Project - Predicting Employee Exit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_data = pd.read_csv('Data/HR_comma_sep.csv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>sales</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years  sales  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_data.rename(columns={'sales':'dept'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years   dept  \\\n",
       "0                   3              0     1                      0  sales   \n",
       "1                   6              0     1                      0  sales   \n",
       "2                   4              0     1                      0  sales   \n",
       "3                   5              0     1                      0  sales   \n",
       "4                   3              0     1                      0  sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.612834</td>\n",
       "      <td>0.716102</td>\n",
       "      <td>3.803054</td>\n",
       "      <td>201.050337</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.248631</td>\n",
       "      <td>0.171169</td>\n",
       "      <td>1.232592</td>\n",
       "      <td>49.943099</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>2.000000</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>0.440000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.720000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.820000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>245.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "count        14999.000000     14999.000000    14999.000000   \n",
       "mean             0.612834         0.716102        3.803054   \n",
       "std              0.248631         0.171169        1.232592   \n",
       "min              0.090000         0.360000        2.000000   \n",
       "25%              0.440000         0.560000        3.000000   \n",
       "50%              0.640000         0.720000        4.000000   \n",
       "75%              0.820000         0.870000        5.000000   \n",
       "max              1.000000         1.000000        7.000000   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident          left  \\\n",
       "count          14999.000000        14999.000000   14999.000000  14999.000000   \n",
       "mean             201.050337            3.498233       0.144610      0.238083   \n",
       "std               49.943099            1.460136       0.351719      0.425924   \n",
       "min               96.000000            2.000000       0.000000      0.000000   \n",
       "25%              156.000000            3.000000       0.000000      0.000000   \n",
       "50%              200.000000            3.000000       0.000000      0.000000   \n",
       "75%              245.000000            4.000000       0.000000      0.000000   \n",
       "max              310.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  \n",
       "count           14999.000000  \n",
       "mean                0.021268  \n",
       "std                 0.144281  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dept', 'salary'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sales          4140\n",
       "technical      2720\n",
       "support        2229\n",
       "IT             1227\n",
       "product_mng     902\n",
       "marketing       858\n",
       "RandD           787\n",
       "accounting      767\n",
       "hr              739\n",
       "management      630\n",
       "Name: dept, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.dept.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dept = le.fit_transform(emp_data.dept)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe_dept = ohe.fit_transform(dept.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe.active_features_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['IT', 'RandD', 'accounting', 'hr', 'management', 'marketing',\n",
       "       'product_mng', 'sales', 'support', 'technical'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dept_df = pd.DataFrame(ohe_dept.toarray(), dtype=int,columns=le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_data['salary_tf'] = emp_data.salary.map({'low':1,'medium':2,'high':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'dept', 'salary', 'salary_tf'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = emp_data[['number_project','average_montly_hours', 'time_spend_company']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x159e3514ac8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x159e31d04e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.kde()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "mm = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_np = mm.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dept_np = dept_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_df = emp_data[['satisfaction_level','last_evaluation','Work_accident','promotion_last_5years','salary_tf']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_np = emp_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = np.hstack([emp_np, scaled_np, dept_np])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = emp_data.left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 18)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11428\n",
       "1     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_data.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression, SGDClassifier, PassiveAggressiveClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [ LogisticRegression(class_weight='balanced'), SGDClassifier(max_iter=10), PassiveAggressiveClassifier(max_iter=20), RandomForestClassifier(n_estimators=20)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX,testX,trainY,testY = train_test_split(feature_data,target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7626666666666667\n",
      "0.8133333333333334\n",
      "0.6269333333333333\n",
      "0.9861333333333333\n"
     ]
    }
   ],
   "source": [
    "for model in models:\n",
    "    model.fit(trainX,trainY)\n",
    "    print (model.score(testX,testY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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