Part-1-Data_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0  2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1  2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2  2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaN                    1            1               2381060   \n",
       "1             NaN                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  \n",
       "0                 2  \n",
       "1                 2  \n",
       "2                 2  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv (\"train.csv\")\n",
    "data_processed = data\n",
    "data.head (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert the MMM-YY, Date Of Joining and Last Working Date Columns to Datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>C7</td>\n",
       "      <td>Master</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>C7</td>\n",
       "      <td>Master</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "3 2017-11-01       2   31   Male   C7          Master   67016    2017-11-06   \n",
       "4 2017-12-01       2   31   Male   C7          Master   67016    2017-11-06   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "3             NaT                    2            2                     0   \n",
       "4             NaT                    2            2                     0   \n",
       "\n",
       "   Quarterly Rating  \n",
       "0                 2  \n",
       "1                 2  \n",
       "2                 2  \n",
       "3                 1  \n",
       "4                 1  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data ['MMM-YY'] = pd.to_datetime (data ['MMM-YY'])\n",
    "data.Dateofjoining = pd.to_datetime (data.Dateofjoining)\n",
    "data.LastWorkingDate = pd.to_datetime (data.LastWorkingDate)\n",
    "data.head (5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add column for No. of months the person worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.26283907, 1.28134048, 2.23413212, ..., 3.77831167, 4.79681308,\n",
       "       5.7824596 ])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_duration = ((data ['MMM-YY'] - data.Dateofjoining)/np.timedelta64 (1, 'M')).values\n",
    "# There were certain dates where this will output a negative value, so to get rid of those :-\n",
    "emp_duration = np.clip (emp_duration, 0, 100000)\n",
    "emp_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame(emp_duration, columns = ['emp_duration'])\n",
    "data_processed = pd.concat([data_processed, new_data], join = 'outer', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  \n",
       "0                 2      0.262839  \n",
       "1                 2      1.281340  \n",
       "2                 2      2.234132  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Amount by which the salary of a person changed compared to the last month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed ['emp_salary_change'] = data.groupby ('Emp_ID')['Salary'].diff ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_salary_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  \n",
       "0                 2      0.262839                NaN  \n",
       "1                 2      1.281340                0.0  \n",
       "2                 2      2.234132                0.0  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Amount by which the Rating of a person changed compared to the last month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed ['emp_rating_change'] = data.groupby ('Emp_ID')['Quarterly Rating'].diff ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>MMM-YY</th>\n",
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       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>Master</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Male</td>\n",
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       "      <td>Master</td>\n",
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       "      <td>2015-12-24</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  emp_rating_change  \n",
       "0                 2      0.262839                NaN                NaN  \n",
       "1                 2      1.281340                0.0                0.0  \n",
       "2                 2      2.234132                0.0                0.0  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Amount by which the Designation of a person changed compared to the last month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed ['emp_designation_change'] = data.groupby ('Emp_ID')['Designation'].diff ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>emp_rating_change</th>\n",
       "      <th>emp_designation_change</th>\n",
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       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  emp_rating_change  \\\n",
       "0                 2      0.262839                NaN                NaN   \n",
       "1                 2      1.281340                0.0                0.0   \n",
       "2                 2      2.234132                0.0                0.0   \n",
       "\n",
       "   emp_designation_change  \n",
       "0                     NaN  \n",
       "1                     0.0  \n",
       "2                     0.0  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Amount by which the Business Value of a person changed compared to the last month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed ['emp_business_value_change'] = data.groupby ('Emp_ID')['Total Business Value'].diff ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
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       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  emp_rating_change  \\\n",
       "0                 2      0.262839                NaN                NaN   \n",
       "1                 2      1.281340                0.0                0.0   \n",
       "2                 2      2.234132                0.0                0.0   \n",
       "\n",
       "   emp_designation_change  emp_business_value_change  \n",
       "0                     NaN                        NaN  \n",
       "1                     0.0                 -3046540.0  \n",
       "2                     0.0                   665480.0  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert MMM-YY to Quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2016Q1</td>\n",
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       "      <th>2</th>\n",
