZRERUWoIsIiIqbajufh8REV0wceHt3S4BgM1fObVj+86MLCIiKi1BFhERlZYgi4iISkuQRUREpSXIIiKi0hJkERFRaQmyiIiotARZRERUmmx3u4YRR9Lz9PMln0PofcCvulzDcJH3Ype8F7vkvdhluLwXH7T9/sbGBNkIJekR29O7XcdwkPdil7wXu+S92GW4vxc5tRgREZWWIIuIiEpLkI1ci7tdwDCS92KXvBe75L3YZVi/F7lGFhERlZYZWUREVFqCLCIiKi1BFhERlZYgixFH0ocknSjpPQ3tM7tVU7dImiHpj4vfp0j6gqRZ3a5rOJD03W7XMBxI+pPi/xcnd7uW/mSxxwgn6Vzb3+l2HUNF0sXAhcAG4CjgEtu3FH2rbX+km/UNJUmLgFOA0cBdwH8E7gZOAu6w/ffdq25oSbq1sQk4AfghgO3Th7yoLpH0kO0Zxe/nU/vv5WbgZGCF7a90s75mEmQjnKSnbX+g23UMFUlrgY/Z3iZpIrAc+D+2vy7pX21P62qBQ6h4L44Cfg/4BXCQ7Rcl7QM8aPs/dLXAISRpNfAz4J8AUwuyJcBcANv3dK+6oVX/34Gkh4FZtp+XNBb4ie0julvh243udgHReZJ+2l8XcMBQ1jIMjLK9DcD2Zkl/CiyX9EFq78dIssP2G8Arkp6w/SKA7Vclvdnl2obadOAS4MvAl2yvkfTqSAqwOu+S9F5ql55k+3kA2y9L2tHd0ppLkI0MBwB/BvymoV3Aj4e+nK76haSjbK8BKGZms4HrgWH3L80Oe13SGNuvAB/d2ShpHDCigsz2m8D/lrSs+PlLRu7fx3HAKmp/HyzpD2z/orimPCz/sTdS/4caaW4D3rPzj3c9SXcPfTld9ZfAW/5VaXsH8JeSvt2dkrrmONu/hd/9Id/p3cA53Smpu2z3AmdKOhV4sdv1dIPtif10vQmcMYSllJZrZBERUWlZfh8REZWWIIuIiEpLkEW8Q0m6W9KfNbRdKukfJb1f0nZJf93Qv1nSWkk/lXRPsZqzsW+NpEeG6nVEDCRBFvHO9bvPQdWZW7SfCfwEmNdkuxOKz5DdDfxtk76jhvO3BcfIkyCLeOdaDsyW9HsAxQfADwTuoxZgXwQOkjShn+0fAPrrixg2EmQR71C2XwAeAnbeQ3Iu8C/AQcAf2H4IuBH4i352MRP4v/W7BO6UtErSgs5UHdG+BFnEO1v96cW5dc9vLNqW8vbTiz+StJXaPRf/ua79mOJelKcAF0o6rmNVR7QhnyOLeAcr7sawidrsaontycV9BQ8AthfDDgT+yPbjkjZTu13Ty8ANwLO2v9Bkv/8D2Gb7qx1/EREDyIws4h2suK/k3dRuwbVE0mRgrO0JticWd3G4goZFIbZfBS6ldseT/SSNlbQvQHHz2JOBdUP3SiL6lyCLeOdbAhzJrtOINzf0/4AmqxdtP1dseyG1Gdx9kh6ldt3tdtsrO1l0RFk5tRgREZWWGVlERFRagiwiIiotQRYREZWWIIuIiEpLkEVERKUlyCIiotISZBERUWkJsoiIqLT/D6VdoUdpWuT6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in discrete_feature:\n",
    "    df_train.groupby(feature)['Attrition_rate'].mean().plot.bar()\n",
    "    plt.xlabel(feature)\n",
    "    plt.yscale('log')\n",
    "    plt.ylabel('Attrition_rate')\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using log transformation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Age', 'Time_of_service', 'growth_rate', 'Pay_Scale', 'Attrition_rate']\n"
     ]
    }
   ],
   "source": [
    "continous_feature=[feature for feature in features_with_num if feature not in discrete_feature]\n",
    "print(continous_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in continous_feature:\n",
    "    df_train[feature].hist(bins=25)\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Count')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7000 entries, 0 to 6999\n",
      "Data columns (total 24 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                7000 non-null   object \n",
      " 1   Gender                     7000 non-null   object \n",
      " 2   Age                        6588 non-null   float64\n",
      " 3   Education_Level            7000 non-null   int64  \n",
      " 4   Relationship_Status        7000 non-null   object \n",
      " 5   Hometown                   7000 non-null   object \n",
      " 6   Unit                       7000 non-null   object \n",
      " 7   Decision_skill_possess     7000 non-null   object \n",
      " 8   Time_of_service            6856 non-null   float64\n",
      " 9   Time_since_promotion       7000 non-null   int64  \n",
      " 10  growth_rate                7000 non-null   int64  \n",
      " 11  Travel_Rate                7000 non-null   int64  \n",
      " 12  Post_Level                 7000 non-null   int64  \n",
      " 13  Pay_Scale                  6991 non-null   float64\n",
      " 14  Compensation_and_Benefits  7000 non-null   object \n",
      " 15  Work_Life_balance          6989 non-null   float64\n",
      " 16  VAR1                       7000 non-null   int64  \n",
      " 17  VAR2                       6423 non-null   float64\n",
      " 18  VAR3                       7000 non-null   float64\n",
      " 19  VAR4                       6344 non-null   float64\n",
      " 20  VAR5                       7000 non-null   int64  \n",
      " 21  VAR6                       7000 non-null   int64  \n",
      " 22  VAR7                       7000 non-null   int64  \n",
