IBM_ATTRITION_SummerAnalytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summer Analytics 2020 Capstone Project\n",
    "## Predicting Employee Attrition in the Dawn of Recession\n",
    "### Debojjal Bagchi\n",
    "### https://github.com/debojjalb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import scipy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading the Train Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>...</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>571</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1614</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>842</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>689</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age  Attrition BusinessTravel              Department  \\\n",
       "0   1   30          0     Non-Travel  Research & Development   \n",
       "1   2   36          0  Travel_Rarely  Research & Development   \n",
       "2   3   55          1  Travel_Rarely                   Sales   \n",
       "3   4   39          0  Travel_Rarely  Research & Development   \n",
       "4   5   37          0  Travel_Rarely  Research & Development   \n",
       "\n",
       "   DistanceFromHome  Education EducationField  EmployeeNumber  \\\n",
       "0                 2          3        Medical             571   \n",
       "1                12          4  Life Sciences            1614   \n",
       "2                 2          1        Medical             842   \n",
       "3                24          1  Life Sciences            2014   \n",
       "4                 3          3          Other             689   \n",
       "\n",
       "   EnvironmentSatisfaction  ... PerformanceRating  StockOptionLevel  \\\n",
       "0                        3  ...                 3                 0   \n",
       "1                        3  ...                 3                 2   \n",
       "2                        3  ...                 3                 0   \n",
       "3                        1  ...                 3                 0   \n",
       "4                        3  ...                 3                 1   \n",
       "\n",
       "  TotalWorkingYears  TrainingTimesLastYear YearsAtCompany  YearsInCurrentRole  \\\n",
       "0                12                      2             11                   7   \n",
       "1                 7                      2              3                   2   \n",
       "2                12                      3              9                   7   \n",
       "3                18                      2              7                   7   \n",
       "4                10                      2             10                   7   \n",
       "\n",
       "   YearsSinceLastPromotion YearsWithCurrManager  CommunicationSkill  Behaviour  \n",
       "0                        6                    7                   4          1  \n",
       "1                        1                    1                   2          1  \n",
       "2                        7                    3                   5          1  \n",
       "3                        1                    7                   4          1  \n",
       "4                        7                    8                   1          1  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train.shape\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ID & Employee number are not required for training purposes. Hence Dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train.drop(['Id','EmployeeNumber'], axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Introducing New Features:\n",
    "\n",
    "- **Tenure per job**: Usually, people who have worked with many companies but for small periods at every organization tend to leave early as they always need a change of Organization to keep them going.\n",
    "\n",
    "- **Years without Change**: For any person, a change either in role or job level or responsibility is needed to keep the work exciting to continue. We create a variable to see how many years it has been for an employee without any sort of change using Promotion, Role and Job Change as a metric to cover different variants of change.\n",
    "\n",
    "- **Compa Ratio**: Compa Ratio is the ratio of the actual pay of an Employee to the midpoint of a salary range. The salary range can be that of his/her department or organization or role. The benchmark numbers can be a organization’s pay or Industry averag.\n",
    "\n",
    "- **Time_in_each_comp**: Age divided by the number of companies worked.\n",
    "\n",
    "- **JobSatisf_mean**: The Ratio of Job Satisfaction & Job Involvement.\n",
    "\n",
    "- **Income_Distance**: The Ratio of Monthly Income & Distance from home.\n",
    "\n",
    "- **Stability**: Ratio of Years in current role to years in a compamy.\n",
    "\n",
    "- **Income_YearsComp**: Ratio of Monthly Income to that of number of years in the company.\n",
    "\n",
    "- **Fidelity**: Ratio of number of companies worked to total working years.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TenurePerjob\n",
    "X_train['tenure']  = train['TotalWorkingYears'] / (train['NumCompaniesWorked'] + 1)\n",
    "\n",
    "#YearWithoutChange\n",
    "X_train['No_Change_1'] = train['YearsInCurrentRole'] - train['YearsSinceLastPromotion']\n",
    "\n",
    "#YearWithoutChange\n",
    "X_train['No_Change_2'] = train['TotalWorkingYears'] - train ['YearsSinceLastPromotion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Department\n",
      "Human Resources           2742\n",
      "Research & Development    3760\n",
      "Sales                     5405\n",
      "Name: MonthlyIncome, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4304.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compa_Ratio\n",
    "\n",
    "print(X_train.groupby('Department')['MonthlyIncome'].median())\n",
    "X_train['MonthlyIncome'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Med_HR = 2742\n",
    "Med_Sales = 5405\n",
    "Med_RnD = 3760\n",
    "Med_whole = 4304"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 30)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "buckets = [0] * 1628\n",
    "\n",
    "X_train['CR'] = buckets\n",
    "\n",
    "X_train['CR_all'] = buckets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
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       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "      <th>tenure</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>No_Change_2</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
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       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
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       "      <td>0.681915</td>\n",
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       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>6</td>\n",
       "      <td>1.240160</td>\n",
       "      <td>1.083411</td>\n",
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       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
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       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0.954672</td>\n",
       "      <td>1.198885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.25</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>1.092553</td>\n",
       "      <td>0.954461</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.509043</td>\n",
       "      <td>2.191914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   30          0     Non-Travel  Research & Development                 2   \n",
       "1   36          0  Travel_Rarely  Research & Development                12   \n",
       "2   55          1  Travel_Rarely                   Sales                 2   \n",
       "3   39          0  Travel_Rarely  Research & Development                24   \n",
       "4   37          0  Travel_Rarely  Research & Development                 3   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  \\\n",
       "0          3        Medical                        3  Female               3   \n",
       "1          4  Life Sciences                        3  Female               3   \n",
       "2          1        Medical                        3    Male               3   \n",
       "3          1  Life Sciences                        1    Male               3   \n",
       "4          3          Other                        3    Male               3   \n",
       "\n",
       "   ... YearsInCurrentRole  YearsSinceLastPromotion YearsWithCurrManager  \\\n",
       "0  ...                  7                        6                    7   \n",
       "1  ...                  2                        1                    1   \n",
       "2  ...                  7                        7                    3   \n",
       "3  ...                  7                        1                    7   \n",
       "4  ...                  7                        7                    8   \n",
       "\n",
       "   CommunicationSkill  Behaviour tenure  No_Change_1  No_Change_2        CR  \\\n",
       "0                   4          1  12.00            1            6  0.681915   \n",
       "1                   2          1   0.70            1            6  1.240160   \n",