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       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  emp_rating_change  \\\n",
       "0                 2      0.262839                NaN                NaN   \n",
       "1                 2      1.281340                0.0                0.0   \n",
       "2                 2      2.234132                0.0                0.0   \n",
       "\n",
       "   emp_designation_change  emp_business_value_change emp_quarter_current  \n",
       "0                     NaN                        NaN              2016Q1  \n",
       "1                     0.0                 -3046540.0              2016Q1  \n",
       "2                     0.0                   665480.0              2016Q1  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed ['emp_quarter_current'] = pd.PeriodIndex (data_processed ['MMM-YY'], freq = 'Q')\n",
    "data_processed.head (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the last working quarter of the employee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LastWorkingDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emp_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-03-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-03-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       LastWorkingDate\n",
       "Emp_ID                \n",
       "1           2016-03-11\n",
       "2                  NaT\n",
       "4           2017-04-27\n",
       "5           2016-03-07\n",
       "6                  NaT"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data_processed.groupby ('Emp_ID')['LastWorkingDate'].max ()\n",
    "x = pd.DataFrame (x)\n",
    "x.head ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For currently working employees (**NaT values**), set the last working day beyond the next 2 quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = pd.to_datetime ('2018-12-1')\n",
    "x.LastWorkingDate = x.LastWorkingDate.fillna (end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>emp_quarter_last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emp_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018Q4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018Q4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       emp_quarter_last\n",
       "Emp_ID                 \n",
       "1                2016Q1\n",
       "2                2018Q4\n",
       "4                2017Q2\n",
       "5                2016Q1\n",
       "6                2018Q4"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x ['emp_quarter_last'] = pd.PeriodIndex (x.LastWorkingDate, freq = 'Q')\n",
    "x = x.drop (columns = ['LastWorkingDate'])\n",
    "x.head ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed = pd.merge (data_processed, x, on = 'Emp_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_salary_change</th>\n",
       "      <th>emp_rating_change</th>\n",
       "      <th>emp_designation_change</th>\n",
       "      <th>emp_business_value_change</th>\n",
       "      <th>emp_quarter_current</th>\n",
       "      <th>emp_quarter_last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>Male</td>\n",
       "      <td>C23</td>\n",
       "      <td>Master</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age Gender City Education_Level  Salary Dateofjoining  \\\n",
       "0 2016-01-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "1 2016-02-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "2 2016-03-01       1   28   Male  C23          Master   57387    2015-12-24   \n",
       "\n",
       "  LastWorkingDate  Joining Designation  Designation  Total Business Value  \\\n",
       "0             NaT                    1            1               2381060   \n",
       "1             NaT                    1            1               -665480   \n",
       "2      2016-03-11                    1            1                     0   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_salary_change  emp_rating_change  \\\n",
       "0                 2      0.262839                NaN                NaN   \n",
       "1                 2      1.281340                0.0                0.0   \n",
       "2                 2      2.234132                0.0                0.0   \n",
       "\n",
       "   emp_designation_change  emp_business_value_change emp_quarter_current  \\\n",
       "0                     NaN                        NaN              2016Q1   \n",
       "1                     0.0                 -3046540.0              2016Q1   \n",
       "2                     0.0                   665480.0              2016Q1   \n",
       "\n",
       "  emp_quarter_last  \n",
       "0           2016Q1  \n",
       "1           2016Q1  \n",
       "2           2016Q1  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head (3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the Attrition value\n",
    "##### Attrition = 1, if the person DOES NOT leave the company in the next 2 Quarters\n",
    "##### Attrition = 0, if the person DOES leave the company in the next 2 Quarters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "attrition = np.where (data_processed ['emp_duration'] >= 6, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "attrition = []\n",
    "for i in range (len (data_processed)) :\n",
    "    day_0 = data_processed.emp_quarter_current[i].to_timestamp()\n",
    "    day_1 = data_processed.emp_quarter_last[i].to_timestamp()\n",
    "    months = (day_1 - day_0) / np.timedelta64(1, 'M')\n",
    "    if months > 5.5 :\n",
    "        attrition.append (1)\n",
    "    else :\n",
    "        attrition.append (0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed ['attrition'] = attrition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>...</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_salary_change</th>\n",
       "      <th>emp_rating_change</th>\n",
       "      <th>emp_designation_change</th>\n",
       "      <th>emp_business_value_change</th>\n",
       "      <th>emp_quarter_current</th>\n",
       "      <th>emp_quarter_last</th>\n",
       "      <th>attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017Q4</td>\n",
       "      <td>2018Q4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.821372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017Q4</td>\n",
       "      <td>2018Q4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age  Gender  City  Education_Level  Salary  \\\n",
       "0 2016-01-01       1   28       1    15                2   57387   \n",
       "1 2016-02-01       1   28       1    15                2   57387   \n",