      " 23  Attrition_rate             7000 non-null   float64\n",
      "dtypes: float64(8), int64(9), object(7)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_value=df_train[features_with_na].mean()\n",
    "df_train.fillna(mean_value, inplace=True)\n",
    "\n",
    "mean_value_test=df_test[features_with_na_test].mean()\n",
    "df_test.fillna(mean_value_test, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7000 entries, 0 to 6999\n",
      "Data columns (total 24 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                7000 non-null   object \n",
      " 1   Gender                     7000 non-null   object \n",
      " 2   Age                        7000 non-null   float64\n",
      " 3   Education_Level            7000 non-null   int64  \n",
      " 4   Relationship_Status        7000 non-null   object \n",
      " 5   Hometown                   7000 non-null   object \n",
      " 6   Unit                       7000 non-null   object \n",
      " 7   Decision_skill_possess     7000 non-null   object \n",
      " 8   Time_of_service            7000 non-null   float64\n",
      " 9   Time_since_promotion       7000 non-null   int64  \n",
      " 10  growth_rate                7000 non-null   int64  \n",
      " 11  Travel_Rate                7000 non-null   int64  \n",
      " 12  Post_Level                 7000 non-null   int64  \n",
      " 13  Pay_Scale                  7000 non-null   float64\n",
      " 14  Compensation_and_Benefits  7000 non-null   object \n",
      " 15  Work_Life_balance          7000 non-null   float64\n",
      " 16  VAR1                       7000 non-null   int64  \n",
      " 17  VAR2                       7000 non-null   float64\n",
      " 18  VAR3                       7000 non-null   float64\n",
      " 19  VAR4                       7000 non-null   float64\n",
      " 20  VAR5                       7000 non-null   int64  \n",
      " 21  VAR6                       7000 non-null   int64  \n",
      " 22  VAR7                       7000 non-null   int64  \n",
      " 23  Attrition_rate             7000 non-null   float64\n",
      "dtypes: float64(8), int64(9), object(7)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Gender', 'Relationship_Status', 'Hometown', 'Unit', 'Decision_skill_possess', 'Compensation_and_Benefits']\n"
     ]
    }
   ],
   "source": [
    "object_features=[feature for feature in df_train if df_train[feature].dtypes=='O']\n",
    "object_features.remove('Employee_ID')\n",
    "print(object_features)\n",
    "\n",
    "object_features_test=[feature for feature in df_test if df_test[feature].dtypes=='O']\n",
    "object_features_test.remove('Employee_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Encoding categ features\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder=LabelEncoder()\n",
    "\n",
    "for f in object_features:\n",
    "   df_train[f]=encoder.fit_transform(df_train[f])\n",
    "\n",
    "\n",
    "for f in object_features_test:\n",
    "    df_test[f]=encoder.fit_transform(df_test[f])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7000 entries, 0 to 6999\n",
      "Data columns (total 24 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                7000 non-null   object \n",
      " 1   Gender                     7000 non-null   int64  \n",
      " 2   Age                        7000 non-null   float64\n",
      " 3   Education_Level            7000 non-null   int64  \n",
      " 4   Relationship_Status        7000 non-null   int64  \n",
      " 5   Hometown                   7000 non-null   int64  \n",
      " 6   Unit                       7000 non-null   int64  \n",
      " 7   Decision_skill_possess     7000 non-null   int64  \n",
      " 8   Time_of_service            7000 non-null   float64\n",
      " 9   Time_since_promotion       7000 non-null   int64  \n",
      " 10  growth_rate                7000 non-null   int64  \n",
      " 11  Travel_Rate                7000 non-null   int64  \n",