       "2                   5          1   2.40            0            5  0.954672   \n",
       "3                   4          1   2.25            6           17  1.092553   \n",
       "4                   1          1   5.00            0            3  2.509043   \n",
       "\n",
       "     CR_all  \n",
       "0  0.595725  \n",
       "1  1.083411  \n",
       "2  1.198885  \n",
       "3  0.954461  \n",
       "4  2.191914  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Logic :\n",
    "#if (X_train['Department'] == 'Sales' ) :\n",
    "#    X_train['CR_dept']  = TrainSales['MonthlyIncome']/Med_Sales\n",
    "    \n",
    "X_train.loc[X_train['Department'] == 'Sales', 'CR'] = X_train['MonthlyIncome']/Med_Sales\n",
    "X_train.loc[X_train['Department'] == 'Research & Development', 'CR'] = X_train['MonthlyIncome']/Med_RnD\n",
    "X_train.loc[X_train['Department'] == 'Human Resources', 'CR'] = X_train['MonthlyIncome']/Med_HR\n",
    "X_train['CR_all']= X_train['MonthlyIncome']/Med_whole\n",
    "\n",
    "\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['Time_in_each_comp'] = (X_train['Age'] - 20) / ((X_train)['NumCompaniesWorked'] + 1)\n",
    "X_train['JobSatisf_mean'] = (X_train['JobSatisfaction'] + X_train['JobInvolvement']) / 2\n",
    "X_train['Income_Distance'] = X_train['MonthlyIncome'] / X_train['DistanceFromHome']\n",
    "X_train['Stability'] = X_train['YearsInCurrentRole'] / X_train['YearsAtCompany']\n",
    "X_train['Stability'].fillna((X_train['Stability'].mean()), inplace=True)\n",
    "X_train['Income_YearsComp'] = X_train['MonthlyIncome'] / X_train['YearsAtCompany']\n",
    "X_train['Income_YearsComp'] = X_train['Income_YearsComp'].replace(np.Inf, 0)\n",
    "X_train['Fidelity'] = (X_train['NumCompaniesWorked']) / X_train['TotalWorkingYears']\n",
    "X_train['Fidelity'] = X_train['Fidelity'].replace(np.Inf, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                          int64\n",
       "Attrition                    int64\n",
       "BusinessTravel              object\n",
       "Department                  object\n",
       "DistanceFromHome             int64\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "JobInvolvement               int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "NumCompaniesWorked           int64\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "PerformanceRating            int64\n",
       "StockOptionLevel             int64\n",
       "TotalWorkingYears            int64\n",
       "TrainingTimesLastYear        int64\n",
       "YearsAtCompany               int64\n",
       "YearsInCurrentRole           int64\n",
       "YearsSinceLastPromotion      int64\n",
       "YearsWithCurrManager         int64\n",
       "CommunicationSkill           int64\n",
       "Behaviour                    int64\n",
       "tenure                     float64\n",
       "No_Change_1                  int64\n",
       "No_Change_2                  int64\n",
       "CR                         float64\n",
       "CR_all                     float64\n",
       "Time_in_each_comp          float64\n",
       "JobSatisf_mean             float64\n",
       "Income_Distance            float64\n",
       "Stability                  float64\n",
       "Income_YearsComp           float64\n",
       "Fidelity                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 BINNING COLUMNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "binners = ['Age', 'DistanceFromHome' , 'CR' , 'CR_all' ,'YearsWithCurrManager' , 'No_Change_1' , 'TotalWorkingYears' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is just for visualistion!\n",
    "test_data = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Department\n",
      "Human Resources           4280.5\n",
      "Research & Development    4402.5\n",
      "Sales                     5740.0\n",
      "Name: MonthlyIncome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5038.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compa_Ratio\n",
    "\n",
    "print(test_data.groupby('Department')['MonthlyIncome'].median())\n",
    "test_data['MonthlyIncome'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.010638</td>\n",
       "      <td>9.293617</td>\n",
       "      <td>1.389059</td>\n",
       "      <td>1.307041</td>\n",
       "      <td>4.261702</td>\n",
       "      <td>1.974468</td>\n",
       "      <td>11.387234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.166425</td>\n",
       "      <td>8.084506</td>\n",
       "      <td>1.042545</td>\n",
       "      <td>0.944519</td>\n",
       "      <td>3.501693</td>\n",
       "      <td>3.221627</td>\n",
       "      <td>7.817961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.190070</td>\n",
       "      <td>0.216554</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.668404</td>\n",
       "      <td>0.588478</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.566202</td>\n",
       "      <td>1.659835</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>4.606238</td>\n",
       "      <td>3.958515</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age  DistanceFromHome          CR      CR_all  \\\n",
       "count  470.000000        470.000000  470.000000  470.000000   \n",
       "mean    37.010638          9.293617    1.389059    1.307041   \n",
       "std      9.166425          8.084506    1.042545    0.944519   \n",
       "min     18.000000          1.000000    0.190070    0.216554   \n",
       "25%     31.000000          2.000000    0.668404    0.588478   \n",
       "50%     35.000000          7.000000    1.000000    1.000000   \n",
       "75%     43.000000         15.000000    1.566202    1.659835   \n",
       "max     60.000000         29.000000    4.606238    3.958515   \n",
       "\n",
       "       YearsWithCurrManager  No_Change_1  TotalWorkingYears  \n",
       "count            470.000000   470.000000         470.000000  \n",
       "mean               4.261702     1.974468          11.387234  \n",
       "std                3.501693     3.221627           7.817961  \n",
       "min                0.000000    -9.000000           0.000000  \n",
       "25%                2.000000     0.000000           6.000000  \n",
       "50%                3.000000     1.500000          10.000000  \n",
       "75%                7.000000     4.000000          15.000000  \n",
       "max               17.000000    15.000000          40.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#TenurePerjob\n",
    "test_data['tenure'] = test_data['TotalWorkingYears'] / (test_data['NumCompaniesWorked'] + 1)\n",
    "\n",
    "#YearWithoutChange\n",
    "test_data['No_Change_1'] = test_data['YearsInCurrentRole'] - test_data['YearsSinceLastPromotion']\n",
    "\n",
    "#YearWithoutChange\n",
    "test_data['No_Change_2'] = test_data['TotalWorkingYears'] - test_data['YearsSinceLastPromotion']\n",
    "\n",
    "#CR\n",
    "Med_HR = 4280.5\n",
    "Med_RnD = 4402.5\n",
    "Med_Sales = 5740.0\n",
    "Med_whole = 5038.0\n",
    "test_data.loc[test_data['Department'] == 'Sales', 'CR'] = test_data['MonthlyIncome']/Med_Sales\n",
    "test_data.loc[test_data['Department'] == 'Research & Development', 'CR'] = test_data['MonthlyIncome']/Med_RnD\n",
    "test_data.loc[test_data['Department'] == 'Human Resources', 'CR'] = test_data['MonthlyIncome']/Med_HR\n",
    "test_data['CR_all']= test_data['MonthlyIncome']/Med_whole\n",
    "\n",
    "test_data['Time_in_each_comp'] = (test_data['Age'] - 20) / ((test_data)['NumCompaniesWorked'] + 1)\n",
    "test_data['JobSatisf_mean'] = (test_data['JobSatisfaction'] + test_data['JobInvolvement']) / 2\n",
    "test_data['Income_Distance'] = test_data['MonthlyIncome'] / test_data['DistanceFromHome']\n",
    "test_data['Stability'] = test_data['YearsInCurrentRole'] / test_data['YearsAtCompany']\n",
    "test_data['Stability'].fillna((test_data['Stability'].mean()), inplace=True)\n",
    "test_data['Income_YearsComp'] = test_data['MonthlyIncome'] / test_data['YearsAtCompany']\n",
    "test_data['Income_YearsComp'] = test_data['Income_YearsComp'].replace(np.Inf, 0)\n",
    "test_data['Fidelity'] = (test_data['NumCompaniesWorked']) / test_data['TotalWorkingYears']\n",
    "test_data['Fidelity'] = test_data['Fidelity'].replace(np.Inf, 0)\n",
    "\n",
    "\n",
    "test_data[binners].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.645577</td>\n",
       "      <td>9.735258</td>\n",
       "      <td>1.383345</td>\n",
       "      <td>1.344220</td>\n",
       "      <td>3.551597</td>\n",
       "      <td>1.565725</td>\n",
       "      <td>9.949017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.481794</td>\n",
       "      <td>8.306546</td>\n",
       "      <td>1.104400</td>\n",
       "      <td>1.008200</td>\n",
       "      <td>3.494369</td>\n",
       "      <td>3.088728</td>\n",
       "      <td>7.482936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.194635</td>\n",
       "      <td>0.234433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.668883</td>\n",
       "      <td>0.609898</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.677983</td>\n",
       "      <td>1.655263</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>7.169220</td>\n",
       "      <td>4.646608</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age  DistanceFromHome           CR       CR_all  \\\n",
