Employee_Turnover.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column names:\n",
      "['satisfaction_level', 'last_evaluation', 'number_project', 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left', 'promotion_last_5years', 'sales', 'salary']\n",
      "\n",
      "Sample data:\n"
     ]
    },
    {
     "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>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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "hr = pd.read_csv('HR.csv')\n",
    "col_names = hr.columns.tolist()\n",
    "print(\"Column names:\")\n",
    "print(col_names)\n",
    "\n",
    "print(\"\\nSample data:\")\n",
    "hr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the column name from \"sales\" to \"department\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hr=hr.rename(columns = {'sales':'department'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "last_evaluation          float64\n",
       "number_project             int64\n",
       "average_montly_hours       int64\n",
       "time_spend_company         int64\n",
       "Work_accident              int64\n",
       "left                       int64\n",
       "promotion_last_5years      int64\n",
       "department                object\n",
       "salary                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is pretty clean, no missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       False\n",
       "last_evaluation          False\n",
       "number_project           False\n",
       "average_montly_hours     False\n",
       "time_spend_company       False\n",
       "Work_accident            False\n",
       "left                     False\n",
       "promotion_last_5years    False\n",
       "department               False\n",
       "salary                   False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data contains 14,999 employees and 10 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "describe() method will give us some basic summary statistics about various fields of the dataset. Let's run some summary statistics to get an insight into what we are dealing with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
       "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr['department'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "hr['department']=np.where(hr['department'] =='support', 'technical', hr['department'])\n",
    "hr['department']=np.where(hr['department'] =='IT', 'technical', hr['department'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sales' 'accounting' 'hr' 'technical' 'management' 'product_mng'\n",
      " 'marketing' 'RandD']\n"
     ]
    }
   ],
   "source": [
    "print(hr['department'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploring - distribution of numeric variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11428\n",
       "1     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr['left'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <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>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      satisfaction_level  last_evaluation  number_project  \\\n",
       "left                                                        \n",
       "0               0.666810         0.715473        3.786664   \n",
       "1               0.440098         0.718113        3.855503   \n",
       "\n",
       "      average_montly_hours  time_spend_company  Work_accident  \\\n",
       "left                                                            \n",
       "0               199.060203            3.380032       0.175009   \n",
       "1               207.419210            3.876505       0.047326   \n",
       "\n",
       "      promotion_last_5years  \n",
       "left                         \n",
       "0                  0.026251  \n",
       "1                  0.005321  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>0.619822</td>\n",
       "      <td>0.712122</td>\n",
       "      <td>3.853875</td>\n",
       "      <td>200.800508</td>\n",
       "      <td>3.367217</td>\n",
       "      <td>0.170267</td>\n",
       "      <td>0.153748</td>\n",
       "      <td>0.034307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>0.582151</td>\n",
       "      <td>0.717718</td>\n",
       "      <td>3.825293</td>\n",
       "      <td>201.162973</td>\n",
       "      <td>3.522816</td>\n",
       "      <td>0.125163</td>\n",
       "      <td>0.265971</td>\n",
       "      <td>0.018253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>0.598809</td>\n",
       "      <td>0.708850</td>\n",
       "      <td>3.654939</td>\n",
       "      <td>198.684709</td>\n",
       "      <td>3.355886</td>\n",
       "      <td>0.120433</td>\n",
       "      <td>0.290934</td>\n",
       "      <td>0.020298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>0.621349</td>\n",
       "      <td>0.724000</td>\n",
       "      <td>3.860317</td>\n",
       "      <td>201.249206</td>\n",
       "      <td>4.303175</td>\n",
       "      <td>0.163492</td>\n",
       "      <td>0.144444</td>\n",
       "      <td>0.109524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>0.618601</td>\n",
       "      <td>0.715886</td>\n",
       "      <td>3.687646</td>\n",
       "      <td>199.385781</td>\n",
       "      <td>3.569930</td>\n",
       "      <td>0.160839</td>\n",
       "      <td>0.236597</td>\n",
       "      <td>0.050117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>0.619634</td>\n",
       "      <td>0.714756</td>\n",
       "      <td>3.807095</td>\n",
       "      <td>199.965632</td>\n",
       "      <td>3.475610</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.219512</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>0.614447</td>\n",