      " 12  Post_Level                 7000 non-null   int64  \n",
      " 13  Pay_Scale                  7000 non-null   float64\n",
      " 14  Compensation_and_Benefits  7000 non-null   int64  \n",
      " 15  Work_Life_balance          7000 non-null   float64\n",
      " 16  VAR1                       7000 non-null   int64  \n",
      " 17  VAR2                       7000 non-null   float64\n",
      " 18  VAR3                       7000 non-null   float64\n",
      " 19  VAR4                       7000 non-null   float64\n",
      " 20  VAR5                       7000 non-null   int64  \n",
      " 21  VAR6                       7000 non-null   int64  \n",
      " 22  VAR7                       7000 non-null   int64  \n",
      " 23  Attrition_rate             7000 non-null   float64\n",
      "dtypes: float64(8), int64(15), object(1)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3000 entries, 0 to 2999\n",
      "Data columns (total 23 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Employee_ID                3000 non-null   object \n",
      " 1   Gender                     3000 non-null   int64  \n",
      " 2   Age                        3000 non-null   float64\n",
      " 3   Education_Level            3000 non-null   int64  \n",
      " 4   Relationship_Status        3000 non-null   int64  \n",
      " 5   Hometown                   3000 non-null   int64  \n",
      " 6   Unit                       3000 non-null   int64  \n",
      " 7   Decision_skill_possess     3000 non-null   int64  \n",
      " 8   Time_of_service            3000 non-null   float64\n",
      " 9   Time_since_promotion       3000 non-null   int64  \n",
      " 10  growth_rate                3000 non-null   int64  \n",
      " 11  Travel_Rate                3000 non-null   int64  \n",
      " 12  Post_Level                 3000 non-null   int64  \n",
      " 13  Pay_Scale                  3000 non-null   float64\n",
      " 14  Compensation_and_Benefits  3000 non-null   int64  \n",
      " 15  Work_Life_balance          3000 non-null   float64\n",
      " 16  VAR1                       3000 non-null   int64  \n",
      " 17  VAR2                       3000 non-null   float64\n",
      " 18  VAR3                       3000 non-null   float64\n",
      " 19  VAR4                       3000 non-null   float64\n",
      " 20  VAR5                       3000 non-null   int64  \n",
      " 21  VAR6                       3000 non-null   int64  \n",
      " 22  VAR7                       3000 non-null   int64  \n",
      "dtypes: float64(7), int64(15), object(1)\n",
      "memory usage: 539.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale_feat= [f for f in df_train.columns if f not in ['Employee_ID','Attrition_rate']]\n",
    "df_train[scale_feat]=scaler.fit_transform(df_train[scale_feat])\n",
    "\n",
    "scale_feat_test=[f for f in df_test.columns if f not in ['Employee_ID','Attrition_rate']]\n",
    "df_test[scale_feat_test]=scaler.fit_transform(df_test[scale_feat_test])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# MODEL FITTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "feat=['Gender', 'Age', 'Education_Level', 'Relationship_Status', 'Hometown', 'Unit', 'Decision_skill_possess','Time_of_service', 'Time_since_promotion', 'growth_rate', 'Travel_Rate','Post_Level', 'Pay_Scale', 'Compensation_and_Benefits','Work_Life_balance', 'VAR1', 'VAR2', 'VAR3', 'VAR4', 'VAR5', 'VAR6','VAR7']\n",
    "X=df_train[feat]\n",
    "y=df_train.Attrition_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test= train_test_split(X,y, test_size=0.33, random_state=42) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import xgboost\n",
    "\n",
    "regressor=xgboost.XGBRegressor()\n",
    "\n",
    "params={\n",
    " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
    " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
    " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
    " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
    " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:    8.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                          colsample_bylevel=None,\n",
       "                                          colsample_bynode=None,\n",
       "                                          colsample_bytree=None, gamma=None,\n",
       "                                          gpu_id=None, importance_type='gain',\n",
       "                                          interaction_constraints=None,\n",
       "                                          learning_rate=None,\n",
       "                                          max_delta_step=None, max_depth=None,\n",
       "                                          min_child_weight=None, missing=nan,\n",
       "                                          monotone_constraints=None,\n",
       "                                          n_estimators=100, n...\n",
       "                                          scale_pos_weight=None, subsample=None,\n",
       "                                          tree_method=None,\n",
       "                                          validate_parameters=None,\n",
       "                                          verbosity=None),\n",
       "                   n_iter=5, n_jobs=-1,\n",
       "                   param_distributions={'colsample_bytree': [0.3, 0.4, 0.5,\n",
       "                                                             0.7],\n",
       "                                        'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],\n",
       "                                        'learning_rate': [0.05, 0.1, 0.15, 0.2,\n",
       "                                                          0.25, 0.3],\n",
       "                                        'max_depth': [3, 4, 5, 6, 8, 10, 12,\n",
       "                                                      15],\n",
       "                                        'min_child_weight': [1, 3, 5, 7]},\n",
       "                   scoring='neg_root_mean_squared_error', verbose=3)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search=RandomizedSearchCV(regressor,param_distributions=params,n_iter=5,scoring='neg_root_mean_squared_error',n_jobs=-1,cv=5,verbose=3)\n",
    "random_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'min_child_weight': 7,\n",
       " 'max_depth': 3,\n",
       " 'learning_rate': 0.25,\n",
       " 'gamma': 0.4,\n",
       " 'colsample_bytree': 0.5}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search.best_estimator_\n",
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model= xgboost.XGBRegressor(min_child_weight= 3,\n",
    " max_depth= 3,\n",
    " learning_rate=0.2,\n",
    " gamma= 0.4,\n",
    " colsample_bytree= 0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.7, gamma=0.4, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.2, max_delta_step=0, max_depth=3,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=new_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse=mean_squared_error(y_test, y_pred)**(1/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19202612980017558\n"
     ]
    }
   ],
   "source": [
    "print(rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=0.7, gamma=0.4, gpu_id=-1,\n",
       "             importance_type='gain', interaction_constraints='',\n",
       "             learning_rate=0.2, max_delta_step=0, max_depth=3,\n",
       "             min_child_weight=3, missing=nan, monotone_constraints='()',\n",
       "             n_estimators=100, n_jobs=0, num_parallel_tree=1, random_state=0,\n",
       "             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=new_model.predict(df_test[feat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Success\n"
     ]
    }
   ],
   "source": [
    "output= pd.DataFrame({'Employee_ID': df_test.Employee_ID, 'Attrition_rate': predictions})\n",
    "output.to_csv('MySubmission.csv', index=False)\n",
    "print('Success')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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