       "count  1628.000000       1628.000000  1628.000000  1628.000000   \n",
       "mean     35.645577          9.735258     1.383345     1.344220   \n",
       "std       9.481794          8.306546     1.104400     1.008200   \n",
       "min      18.000000          1.000000     0.194635     0.234433   \n",
       "25%      29.000000          2.000000     0.668883     0.609898   \n",
       "50%      34.000000          8.000000     1.000000     1.000000   \n",
       "75%      42.000000         15.000000     1.677983     1.655263   \n",
       "max      60.000000         29.000000     7.169220     4.646608   \n",
       "\n",
       "       YearsWithCurrManager  No_Change_1  TotalWorkingYears  \n",
       "count           1628.000000  1628.000000        1628.000000  \n",
       "mean               3.551597     1.565725           9.949017  \n",
       "std                3.494369     3.088728           7.482936  \n",
       "min                0.000000   -10.000000           0.000000  \n",
       "25%                0.000000     0.000000           5.000000  \n",
       "50%                2.000000     1.000000           8.000000  \n",
       "75%                7.000000     3.000000          13.000000  \n",
       "max               17.000000    16.000000          38.000000  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[binners].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BINNING\n",
    "\n",
    "- AgeGroup = 15 to 60 years with groups of 5 Years\n",
    "- DistanceGroup = 15 to 30 units with groups of 5 unis\n",
    "- CR  OverallGroup = CR less than 0.5, CR greater then 0.5\n",
    "- CR Dept. Group = CR less than 0.5, CR greater then 0.5\n",
    "- Years with Curr Manager Group = 0 to 20 years with groups of 5 Years\n",
    "- Change 1 Group = Less then 2.5, Greater than 2.5\n",
    "- TotalWorkingYearsGrp = 5 to 45 years with groups of 5 Years\n",
    "- Change 2 Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Attrition</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>...</th>\n",
       "      <th>Stability</th>\n",
       "      <th>Income_YearsComp</th>\n",
       "      <th>Fidelity</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>DistanceGroup</th>\n",
       "      <th>CompaOverallGroup</th>\n",
       "      <th>CRGroup</th>\n",
       "      <th>YearContGroup</th>\n",
       "      <th>No_Change_1grp</th>\n",
       "      <th>TotalWorkingYearsGrp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>233.090909</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>3</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1554.333333</td>\n",
       "      <td>1.285714</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>573.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>586.857143</td>\n",
       "      <td>0.388889</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Other</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>943.400000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Attrition BusinessTravel              Department  DistanceFromHome  \\\n",
       "0   30          0     Non-Travel  Research & Development                 2   \n",
       "1   36          0  Travel_Rarely  Research & Development                12   \n",
       "2   55          1  Travel_Rarely                   Sales                 2   \n",
       "3   39          0  Travel_Rarely  Research & Development                24   \n",
       "4   37          0  Travel_Rarely  Research & Development                 3   \n",
       "\n",
       "   Education EducationField  EnvironmentSatisfaction  Gender  JobInvolvement  \\\n",
       "0          3        Medical                        3  Female               3   \n",
       "1          4  Life Sciences                        3  Female               3   \n",
       "2          1        Medical                        3    Male               3   \n",
       "3          1  Life Sciences                        1    Male               3   \n",
       "4          3          Other                        3    Male               3   \n",
       "\n",
       "   ... Stability  Income_YearsComp  Fidelity  AgeGroup  DistanceGroup  \\\n",
       "0  ...  0.636364        233.090909  0.000000         1              0   \n",
       "1  ...  0.666667       1554.333333  1.285714         1              1   \n",
       "2  ...  0.777778        573.333333  0.333333         3              0   \n",
       "3  ...  1.000000        586.857143  0.388889         2              3   \n",
       "4  ...  0.700000        943.400000  0.100000         1              0   \n",
       "\n",
       "  CompaOverallGroup  CRGroup  YearContGroup  No_Change_1grp  \\\n",
       "0                 1        1              1               0   \n",
       "1                 1        1              0               0   \n",
       "2                 1        1              0               0   \n",
       "3                 1        1              1               1   \n",
       "4                 1        1              1               0   \n",
       "\n",
       "   TotalWorkingYearsGrp  \n",
       "0                     3  \n",
       "1                     2  \n",
       "2                     3  \n",
       "3                     4  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train['AgeGroup'] = pd.cut(X_train['Age'], bins=np.linspace(15, 60, 5), labels= False)\n",
    "X_train['DistanceGroup'] = pd.cut(X_train['DistanceFromHome'], bins=np.linspace(0, 30, 5), labels= False)\n",
    "X_train['CompaOverallGroup'] = pd.cut(X_train['CR_all'], bins= [-100, 0.5, 100], labels= False)\n",
    "X_train['CRGroup'] = pd.cut(X_train['CR'], bins= [-100, 0.5, 100], labels= False)\n",
    "X_train['YearContGroup'] = pd.cut(X_train['YearsWithCurrManager'], bins= [-1, 5, 10, 15, 20], labels= False)\n",
    "X_train['No_Change_1grp'] = pd.cut(X_train['No_Change_1'], bins= [-100, 2.5, 100], labels= False)\n",
    "X_train['TotalWorkingYearsGrp'] = pd.cut(X_train['TotalWorkingYears'], bins= [-100,0,5,10,15,20,25,30,35,40,45,50], labels= False)\n",
    "\n",
    "\n",
    "sns.heatmap(X_train.isnull(),False,False,'viridis')\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. CORRELATION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.drop (binners , axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "X_train.corr()\n",
    "corrMatrix = X_train.corr()\n",
    "sn.heatmap(corrMatrix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Find High Correlation\n",
    "\n",
    "- Between Years at Company, Years in Curr Role, Years with Curr Manager & Years Since Last Promotion - We will consider ‘Years with Curr Manager’\n",
    "\n",
    "- Between Job Level & Monthly Income - We will consider ‘Job Level’\n",
    "\n",
    "- Betweem Percent Salary Hike & Performance Ratiing - We shall consider ’Percent Salary Hike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition', 'BusinessTravel', 'Department', 'Education',\n",
       "       'EducationField', 'EnvironmentSatisfaction', 'Gender', 'JobInvolvement',\n",
       "       'JobRole', 'JobSatisfaction', 'MaritalStatus', 'MonthlyIncome',\n",
       "       'NumCompaniesWorked', 'OverTime', 'PercentSalaryHike',\n",
       "       'PerformanceRating', 'StockOptionLevel', 'TrainingTimesLastYear',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'CommunicationSkill', 'Behaviour', 'tenure', 'No_Change_2',\n",
       "       'Time_in_each_comp', 'JobSatisf_mean', 'Income_Distance', 'Stability',\n",
       "       'Income_YearsComp', 'Fidelity', 'AgeGroup', 'DistanceGroup',\n",
       "       'CompaOverallGroup', 'CRGroup', 'YearContGroup', 'No_Change_1grp',\n",
       "       'TotalWorkingYearsGrp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Drop highly related Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 34)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_copy = X_train\n",
    "X_train = X_train.drop(['YearsInCurrentRole','YearsSinceLastPromotion','YearsAtCompany' , 'PerformanceRating'], axis =1)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_corr = X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Drop Empty Columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 31)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_corr.drop(['Attrition','Behaviour', 'tenure'], axis =1)\n",
    "y_train = train_corr['Attrition']\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BusinessTravel              object\n",
       "Department                  object\n",
       "Education                    int64\n",
       "EducationField              object\n",
       "EnvironmentSatisfaction      int64\n",
       "Gender                      object\n",
       "JobInvolvement               int64\n",
       "JobRole                     object\n",
       "JobSatisfaction              int64\n",
       "MaritalStatus               object\n",
       "MonthlyIncome                int64\n",
       "NumCompaniesWorked           int64\n",
       "OverTime                    object\n",
       "PercentSalaryHike            int64\n",
       "StockOptionLevel             int64\n",
       "TrainingTimesLastYear        int64\n",
       "CommunicationSkill           int64\n",
       "No_Change_2                  int64\n",
       "Time_in_each_comp          float64\n",
       "JobSatisf_mean             float64\n",
       "Income_Distance            float64\n",