       "2 2016-03-01       1   28       1    15                2   57387   \n",
       "3 2017-11-01       2   31       1    26                2   67016   \n",
       "4 2017-12-01       2   31       1    26                2   67016   \n",
       "\n",
       "  Dateofjoining LastWorkingDate  Joining Designation  ...  \\\n",
       "0    2015-12-24             NaT                    1  ...   \n",
       "1    2015-12-24             NaT                    1  ...   \n",
       "2    2015-12-24      2016-03-11                    1  ...   \n",
       "3    2017-11-06             NaT                    2  ...   \n",
       "4    2017-11-06             NaT                    2  ...   \n",
       "\n",
       "   Total Business Value  Quarterly Rating  emp_duration  emp_salary_change  \\\n",
       "0               2381060                 2      0.262839                NaN   \n",
       "1               -665480                 2      1.281340                0.0   \n",
       "2                     0                 2      2.234132                0.0   \n",
       "3                     0                 1      0.000000                NaN   \n",
       "4                     0                 1      0.821372                0.0   \n",
       "\n",
       "   emp_rating_change  emp_designation_change  emp_business_value_change  \\\n",
       "0                NaN                     NaN                        NaN   \n",
       "1                0.0                     0.0                 -3046540.0   \n",
       "2                0.0                     0.0                   665480.0   \n",
       "3                NaN                     NaN                        NaN   \n",
       "4                0.0                     0.0                        0.0   \n",
       "\n",
       "   emp_quarter_current emp_quarter_last attrition  \n",
       "0               2016Q1           2016Q1         0  \n",
       "1               2016Q1           2016Q1         0  \n",
       "2               2016Q1           2016Q1         0  \n",
       "3               2017Q4           2018Q4         1  \n",
       "4               2017Q4           2018Q4         1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "data_processed.Gender = label_encoder.fit_transform (data_processed.Gender)\n",
    "data_processed.City = label_encoder.fit_transform (data_processed.City)\n",
    "data_processed.Education_Level = label_encoder.fit_transform (data_processed.Education_Level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "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>MMM-YY</th>\n",
       "      <th>Emp_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Dateofjoining</th>\n",
       "      <th>LastWorkingDate</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>...</th>\n",
       "      <th>Total Business Value</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_salary_change</th>\n",
       "      <th>emp_rating_change</th>\n",
       "      <th>emp_designation_change</th>\n",
       "      <th>emp_business_value_change</th>\n",
       "      <th>emp_quarter_current</th>\n",
       "      <th>emp_quarter_last</th>\n",
       "      <th>attrition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2381060</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-665480</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>2016-03-11</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>2016Q1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-11-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2017Q4</td>\n",
       "      <td>2018Q4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-01</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>2</td>\n",
       "      <td>67016</td>\n",
       "      <td>2017-11-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.821372</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017Q4</td>\n",
       "      <td>2018Q4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MMM-YY  Emp_ID  Age  Gender  City  Education_Level  Salary  \\\n",
       "0 2016-01-01       1   28       1    15                2   57387   \n",
       "1 2016-02-01       1   28       1    15                2   57387   \n",
       "2 2016-03-01       1   28       1    15                2   57387   \n",
       "3 2017-11-01       2   31       1    26                2   67016   \n",
       "4 2017-12-01       2   31       1    26                2   67016   \n",
       "\n",
       "  Dateofjoining LastWorkingDate  Joining Designation  ...  \\\n",
       "0    2015-12-24             NaT                    1  ...   \n",
       "1    2015-12-24             NaT                    1  ...   \n",
       "2    2015-12-24      2016-03-11                    1  ...   \n",
       "3    2017-11-06             NaT                    2  ...   \n",
       "4    2017-11-06             NaT                    2  ...   \n",
       "\n",
       "   Total Business Value  Quarterly Rating  emp_duration  emp_salary_change  \\\n",
       "0               2381060                 2      0.262839                NaN   \n",
       "1               -665480                 2      1.281340                0.0   \n",
       "2                     0                 2      2.234132                0.0   \n",
       "3                     0                 1      0.000000                NaN   \n",
       "4                     0                 1      0.821372                0.0   \n",
       "\n",
       "   emp_rating_change  emp_designation_change  emp_business_value_change  \\\n",
       "0                NaN                     NaN                        NaN   \n",
       "1                0.0                     0.0                 -3046540.0   \n",
       "2                0.0                     0.0                   665480.0   \n",
       "3                NaN                     NaN                        NaN   \n",
       "4                0.0                     0.0                        0.0   \n",
       "\n",
       "   emp_quarter_current emp_quarter_last attrition  \n",
       "0               2016Q1           2016Q1         0  \n",
       "1               2016Q1           2016Q1         0  \n",
       "2               2016Q1           2016Q1         0  \n",
       "3               2017Q4           2018Q4         0  \n",
       "4               2017Q4           2018Q4         0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_processed.head ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_processed.to_csv (\"data_processed_v1.csv\", index = False)"
   ]
  }
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