       "      <td>0.709717</td>\n",
       "      <td>3.776329</td>\n",
       "      <td>200.911353</td>\n",
       "      <td>3.534058</td>\n",
       "      <td>0.141787</td>\n",
       "      <td>0.244928</td>\n",
       "      <td>0.024155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>0.613687</td>\n",
       "      <td>0.720976</td>\n",
       "      <td>3.839054</td>\n",
       "      <td>201.813795</td>\n",
       "      <td>3.416127</td>\n",
       "      <td>0.144106</td>\n",
       "      <td>0.246924</td>\n",
       "      <td>0.008258</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             satisfaction_level  last_evaluation  number_project  \\\n",
       "department                                                         \n",
       "RandD                  0.619822         0.712122        3.853875   \n",
       "accounting             0.582151         0.717718        3.825293   \n",
       "hr                     0.598809         0.708850        3.654939   \n",
       "management             0.621349         0.724000        3.860317   \n",
       "marketing              0.618601         0.715886        3.687646   \n",
       "product_mng            0.619634         0.714756        3.807095   \n",
       "sales                  0.614447         0.709717        3.776329   \n",
       "technical              0.613687         0.720976        3.839054   \n",
       "\n",
       "             average_montly_hours  time_spend_company  Work_accident  \\\n",
       "department                                                             \n",
       "RandD                  200.800508            3.367217       0.170267   \n",
       "accounting             201.162973            3.522816       0.125163   \n",
       "hr                     198.684709            3.355886       0.120433   \n",
       "management             201.249206            4.303175       0.163492   \n",
       "marketing              199.385781            3.569930       0.160839   \n",
       "product_mng            199.965632            3.475610       0.146341   \n",
       "sales                  200.911353            3.534058       0.141787   \n",
       "technical              201.813795            3.416127       0.144106   \n",
       "\n",
       "                 left  promotion_last_5years  \n",
       "department                                    \n",
       "RandD        0.153748               0.034307  \n",
       "accounting   0.265971               0.018253  \n",
       "hr           0.290934               0.020298  \n",
       "management   0.144444               0.109524  \n",
       "marketing    0.236597               0.050117  \n",
       "product_mng  0.219512               0.000000  \n",
       "sales        0.244928               0.024155  \n",
       "technical    0.246924               0.008258  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.groupby('department').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>salary</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>0.637470</td>\n",
       "      <td>0.704325</td>\n",
       "      <td>3.767179</td>\n",
       "      <td>199.867421</td>\n",
       "      <td>3.692805</td>\n",
       "      <td>0.155214</td>\n",
       "      <td>0.066289</td>\n",
       "      <td>0.058205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>0.600753</td>\n",
       "      <td>0.717017</td>\n",
       "      <td>3.799891</td>\n",
       "      <td>200.996583</td>\n",
       "      <td>3.438218</td>\n",
       "      <td>0.142154</td>\n",
       "      <td>0.296884</td>\n",
       "      <td>0.009021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medium</th>\n",
       "      <td>0.621817</td>\n",
       "      <td>0.717322</td>\n",
       "      <td>3.813528</td>\n",
       "      <td>201.338349</td>\n",
       "      <td>3.529010</td>\n",
       "      <td>0.145361</td>\n",
       "      <td>0.204313</td>\n",
       "      <td>0.028079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        satisfaction_level  last_evaluation  number_project  \\\n",
       "salary                                                        \n",
       "high              0.637470         0.704325        3.767179   \n",
       "low               0.600753         0.717017        3.799891   \n",
       "medium            0.621817         0.717322        3.813528   \n",
       "\n",
       "        average_montly_hours  time_spend_company  Work_accident      left  \\\n",
       "salary                                                                      \n",
       "high              199.867421            3.692805       0.155214  0.066289   \n",
       "low               200.996583            3.438218       0.142154  0.296884   \n",
       "medium            201.338349            3.529010       0.145361  0.204313   \n",
       "\n",
       "        promotion_last_5years  \n",
       "salary                         \n",
       "high                 0.058205  \n",
       "low                  0.009021  \n",
       "medium               0.028079  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.groupby('salary').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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wXiFpgKT/V3Qc9ZB0bD1l1jdJWkbSkKLjKDs3mHdB0geBL5LuVgd4FDgzIu4o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\nPMzMrGH/H3DCnRwAbRQuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7692e5240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "pd.crosstab(hr.department,hr.left).plot(kind='bar')\n",
    "plt.title('Turnover Frequency for Department')\n",
    "plt.xlabel('Department')\n",