       "Stability                  float64\n",
       "Income_YearsComp           float64\n",
       "Fidelity                   float64\n",
       "AgeGroup                     int64\n",
       "DistanceGroup                int64\n",
       "CompaOverallGroup            int64\n",
       "CRGroup                      int64\n",
       "YearContGroup                int64\n",
       "No_Change_1grp               int64\n",
       "TotalWorkingYearsGrp         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Handling Skewed Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 31)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cols =X_train.columns\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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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>Education</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>No_Change_2</th>\n",
       "      <th>Time_in_each_comp</th>\n",
       "      <th>JobSatisf_mean</th>\n",
       "      <th>Income_Distance</th>\n",
       "      <th>Stability</th>\n",
       "      <th>Income_YearsComp</th>\n",
       "      <th>Fidelity</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>DistanceGroup</th>\n",
       "      <th>CompaOverallGroup</th>\n",
       "      <th>CRGroup</th>\n",
       "      <th>YearContGroup</th>\n",
       "      <th>No_Change_1grp</th>\n",
       "      <th>TotalWorkingYearsGrp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "      <td>1628.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.884521</td>\n",
       "      <td>2.646192</td>\n",
       "      <td>2.650491</td>\n",
       "      <td>2.639435</td>\n",
       "      <td>5785.524570</td>\n",
       "      <td>2.761671</td>\n",
       "      <td>15.207617</td>\n",
       "      <td>0.692260</td>\n",
       "      <td>2.752457</td>\n",
       "      <td>3.135749</td>\n",
       "      <td>7.899877</td>\n",
       "      <td>5.652035</td>\n",
       "      <td>2.644963</td>\n",
       "      <td>1649.409438</td>\n",
       "      <td>0.570940</td>\n",
       "      <td>1561.071353</td>\n",
       "      <td>0.419111</td>\n",
       "      <td>1.324939</td>\n",
       "      <td>0.877764</td>\n",
       "      <td>0.914005</td>\n",
       "      <td>0.919533</td>\n",
       "      <td>0.336609</td>\n",
       "      <td>0.285627</td>\n",
       "      <td>2.396806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.020470</td>\n",
       "      <td>1.125283</td>\n",
       "      <td>0.736330</td>\n",
       "      <td>1.096272</td>\n",
       "      <td>4339.293147</td>\n",
       "      <td>2.549995</td>\n",
       "      <td>3.686703</td>\n",
       "      <td>0.864808</td>\n",
       "      <td>1.288033</td>\n",
       "      <td>1.408770</td>\n",
       "      <td>6.874173</td>\n",
       "      <td>4.861544</td>\n",
       "      <td>0.658633</td>\n",
       "      <td>2579.441230</td>\n",
       "      <td>0.340795</td>\n",
       "      <td>2027.083746</td>\n",
       "      <td>0.465011</td>\n",
       "      <td>0.901787</td>\n",
       "      <td>1.049680</td>\n",
       "      <td>0.280443</td>\n",
       "      <td>0.272098</td>\n",
       "      <td>0.560719</td>\n",
       "      <td>0.451851</td>\n",
       "      <td>1.478314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1009.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>46.583333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2625.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>291.071429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>573.333333</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4304.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>665.750000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>978.071429</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7124.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1742.500000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>1917.968750</td>\n",
       "      <td>0.587500</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19999.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19613.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Education  EnvironmentSatisfaction  JobInvolvement  JobSatisfaction  MonthlyIncome  NumCompaniesWorked  PercentSalaryHike  StockOptionLevel  TrainingTimesLastYear  CommunicationSkill  No_Change_2  Time_in_each_comp  JobSatisf_mean  Income_Distance    Stability  Income_YearsComp     Fidelity     AgeGroup  DistanceGroup  CompaOverallGroup      CRGroup  YearContGroup  No_Change_1grp  TotalWorkingYearsGrp\n",
       "count  1628.000000              1628.000000     1628.000000      1628.000000    1628.000000         1628.000000        1628.000000       1628.000000            1628.000000         1628.000000  1628.000000        1628.000000     1628.000000      1628.000000  1628.000000       1628.000000  1628.000000  1628.000000    1628.000000        1628.000000  1628.000000    1628.000000     1628.000000           1628.000000\n",
       "mean      2.884521                 2.646192        2.650491         2.639435    5785.524570            2.761671          15.207617          0.692260               2.752457            3.135749     7.899877           5.652035        2.644963      1649.409438     0.570940       1561.071353     0.419111     1.324939       0.877764           0.914005     0.919533       0.336609        0.285627              2.396806\n",
       "std       1.020470                 1.125283        0.736330         1.096272    4339.293147            2.549995           3.686703          0.864808               1.288033            1.408770     6.874173           4.861544        0.658633      2579.441230     0.340795       2027.083746     0.465011     0.901787       1.049680           0.280443     0.272098       0.560719        0.451851              1.478314\n",
       "min       1.000000                 1.000000        1.000000         1.000000    1009.000000            0.000000          11.000000          0.000000               0.000000            1.000000     0.000000          -1.000000        1.000000        46.583333     0.000000          0.000000     0.000000     0.000000       0.000000           0.000000     0.000000       0.000000        0.000000              0.000000\n",
       "25%       2.000000                 2.000000        2.000000         2.000000    2625.000000            1.000000          12.000000          0.000000               2.000000            2.000000     3.000000           2.500000        2.000000       291.071429     0.333333        573.333333     0.100000     1.000000       0.000000           1.000000     1.000000       0.000000        0.000000              1.000000\n",
       "50%       3.000000                 3.000000        3.000000         3.000000    4304.000000            2.000000          14.000000          0.000000               3.000000            3.000000     6.000000           4.500000        2.500000       665.750000     0.666667        978.071429     0.250000     1.000000       1.000000           1.000000     1.000000       0.000000        0.000000              2.000000\n",
       "75%       4.000000                 4.000000        3.000000         4.000000    7124.250000            4.000000          18.000000          1.000000               3.000000            4.000000    10.000000           7.500000        3.000000      1742.500000     0.833333       1917.968750     0.587500     2.000000       1.000000           1.000000     1.000000       1.000000        1.000000              3.000000\n",
       "max       5.000000                 4.000000        4.000000         4.000000   19999.000000            9.000000          25.000000          3.000000               6.000000            5.000000    37.000000          40.000000        4.000000     19999.000000     1.000000      19613.000000     3.000000     3.000000       3.000000           1.000000     1.000000       3.000000        1.000000              8.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Skewness involved in\n",
    "- MonthlyIncome\n",
    "- No_Change_2\n",
    "- Time_in_each_comp\n",
    "- Income_YearsComp\n",
    "- Fidelity\n",
    "- TotalWorkingYearsGrp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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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,
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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G+0gfgSCNIsnbga8CH6mqny12P0tRVf2mqq5g8I70q4HLhg07tV0tLUneDxysqifnlocMXZB56vV/YvIjEI7fi0kurqoDSS5msAI74yU5i0Gwf7GqvtbKztVRVNUrSR5j8BzFiiTL26rUf4NwDfCBJDcC5wDvYLCSH8s89bpy9yMQjt8OYGM73gg8uIi9LAltP/ReYHdVfWbOJedqjiQTSVa047cC72Pw/MSjwM1t2Bk/T1X1iapaVVWTDDLpkar6MGOap27fodp+O36WNz4C4R8WuaUlI8mXgPcy+FjWF4FPAf8K3A9cArwAbKiqw590PaMk+RPgP4Dv88Ye6ScZ7Ls7V02SP2TwROAyBgvG+6vq75K8i8GLGc4DngL+oqp+tXidLh1J3gv8TVW9f1zz1G24S9KZrNdtGUk6oxnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUP/Bxr8Duxvnv4zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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sGO6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ/8Pp6Cbfv5jQgwAAAAASUVORK5CYII=\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": [
    "plt.hist(X_train['MonthlyIncome']);\n",
    "plt.show()\n",
    "plt.hist(X_train['No_Change_2']);\n",
    "plt.show()\n",
    "plt.hist(X_train['Time_in_each_comp']);\n",
    "plt.show()\n",
