Part-3-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>...</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>NaN</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>NaN</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 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",
       "\n",
       "  Dateofjoining LastWorkingDate  Joining Designation  ...  \\\n",
       "0    2015-12-24             NaN                    1  ...   \n",
       "1    2015-12-24             NaN                    1  ...   \n",
       "2    2015-12-24      2016-03-11                    1  ...   \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",
       "\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",
       "\n",
       "   emp_quarter_current emp_quarter_last attrition  \n",
       "0               2016Q1           2016Q1         0  \n",
       "1               2016Q1           2016Q1         0  \n",
       "2               2016Q1           2016Q1         0  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv (\"data_processed_v1.csv\")\n",
    "data.head (3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = data.drop (columns = ['Emp_ID', 'MMM-YY', 'Gender', 'LastWorkingDate', 'Dateofjoining',\n",
    "                              'emp_salary_change', 'emp_rating_change', 'Total Business Value',\n",
    "                              'emp_quarter_current', 'emp_quarter_last', 'attrition'])\n",
    "y_data = data ['attrition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_designation_change</th>\n",
       "      <th>emp_business_value_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.262839</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.281340</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.234132</td>\n",
       "      <td>0.0</td>\n",
       "      <td>665480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  City  Education_Level  Salary  Joining Designation  Designation  \\\n",
       "0   28    15                2   57387                    1            1   \n",
       "1   28    15                2   57387                    1            1   \n",
       "2   28    15                2   57387                    1            1   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_designation_change  \\\n",
       "0                 2      0.262839                     NaN   \n",
       "1                 2      1.281340                     0.0   \n",
       "2                 2      2.234132                     0.0   \n",
       "\n",
       "   emp_business_value_change  \n",
       "0                        NaN  \n",
       "1                 -3046540.0  \n",
       "2                   665480.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.head (3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "Name: attrition, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_data.head (3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change NaN values to 0\n",
    "x_data.emp_designation_change = x_data.emp_designation_change.fillna(0)\n",
    "x_data.emp_business_value_change = x_data.emp_business_value_change.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Education_Level</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Joining Designation</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Quarterly Rating</th>\n",
       "      <th>emp_duration</th>\n",
       "      <th>emp_designation_change</th>\n",
       "      <th>emp_business_value_change</th>\n",
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       "      <th>0</th>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
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       "      <td>57387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>28</td>\n",
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       "      <td>57387</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-3046540.0</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>57387</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2.234132</td>\n",
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       "      <td>665480.0</td>\n",
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      "text/plain": [
       "   Age  City  Education_Level  Salary  Joining Designation  Designation  \\\n",
       "0   28    15                2   57387                    1            1   \n",
       "1   28    15                2   57387                    1            1   \n",
       "2   28    15                2   57387                    1            1   \n",
       "\n",
       "   Quarterly Rating  emp_duration  emp_designation_change  \\\n",
       "0                 2      0.262839                     0.0   \n",
       "1                 2      1.281340                     0.0   \n",
       "2                 2      2.234132                     0.0   \n",
       "\n",
       "   emp_business_value_change  \n",
       "0                        0.0  \n",
       "1                 -3046540.0  \n",
       "2                   665480.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data.head (3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data.to_csv (\"x_data.csv\", index = False)\n",
    "y_data.to_csv (\"y_data.csv\", index = False)"
   ]
  }
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