    "plt.ylabel('Frequency of Turnover')\n",
    "plt.savefig('department_bar_chart')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cFMzMrMJJwczMKrrnKamnrVSzqoYD0498vt1yPzz+WKbcdQerDFyVG+/6a822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\nwczMKpwUzMyswknBzMwq/j/67Lu/C9CTIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a769621cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(hr.salary, hr.left)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Chart of Salary Level vs Turnover')\n",
    "plt.xlabel('Salary Level')\n",
    "plt.ylabel('Proportion of Employees')\n",
    "plt.savefig('salary_bar_chart')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>left</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RandD</th>\n",
       "      <td>666</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accounting</th>\n",
       "      <td>563</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr</th>\n",
       "      <td>524</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>539</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>marketing</th>\n",
       "      <td>655</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_mng</th>\n",
       "      <td>704</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sales</th>\n",
       "      <td>3126</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technical</th>\n",
       "      <td>4651</td>\n",
       "      <td>1525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "left            0     1\n",
       "department             \n",
       "RandD         666   121\n",
       "accounting    563   204\n",
       "hr            524   215\n",
       "management    539    91\n",
       "marketing     655   203\n",
       "product_mng   704   198\n",
       "sales        3126  1014\n",
       "technical    4651  1525"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(hr.department, hr.left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u0qpwCfA2uqcp3gZcCPyrxVhwVW0BtgCsX7++JicnF2Ox8zI1NcUw178nc90p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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a76955b358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_bins = 10\n",
    "\n",
    "hr.hist(bins=num_bins, figsize=(20,15))\n",
    "plt.savefig(\"hr_histogram_plots\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dummy variables for categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two categorical variables in the dataset and they need to be converted to dummy variables before they can be used for modelling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</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>department</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 department  \\\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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cat_vars=['department','salary']\n",
    "for var in cat_vars:\n",
    "    cat_list='var'+'_'+var\n",
    "    cat_list = pd.get_dummies(hr[var], prefix=var)\n",
    "    hr1=hr.join(cat_list)\n",
    "    hr=hr1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The actual categorical variable needs to be removed once the dummy variables have been created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "hr.drop(hr.columns[[8, 9]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
       "       'left', 'promotion_last_5years', 'department_RandD',\n",
       "       'department_accounting', 'department_hr', 'department_management',\n",
       "       'department_marketing', 'department_product_mng',\n",
       "       'department_sales', 'department_technical', 'salary_high',\n",
       "       'salary_low', 'salary_medium'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hr_vars=hr.columns.values.tolist()\n",
    "y=['left']\n",
    "X=[i for i in hr_vars if i not in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['satisfaction_level',\n",
       " 'last_evaluation',\n",
       " 'number_project',\n",
       " 'average_montly_hours',\n",
       " 'time_spend_company',\n",
       " 'Work_accident',\n",
       " 'promotion_last_5years',\n",
       " 'department_RandD',\n",
       " 'department_accounting',\n",
       " 'department_hr',\n",
       " 'department_management',\n",
       " 'department_marketing',\n",
       " 'department_product_mng',\n",
       " 'department_sales',\n",
       " 'department_technical',\n",
       " 'salary_high',\n",
       " 'salary_low',\n",
       " 'salary_medium']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True False False  True  True  True  True False  True  True False\n",
      " False False False  True  True False]\n",
      "[1 1 3 9 1 1 1 1 5 1 1 6 8 7 4 1 1 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "\n",
    "rfe = RFE(model, 10)\n",
    "rfe = rfe.fit(hr[X], hr[y])\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['satisfaction_level', 'last_evaluation', 'time_spend_company', 'Work_accident', 'promotion_last_5years', \n",
    "      'department_RandD', 'department_hr', 'department_management', 'salary_high', 'salary_low'] \n",
    "X=hr[cols]\n",
    "y=hr['left']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Susan\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic regression accuracy: 0.771\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Logistic regression accuracy: {:.3f}'.format(accuracy_score(y_test, logreg.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 0.978\n"
     ]
    }
   ],
   "source": [
    "print('Random Forest Accuracy: {:.3f}'.format(accuracy_score(y_test, rf.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "svc = SVC()\n",
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support vector machine accuracy: 0.909\n"