    "#plt.hist(X_train['Income_YearsComp']);\n",
    "#plt.show()\n",
    "plt.hist(X_train['Fidelity']);\n",
    "plt.show()\n",
    "plt.hist(X_train['TotalWorkingYearsGrp']);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Apply Squareroot Transform to fix skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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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1JuAM4BeBlzH4FfxAkzK+hzPJPxckuYbBkuit+5oW6Da2epO8FLgG+LOFTi/QdtS1TnO4T/zHHCR5EYNgv7WqPtWan06yup1fDewdV31DzgUuSfI4g0/xPI/BTH5lkn1vdJuk8Z0D5qrq3nZ8O4Own8SxBXgj8K2qmq+qHwKfAn6TyR1fOPRYTuzzLslm4M3A26qtazB59f4yg3/kv9aeb2uBryT5BZa41mkO94n+mIMkAW4GdlXV+4ZO7QA2t/3NDNbix6qqrq6qtVU1w2Ac766qtwH3AG9p3SaiVoCq+g/gySS/0prOB77OBI5t8wSwMclL28/FvnoncnybQ43lDuCKdmfHRuC5fcs345TBfxD0HuCSqnp+6NQO4PIkJyY5g8GLlV8aR40AVfVgVZ1WVTPt+TYHnN1+ppd2bI/3CyFL/ELFRQxeGf8mcM246zmgtt9i8CvVA8D97XERg7XsncCjbXvyuGs9oO43AHe2/VcyeCLsBv4BOHHc9Q3VeSYw28b3H4GTJnlsgfcC3wAeAj4KnDgp4wt8jMFrAT9sYbPlUGPJYOngg+059yCDO4Amod7dDNar9z3X/mao/zWt3keAC8dd6wHnH+cnL6gu6dj68QOS1KFpXpaRJB2C4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI69P9RnD9KPXy/HAAAAABJRU5ErkJggg==\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": [
    "plt.hist(np.sqrt(X_train['MonthlyIncome']));\n",
    "plt.show()\n",
    "plt.hist(np.sqrt(X_train['No_Change_2']));\n",
    "plt.show()\n",
    "#plt.hist(np.sqrt(X_train['Time_in_each_comp']));\n",
    "#plt.show()\n",
    "plt.hist(np.sqrt(X_train['Income_YearsComp']));\n",
    "plt.show()\n",
    "plt.hist(np.sqrt(X_train['Fidelity']));\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train['MonthlyIncome'] = (np.sqrt(X_train['MonthlyIncome']))\n",
    "X_train['No_Change_2'] = (np.sqrt(X_train['No_Change_2']))\n",
    "#X_train['Time_in_each_comp'] = (np.sqrt(X_train['Time_in_each_comp']))\n",
    "X_train['Income_YearsComp'] = (np.sqrt(X_train['Income_YearsComp']))\n",
    "X_train['Fidelity'] = (np.sqrt(X_train['Fidelity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Handling Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 One Hot Encoding Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "onehotencoder = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.compose import make_column_transformer\n",
    "columns_trans= make_column_transformer((onehotencoder, ['BusinessTravel','Department' ,'EducationField','Gender', 'JobRole', 'MaritalStatus', 'OverTime']), remainder = 'passthrough' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1628, 52)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_pp = columns_trans.fit_transform(X_train)\n",
    "X_train_pp.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Loading the Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(470, 28)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Apply Same Preprocessing steps as in Train Data to prevent Data Leakage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TenurePerjob\n",
    "test['tenure'] = test['TotalWorkingYears'] / (test['NumCompaniesWorked'] + 1)\n",
    "\n",
    "#YearWithoutChange\n",
    "test['No_Change_1'] = test['YearsInCurrentRole'] - test['YearsSinceLastPromotion']\n",
    "\n",
    "#YearWithoutChange\n",
    "test['No_Change_2'] = test['TotalWorkingYears'] - test['YearsSinceLastPromotion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Department\n",
      "Human Resources           4280.5\n",
      "Research & Development    4402.5\n",
      "Sales                     5740.0\n",
      "Name: MonthlyIncome, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5038.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compa_Ratio\n",
    "\n",
    "print(test.groupby('Department')['MonthlyIncome'].median())\n",
    "test['MonthlyIncome'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Med_HR = 4280.5\n",
    "Med_RnD = 4402.5\n",
    "Med_Sales = 5740.0\n",
    "Med_whole = 5038.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "buckets = [0] * 470\n",
    "test['CR'] = buckets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "      <th>tenure</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>No_Change_2</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>466</td>\n",
       "      <td>32</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1692</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Single</td>\n",
       "      <td>1393</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.316411</td>\n",
       "      <td>0.276499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>467</td>\n",
       "      <td>18</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>Sales</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1624</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>4</td>\n",
       "      <td>Single</td>\n",
       "      <td>1569</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.273345</td>\n",
       "      <td>0.311433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>468</td>\n",
       "      <td>24</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>639</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>2725</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0.618966</td>\n",
       "      <td>0.540889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>367</td>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>2</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>5582</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>21</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>3</td>\n",
       "      <td>1.267916</td>\n",
       "      <td>1.107979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>470</td>\n",
       "      <td>36</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1651</td>\n",
       "      <td>2</td>\n",
       "      <td>Female</td>\n",
       "      <td>3</td>\n",
       "      <td>Healthcare Representative</td>\n",
       "      <td>1</td>\n",
       "      <td>Married</td>\n",
       "      <td>8008</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1.818966</td>\n",
       "      <td>1.589520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  Age     BusinessTravel              Department  DistanceFromHome  Education EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  JobInvolvement                    JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  NumCompaniesWorked OverTime  PercentSalaryHike  PerformanceRating  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  Behaviour  tenure  No_Change_1  No_Change_2        CR    CR_all\n",
       "465  466   32      Travel_Rarely  Research & Development                 2          4  Life Sciences            1692                        4    Male               3      Laboratory Technician                2        Single           1393                   1       No                 12                  3                 0                  1                      2               1                   0                        0                     0                   5          1     0.5            0            1  0.316411  0.276499\n",
       "466  467   18  Travel_Frequently                   Sales                 3          2        Medical            1624                        2  Female               3       Sales Representative                4        Single           1569                   1      Yes                 12                  3                 0                  0                      2               0                   0                        0                     0                   2          1     0.0            0            0  0.273345  0.311433\n",
       "467  468   24      Travel_Rarely  Research & Development                23          3        Medical             639                        2    Male               4         Research Scientist                4       Married           2725                   1      Yes                 11                  3                 2                  6                      3               6                   5                        1                     4                   1          1     3.0            4            5  0.618966  0.540889\n",
       "468  469   31      Travel_Rarely  Research & Development                23          3        Medical             367                        2    Male               2  Healthcare Representative                4       Married           5582                   0       No                 21                  4                 1                 10                      2               9                   0                        7                     8                   3          1    10.0           -7            3  1.267916  1.107979\n",
       "469  470   36      Travel_Rarely  Research & Development                 5          4  Life Sciences            1651                        2  Female               3  Healthcare Representative                1       Married           8008                   4       No                 12                  3                 2                  9                      6               3                   2                        0                     2                   5          1     1.8            2            9  1.818966  1.589520"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.loc[test['Department'] == 'Sales', 'CR'] = test['MonthlyIncome']/Med_Sales\n",
    "test.loc[test['Department'] == 'Research & Development', 'CR'] = test['MonthlyIncome']/Med_RnD\n",
    "test.loc[test['Department'] == 'Human Resources', 'CR'] = test['MonthlyIncome']/Med_HR\n",
    "test['CR_all']= test['MonthlyIncome']/Med_whole\n",
    "\n",
    "\n",
    "test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "      <th>tenure</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>No_Change_2</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>DistanceGroup</th>\n",
       "      <th>CompaOverallGroup</th>\n",
       "      <th>CRGroup</th>\n",
       "      <th>YearContGroup</th>\n",
       "      <th>No_Change_1grp</th>\n",
       "      <th>TotalWorkingYearsGrp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>377</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>2070</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.470187</td>\n",
       "      <td>0.410877</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>653</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>5460</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>1.083763</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>474</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>5974</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1.356956</td>\n",
       "      <td>1.185788</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>827</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>6062</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>1.376945</td>\n",
       "      <td>1.203255</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>972</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>4447</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1.010108</td>\n",
       "      <td>0.882692</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age BusinessTravel              Department  DistanceFromHome  Education EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  JobInvolvement                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  NumCompaniesWorked OverTime  PercentSalaryHike  PerformanceRating  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  Behaviour  tenure  No_Change_1  No_Change_2        CR    CR_all  AgeGroup  DistanceGroup  CompaOverallGroup  CRGroup  YearContGroup  No_Change_1grp  TotalWorkingYearsGrp\n",
       "0   1   28  Travel_Rarely  Research & Development                 9          3        Medical             377                        4    Male               3      Research Scientist                4       Married           2070                   1       No                 23                  4                 1                  5                      3               5                   2                        0                     4                   5          1     2.5            2            5  0.470187  0.410877         1              1                  0        0              0               0                     1\n",
       "1   2   31  Travel_Rarely                   Sales                 6          4        Medical             653                        1    Male               4         Sales Executive                4      Divorced           5460                   4       No                 22                  4                 2                 13                      4               7                   7                        5                     7                   3          1     2.6            2            8  0.951220  1.083763         1              0                  1        1              1               0                     3\n",
       "2   3   37  Travel_Rarely  Research & Development                 6          3        Medical             474                        3    Male               4      Research Scientist                1      Divorced           5974                   4      Yes                 13                  3                 2                 13                      2               7                   7                        6                     7                   4          1     2.6            1            7  1.356956  1.185788         1              0                  1        1              1               0                     3\n",
       "3   4   42  Travel_Rarely  Research & Development                 1          2  Life Sciences             827                        4  Female               2  Manufacturing Director                4       Married           6062                   9      Yes                 13                  3                 1                  8                      4               4                   3                        0                     2                   5          1     0.8            3            8  1.376945  1.203255         2              0                  1        1              0               1                     2\n",
       "4   5   45     Non-Travel  Research & Development                 4          2  Life Sciences             972                        3    Male               3   Laboratory Technician                2       Married           4447                   1       No                 12                  3                 0                  9                      5               9                   7                        0                     8                   2          1     4.5            7            9  1.010108  0.882692         2              0                  1        1              1               1                     2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test['AgeGroup'] = pd.cut(test['Age'], bins=np.linspace(15, 60, 5), labels= False)\n",
    "test['DistanceGroup'] = pd.cut(test['DistanceFromHome'], bins=np.linspace(0, 30, 5), labels= False)\n",
    "test['CompaOverallGroup'] = pd.cut(test['CR_all'], bins= [-100, 0.5, 100], labels= False)\n",
    "test['CRGroup'] = pd.cut(test['CR'], bins= [-100, 0.5, 100], labels= False)\n",
    "test['YearContGroup'] = pd.cut(test['YearsWithCurrManager'], bins= [-1, 5, 10, 15, 20], labels= False)\n",
    "test['No_Change_1grp'] = pd.cut(test['No_Change_1'], bins= [-100, 2.5, 100], labels= False)\n",
    "test['TotalWorkingYearsGrp'] = pd.cut(test['TotalWorkingYears'], bins= [-100,0,5,10,15,20,25,30,35,40,45,50], labels= False)\n",
    "\n",
    "\n",
    "sns.heatmap(test.isnull(),False,False,'viridis')\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Time_in_each_comp'] = (test['Age'] - 20) / ((test)['NumCompaniesWorked'] + 1)\n",
    "test['JobSatisf_mean'] = (test['JobSatisfaction'] + test['JobInvolvement']) / 2\n",
    "test['Income_Distance'] = test['MonthlyIncome'] / test['DistanceFromHome']\n",
    "test['Stability'] = test['YearsInCurrentRole'] / test['YearsAtCompany']\n",
    "test['Stability'].fillna((test['Stability'].mean()), inplace=True)\n",
    "test['Income_YearsComp'] = test['MonthlyIncome'] / test['YearsAtCompany']\n",
    "test['Income_YearsComp'] = test['Income_YearsComp'].replace(np.Inf, 0)\n",
    "test['Fidelity'] = (test['NumCompaniesWorked']) / test['TotalWorkingYears']\n",
    "test['Fidelity'] = test['Fidelity'].replace(np.Inf, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['MonthlyIncome'] = np.sqrt(test['MonthlyIncome'])\n",
    "test['No_Change_2'] = np.sqrt(test['No_Change_2'])\n",
    "#test['Time_in_each_comp'] = np.sqrt(test['Time_in_each_comp'])\n",
    "test['Income_YearsComp'] = np.sqrt(test['Income_YearsComp'])\n",
    "test['Fidelity'] = np.sqrt(test['Fidelity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .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>Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>Department</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EducationField</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobRole</th>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <th>MaritalStatus</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <th>OverTime</th>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <th>PerformanceRating</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <th>CommunicationSkill</th>\n",
       "      <th>Behaviour</th>\n",
       "      <th>tenure</th>\n",
       "      <th>No_Change_1</th>\n",
       "      <th>No_Change_2</th>\n",
       "      <th>CR</th>\n",
       "      <th>CR_all</th>\n",
       "      <th>AgeGroup</th>\n",
       "      <th>DistanceGroup</th>\n",
       "      <th>CompaOverallGroup</th>\n",
       "      <th>CRGroup</th>\n",
       "      <th>YearContGroup</th>\n",
       "      <th>No_Change_1grp</th>\n",
       "      <th>TotalWorkingYearsGrp</th>\n",
       "      <th>Time_in_each_comp</th>\n",
       "      <th>JobSatisf_mean</th>\n",
       "      <th>Income_Distance</th>\n",
       "      <th>Stability</th>\n",
       "      <th>Income_YearsComp</th>\n",
       "      <th>Fidelity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>377</td>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>45.497253</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2</td>\n",
       "      <td>2.236068</td>\n",
       "      <td>0.470187</td>\n",
       "      <td>0.410877</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>230.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>20.346990</td>\n",
       "      <td>0.447214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Sales</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>Medical</td>\n",
       "      <td>653</td>\n",