     ]
    }
   ],
   "source": [
    "print('Support vector machine accuracy: {:.3f}'.format(accuracy_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random forest won, right?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-fold cross validation average accuracy: 0.977\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import cross_val_score\n",
    "kfold = model_selection.KFold(n_splits=10, random_state=7)\n",
    "modelCV = RandomForestClassifier()\n",
    "scoring = 'accuracy'\n",
    "results = model_selection.cross_val_score(modelCV, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "print(\"10-fold cross validation average accuracy: %.3f\" % (results.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision and recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.98      0.99      3462\n",
      "          1       0.95      0.95      0.95      1038\n",
      "\n",
      "avg / total       0.98      0.98      0.98      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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khQ6d8ChVCoD47Vt4oGzZOz6ud2kvLqalYjabLdeWzl+5TDnP0viUvo8LaVcs2567cply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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a76a0b5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns\n",
    "forest_cm = metrics.confusion_matrix(y_pred, y_test, [1,0])\n",
    "sns.heatmap(forest_cm, annot=True, fmt='.2f',xticklabels = [\"Left\", \"Stayed\"] , yticklabels = [\"Left\", \"Stayed\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('Random Forest')\n",
    "plt.savefig('random_forest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.92      0.86      3462\n",
      "          1       0.51      0.26      0.35      1038\n",
      "\n",
      "avg / total       0.74      0.77      0.74      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JLAsLZ8ALz+fnoYsDud15HDD6NcIivmTZxx/g6enB6+MC+TQsnEH9XuDLsKWW7WfPm0+1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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a76a117518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logreg_y_pred = logreg.predict(X_test)\n",
    "logreg_cm = metrics.confusion_matrix(logreg_y_pred, y_test, [1,0])\n",
    "sns.heatmap(logreg_cm, annot=True, fmt='.2f',xticklabels = [\"Left\", \"Stayed\"] , yticklabels = [\"Left\", \"Stayed\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('Logistic Regression')\n",
    "plt.savefig('logistic_regression')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.92      0.94      3462\n",
      "          1       0.77      0.86      0.81      1038\n",
      "\n",
      "avg / total       0.91      0.91      0.91      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p2rUrAL///jvNmzfnwIEDNm+YiIhIQZJrQJ0wYQKRkZEAlC9fntmzZ2vpQRER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\nIiJiBQqoIiIiVqCAKiIiYgX/B1KRs5bqc3R5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a7768767b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svc_y_pred = svc.predict(X_test)\n",
    "svc_cm = metrics.confusion_matrix(svc_y_pred, y_test, [1,0])\n",
    "sns.heatmap(svc_cm, annot=True, fmt='.2f',xticklabels = [\"Left\", \"Stayed\"] , yticklabels = [\"Left\", \"Stayed\"] )\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.title('Support Vector Machine')\n",
    "plt.savefig('support_vector_machine')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When an employee left, how often does my classifier predict that correctly? This measurement is called \"recall\" and a quick look at these diagrams can demonstrate that random forest is clearly best for this criteria. Out of all the turnover cases, random forest correctly retrieved 991 out of 1038. This translates to a turnover \"recall\" of about 95% (991/1038), far better than logistic regression (26%) or support vector machines (85%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When a classifier predicts an employee will leave, how often does that employee actually leave? This measurement is called \"precision\". Random forest again out preforms the other two at about 95% precision (991 out of 1045) with logistic regression at about 51% (273 out of 540), and support vector machine at about 77% (890 out of 1150)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m8OwV+da9NvQmoiND7p+ZEMHBYsFbuw55Fgv26S+jV6lqdEUhIeQ+cU622GMk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a76a111f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "\n",
    "rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, rf.predict_proba(X_test)[:,1])\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot(rf_fpr, rf_tpr, label='Random Forest (area = %0.2f)' % rf_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "promotion_last_5years-0.20%\n",
      "department_management-0.22%\n",
      "department_hr-0.29%\n",
      "department_RandD-0.34%\n",
      "salary_high-0.55%\n",
      "salary_low-1.35%\n",
      "Work_accident-1.46%\n",
      "last_evaluation-19.19%\n",
      "time_spend_company-25.73%\n",
      "satisfaction_level-50.65%\n"
     ]
    }
   ],
   "source": [
    "feature_labels = np.array(['satisfaction_level', 'last_evaluation', 'time_spend_company', 'Work_accident', 'promotion_last_5years', \n",
    "      'department_RandD', 'department_hr', 'department_management', 'salary_high', 'salary_low'])\n",
    "importance = rf.feature_importances_\n",
    "feature_indexes_by_importance = importance.argsort()\n",
    "for index in feature_indexes_by_importance:\n",
    "    print('{}-{:.2f}%'.format(feature_labels[index], (importance[index] *100.0)))"
   ]
  },
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   "source": []
  }
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