       "      <td>1</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>Sales Executive</td>\n",
       "      <td>4</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>73.891813</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>0.951220</td>\n",
       "      <td>1.083763</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>910.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.928480</td>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>474</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>4</td>\n",
       "      <td>Research Scientist</td>\n",
       "      <td>1</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>77.291655</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>1.356956</td>\n",
       "      <td>1.185788</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>995.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.213500</td>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>827</td>\n",
       "      <td>4</td>\n",
       "      <td>Female</td>\n",
       "      <td>2</td>\n",
       "      <td>Manufacturing Director</td>\n",
       "      <td>4</td>\n",
       "      <td>Married</td>\n",
       "      <td>77.858847</td>\n",
       "      <td>9</td>\n",
       "      <td>Yes</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>1.376945</td>\n",
       "      <td>1.203255</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6062.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>38.929423</td>\n",
       "      <td>1.060660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>972</td>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>3</td>\n",
       "      <td>Laboratory Technician</td>\n",
       "      <td>2</td>\n",
       "      <td>Married</td>\n",
       "      <td>66.685831</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5</td>\n",
       "      <td>7</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.010108</td>\n",
       "      <td>0.882692</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>12.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1111.750000</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>22.228610</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Age BusinessTravel              Department  DistanceFromHome  Education EducationField  EmployeeNumber  EnvironmentSatisfaction  Gender  JobInvolvement                 JobRole  JobSatisfaction MaritalStatus  MonthlyIncome  NumCompaniesWorked OverTime  PercentSalaryHike  PerformanceRating  StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  YearsAtCompany  YearsInCurrentRole  YearsSinceLastPromotion  YearsWithCurrManager  CommunicationSkill  Behaviour  tenure  No_Change_1  No_Change_2        CR    CR_all  AgeGroup  DistanceGroup  CompaOverallGroup  CRGroup  YearContGroup  No_Change_1grp  TotalWorkingYearsGrp  Time_in_each_comp  JobSatisf_mean  Income_Distance  Stability  Income_YearsComp  Fidelity\n",
       "0   1   28  Travel_Rarely  Research & Development                 9          3        Medical             377                        4    Male               3      Research Scientist                4       Married      45.497253                   1       No                 23                  4                 1                  5                      3               5                   2                        0                     4                   5          1     2.5            2     2.236068  0.470187  0.410877         1              1                  0        0              0               0                     1                4.0             3.5       230.000000   0.400000         20.346990  0.447214\n",
       "1   2   31  Travel_Rarely                   Sales                 6          4        Medical             653                        1    Male               4         Sales Executive                4      Divorced      73.891813                   4       No                 22                  4                 2                 13                      4               7                   7                        5                     7                   3          1     2.6            2     2.828427  0.951220  1.083763         1              0                  1        1              1               0                     3                2.2             4.0       910.000000   1.000000         27.928480  0.554700\n",
       "2   3   37  Travel_Rarely  Research & Development                 6          3        Medical             474                        3    Male               4      Research Scientist                1      Divorced      77.291655                   4      Yes                 13                  3                 2                 13                      2               7                   7                        6                     7                   4          1     2.6            1     2.645751  1.356956  1.185788         1              0                  1        1              1               0                     3                3.4             2.5       995.666667   1.000000         29.213500  0.554700\n",
       "3   4   42  Travel_Rarely  Research & Development                 1          2  Life Sciences             827                        4  Female               2  Manufacturing Director                4       Married      77.858847                   9      Yes                 13                  3                 1                  8                      4               4                   3                        0                     2                   5          1     0.8            3     2.828427  1.376945  1.203255         2              0                  1        1              0               1                     2                2.2             3.0      6062.000000   0.750000         38.929423  1.060660\n",
       "4   5   45     Non-Travel  Research & Development                 4          2  Life Sciences             972                        3    Male               3   Laboratory Technician                2       Married      66.685831                   1       No                 12                  3                 0                  9                      5               9                   7                        0                     8                   2          1     4.5            7     3.000000  1.010108  0.882692         2              0                  1        1              1               1                     2               12.5             2.5      1111.750000   0.777778         22.228610  0.333333"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(470, 31)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = test[X_train_cols]\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_pp = columns_trans.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Do a final Check on the shape of Train & Test Data. As this is the end of preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1628, 52)\n",
      "(1628,)\n",
      "(470, 52)\n"
     ]
    }
   ],
   "source": [
    "print(X_train_pp.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test_pp.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Import The XGB Classifer from Sklearn Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "from datetime import datetime\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Use Grid Search to find the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 800 candidates, totalling 4000 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  24 tasks      | elapsed:   32.6s\n",
      "[Parallel(n_jobs=-1)]: Done 120 tasks      | elapsed:  2.2min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed:  4.6min\n",
      "[Parallel(n_jobs=-1)]: Done 504 tasks      | elapsed:  8.3min\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed: 11.7min\n",
      "[Parallel(n_jobs=-1)]: Done 1144 tasks      | elapsed: 16.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1560 tasks      | elapsed: 21.8min\n",
      "[Parallel(n_jobs=-1)]: Done 2040 tasks      | elapsed: 27.9min\n",
      "[Parallel(n_jobs=-1)]: Done 2584 tasks      | elapsed: 35.0min\n",
      "[Parallel(n_jobs=-1)]: Done 3192 tasks      | elapsed: 43.2min\n",
      "[Parallel(n_jobs=-1)]: Done 3864 tasks      | elapsed: 52.4min\n",
      "[Parallel(n_jobs=-1)]: Done 4000 out of 4000 | elapsed: 54.4min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Time taken: 0 hours 54 minutes and 25.23 seconds.\n"
     ]
    }
   ],
   "source": [
    "def timer(start_time=None):\n",
    "    if not start_time:\n",
    "        start_time = datetime.now()\n",
    "        return start_time\n",
    "    elif start_time:\n",
    "        thour, temp_sec = divmod((datetime.now() - start_time).total_seconds(), 3600)\n",
    "        tmin, tsec = divmod(temp_sec, 60)\n",
    "        print('\\n Time taken: %i hours %i minutes and %s seconds.' % (thour, tmin, round(tsec, 2)))\n",
    "        \n",
    "        \n",
    "xgb_cfl = xgb.XGBClassifier(n_jobs = -1)\n",
    "\n",
    "\n",
    "# A parameter grid for XGBoost\n",
    "params = {\n",
    "        'n_estimators' : [100, 200, 500, 750],\n",
    "        'learning_rate' : [0.01, 0.02, 0.05, 0.1, 0.25],\n",
    "        'min_child_weight': [1, 5, 7, 10],\n",
    "        'gamma': [0.001, 0.01, 0.1, 0.5, 1, 1.5, 5],\n",
    "        'subsample': [0.6, 0.8, 1.0],\n",
    "        'colsample_bytree': [0.6, 0.8, 1.0],\n",
    "        'max_depth': [3, 4, 5, 10, 12]\n",
    "        }\n",
    "\n",
    "folds = 5\n",
    "param_comb = 800\n",
    "\n",
    "random_search = RandomizedSearchCV(xgb_cfl, param_distributions=params, n_iter=param_comb, scoring='roc_auc', n_jobs=-1, cv=5, verbose=3, random_state=42)\n",
    "\n",
    "# Here we go\n",
    "start_time = timer(None) # timing starts from this point for \"start_time\" variable\n",
    "random_search.fit(X_train_pp, y_train)\n",
    "timer(start_time) # timing ends here for \"start_time\" variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " All results:\n",
      "\n",
      " Best estimator:\n",
      "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "              colsample_bynode=1, colsample_bytree=0.6, gamma=0.001,\n",
      "              learning_rate=0.25, max_delta_step=0, max_depth=10,\n",
      "              min_child_weight=1, missing=None, n_estimators=200, n_jobs=-1,\n",
      "              nthread=None, objective='binary:logistic', random_state=0,\n",
      "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
      "              silent=None, subsample=0.8, verbosity=1)\n",
      "\n",
      " Best accuracy for 5-fold search with 800 parameter combinations:\n",
      "0.9994636406934299\n",
      "\n",
      " Best hyperparameters:\n",
      "{'subsample': 0.8, 'n_estimators': 200, 'min_child_weight': 1, 'max_depth': 10, 'learning_rate': 0.25, 'gamma': 0.001, 'colsample_bytree': 0.6}\n"
     ]
    }
   ],
   "source": [
    "print('\\n All results:')\n",
    "#print(random_search.cv_results_)\n",
    "print('\\n Best estimator:')\n",
    "print(random_search.best_estimator_)\n",
    "print('\\n Best accuracy for %d-fold search with %d parameter combinations:' % (folds, param_comb))\n",
    "print(random_search.best_score_ )\n",
    "print('\\n Best hyperparameters:')\n",
    "print(random_search.best_params_)\n",
    "results = pd.DataFrame(random_search.cv_results_)\n",
    "results.to_csv('xgb-random-grid-search-results-02.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Fit the model with the XGB Classifier with Best Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1.0, gamma=0.01,\n",
       "              interaction_constraints='', learning_rate=0.02, max_delta_step=0,\n",
       "              max_depth=12, min_child_weight=1, missing=None, n_estimators=750,\n",
       "              n_jobs=-1, nthread=None, num_parallel_tree=1,\n",
       "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
       "              reg_lambda=1, scale_pos_weight=1, seed=None, silent=None,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=1)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xgb \n",
    "xgb_clf = xgb.XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
    "              colsample_bynode=1, colsample_bytree=1.0, gamma=0.01,\n",
    "              importance_type='gain', interaction_constraints='',\n",
    "              learning_rate=0.02, max_delta_step=0, max_depth=12,\n",
    "              min_child_weight=1, missing= None ,\n",
    "              n_estimators=750, n_jobs=-1, num_parallel_tree=1,\n",
    "              objective='binary:logistic', random_state=0, reg_alpha=0,\n",
    "              reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
    "              tree_method='exact', validate_parameters=1)\n",
    "\n",
    "\n",
    "xgb_clf.fit(X_train_pp, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_clf.score(X_train_pp, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13. Get the Predicted Probabilities of Attrition with XGB Classifier (Kaggle Submission #1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = xgb_clf.predict_proba(X_test_pp )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Id  Attrition\n",
      "0      1   0.002298\n",
      "1      2   0.030858\n",
      "2      3   0.005094\n",
      "3      4   0.032337\n",
      "4      5   0.003861\n",
      "..   ...        ...\n",
      "465  466   0.085795\n",
      "466  467   0.920488\n",
      "467  468   0.663670\n",
      "468  469   0.013050\n",
      "469  470   0.002916\n",
      "\n",
      "[470 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(y_pred, columns =['Delete', 'Attrition'])  \n",
    "\n",
    "df.insert(0, 'Id', test['Id'])\n",
    "df=df.drop(['Delete'], axis = 1)\n",
    "print(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('XGB_FINAL_SUBMISSION', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14. Fit the model with the Light GBM Classifier with goss boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "clf = lgb.LGBMClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lgb \n",
    "clf = lgb.LGBMClassifier() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_feats = ['OverTime', 'MaritalStatus', 'JobRole', 'Gender', 'EducationField', 'Department', 'BusinessTravel']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/debojjalbagchi/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "for c in categorical_feats:\n",
    "    X_train[c] = X_train[c].astype('category')\n",
    "for c in categorical_feats:\n",
    "    X_test[c] = X_test[c].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_train = lgb.Dataset(X_train, y_train , categorical_feature = categorical_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "gbm = clf.fit(X_train, y_train , categorical_feature=categorical_feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 15. Get the Predicted Probabilities of Attrition using Light GBM Classifier (Kaggle Submission #2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Id  Attrition\n",
      "0      1   0.005776\n",
      "1      2   0.002193\n",
      "2      3   0.005664\n",
      "3      4   0.011200\n",
      "4      5   0.000900\n",
      "..   ...        ...\n",
      "465  466   0.268247\n",
      "466  467   0.993305\n",
      "467  468   0.020418\n",
      "468  469   0.026015\n",
      "469  470   0.001971\n",
      "\n",
      "[470 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(y_pred, columns =['Delete', 'Attrition'])  \n",
    "\n",
    "df.insert(0, 'Id', test['Id'])\n",
    "df=df.drop(['Delete'], axis = 1)\n",
    "print(df)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('LGBM_FINAL_SUBMISSION', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 16. Compare the models (Validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.model_selection\n",
    "X_train_val, X_test_val, y_train_val, y_test_val = sklearn.model_selection.train_test_split(X_train_pp, y_train, train_size=0.8,test_size=0.2, random_state=43)\n",
    "X_train_val_lgb, X_test_val_lgb, y_train_val_lgb, y_test_val_lgb = sklearn.model_selection.train_test_split(X_train, y_train, train_size=0.8,test_size=0.2, random_state=43)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_clf.fit(X_train_val, y_train_val)\n",
    "clf.fit(X_train_val_lgb, y_train_val_lgb)\n",
    "y_pred_xgb_val = xgb_clf.predict(X_test_val )\n",
    "y_pred_lgbm_val = clf.predict(X_test_val_lgb )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function sklearn.metrics.classification.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGB Classifier\n",
      "\n",
      "\n",
      "ACCURACY:\n",
      "0.950920245398773\n",
      "\n",
      "\n",
      "AUC:\n",
      "0.9555555555555555\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.91      0.95       180\n",
      "           1       0.90      1.00      0.95       146\n",
      "\n",
      "    accuracy                           0.95       326\n",
      "   macro avg       0.95      0.96      0.95       326\n",
      "weighted avg       0.96      0.95      0.95       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('XGB Classifier')\n",
    "print('\\n')\n",
    "\n",
    "print('ACCURACY:')\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(y_test_val, y_pred_xgb_val))\n",
    "print('\\n')\n",
    "print('AUC:')\n",
    "\n",
    "print(sklearn.metrics.roc_auc_score(y_test_val, y_pred_xgb_val))\n",
    "print('\\n')\n",
    "\n",
    "print('Classification Report:')\n",
    "\n",
    "print(sklearn.metrics.classification_report(y_test_val, y_pred_xgb_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Light GBM Classifier\n",
      "\n",
      "\n",
      "ACCURACY\n",
      "0.9601226993865031\n",
      "\n",
      "\n",
      "AUC\n",
      "0.9638888888888889\n",
      "\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.93      0.96       180\n",
      "           1       0.92      1.00      0.96       146\n",
      "\n",
      "    accuracy                           0.96       326\n",
      "   macro avg       0.96      0.96      0.96       326\n",
      "weighted avg       0.96      0.96      0.96       326\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('Light GBM Classifier')\n",
    "print('\\n')\n",
    "\n",
    "\n",
    "print('ACCURACY')\n",
    "\n",
    "print(sklearn.metrics.accuracy_score(y_test_val_lgb, y_pred_lgbm_val))\n",
    "print('\\n')\n",
    "\n",
    "print('AUC')\n",
    "\n",
    "print(sklearn.metrics.roc_auc_score(y_test_val_lgb, y_pred_lgbm_val))\n",
    "print('\\n')\n",
    "\n",
    "print('Classification Report:')\n",
    "\n",
    "print(sklearn.metrics.classification_report(y_test_val_lgb, y_pred_lgbm_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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