main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Employee Turnover Prediction - Classification\n",
    "Employee Turnover is the measurement of the total number of employees who leave an organization in a particular year. Employee Turnover Prediction means to predict whether an employee is going to leave the organization in the coming period.<br>\n",
    "\n",
    "A Company uses this predictive analysis to measure how many employees they will need if the potential employees will leave their organization. A company also uses this predictive analysis to make the workplace better for employees by understanding the core reasons for the high turnover ratio.<br>\n",
    "\n",
    "Here 'left' is the target (dependent) variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps\n",
    "\n",
    "1. Import Library and Dataset\n",
    "2. Data Preprocessing\n",
    "    - Gethering dataset info\n",
    "    - Data Statistics\n",
    "    - Missing value handling \n",
    "    - columns operation\n",
    "3. Categorical to Numerical \n",
    "4. Data Visualization\n",
    "    - Get Insights\n",
    "    - Outlier detection\n",
    "    - Correlation \n",
    "5. Feature Selection\n",
    "6. Train and Test Split\n",
    "7. Model Building\n",
    "    - Logistic Regression\n",
    "    - Decision Tree\n",
    "    - Random Forest\n",
    "8. Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Library and dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains 10 columns (features) and 14999 rows (records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Column list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       " 'left',\n",
       " 'promotion_last_5years',\n",
       " 'sales',\n",
       " 'salary']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### dataframe info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14999 entries, 0 to 14998\n",
      "Data columns (total 10 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   satisfaction_level     14999 non-null  float64\n",
      " 1   last_evaluation        14999 non-null  float64\n",
      " 2   number_project         14999 non-null  int64  \n",
      " 3   average_montly_hours   14999 non-null  int64  \n",
      " 4   time_spend_company     14999 non-null  int64  \n",
      " 5   Work_accident          14999 non-null  int64  \n",
      " 6   left                   14999 non-null  int64  \n",
      " 7   promotion_last_5years  14999 non-null  int64  \n",
      " 8   sales                  14999 non-null  object \n",
      " 9   salary                 14999 non-null  object \n",
      "dtypes: float64(2), int64(6), object(2)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here sales and salary columns are object (string) datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Missing Value Handing - Checking Null Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "sales                    0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no null value (NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
       "  </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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Chnage the column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'sales' : 'department'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department</th>\n",
       "      <th>salary</th>\n",
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       "  <tbody>\n",
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       "      <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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Change the columns values "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'department' column of the dataset has many categories, need to reduce the categories for better modelling. \n",
    "Let’s see all the categories of the department column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sales', 'accounting', 'hr', 'technical', 'support', 'management',\n",
       "       'IT', 'product_mng', 'marketing', 'RandD'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['department'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can merge “technical”, “support” and “IT” columns into one column to make our analysis easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['department']=np.where(df['department'] =='support', 'technical', df['department'])\n",
    "\n",
    "df['department']=np.where(df['department'] =='support', 'technical', df['department'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Changed column value of “support” and “IT” into \"Technical\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical to Numerical - Feature Transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Label Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "depart = pd.get_dummies(df['department'], prefix='department', drop_first=True )\n",
    "sales = pd.get_dummies(df['salary'], prefix='salary', drop_first=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.join(depart)\n",
    "df = df.join(sales)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the actual variables need to be removed after the dummy variable have been created. Column names after creating dummy variables for categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['department', 'salary']\n",
    "df.drop(cols, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>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_RandD</th>\n",
       "      <th>department_accounting</th>\n",
       "      <th>department_hr</th>\n",
       "      <th>department_management</th>\n",
       "      <th>department_marketing</th>\n",
       "      <th>department_product_mng</th>\n",
       "      <th>department_sales</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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  \\\n",
       "0                   3              0     1                      0   \n",
       "1                   6              0     1                      0   \n",
       "2                   4              0     1                      0   \n",
       "3                   5              0     1                      0   \n",
       "4                   3              0     1                      0   \n",
       "\n",
       "   department_RandD  department_accounting  department_hr  \\\n",
       "0                 0                      0              0   \n",
       "1                 0                      0              0   \n",
       "2                 0                      0              0   \n",
       "3                 0                      0              0   \n",
       "4                 0                      0              0   \n",
       "\n",
       "   department_management  department_marketing  department_product_mng  \\\n",
       "0                      0                     0                       0   \n",
       "1                      0                     0                       0   \n",
       "2                      0                     0                       0   \n",
       "3                      0                     0                       0   \n",
       "4                      0                     0                       0   \n",
       "\n",
       "   department_sales  department_technical  salary_low  salary_medium  \n",
       "0                 1                     0           1              0  \n",
       "1                 1                     0           0              1  \n",
       "2                 1                     0           0              1  \n",
       "3                 1                     0           1              0  \n",
       "4                 1                     0           1              0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 18)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now this dataset contains 10 columns (features) and 14999 rows (records)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gets insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1170a7a8>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(df['satisfaction_level'], df['left'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x129e6880>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(df['last_evaluation'], df['left'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Distribution "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,25))\n",
    "plt_num =1\n",
    "\n",
    "for col in df.columns:\n",
    "    if plt_num < 5:\n",
    "        plt.subplot(5,2,plt_num)\n",
    "        sns.distplot(df[col])\n",
    "    plt_num += 1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier handing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,25))\n",
    "plt_num =1\n",
    "\n",
    "for col in df.columns:\n",
    "    if plt_num < 5:\n",
    "        plt.subplot(5,2,plt_num)\n",
    "        sns.boxplot(y=df[col])\n",
    "    plt_num += 1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,25))\n",
    "plt_num =1\n",
    "\n",
    "for col in df.columns:\n",
    "    if plt_num < 5:\n",
    "        plt.subplot(5,2,plt_num)\n",
    "        sns.countplot(df[col])\n",
    "    plt_num += 1\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x12afd0e8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3240x3240 with 342 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df[:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115ab220>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,30))\n",
    "sns.heatmap(df.corr(), annot=True, fmt='.0%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This heat map shows that there is no correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dependent and Independent Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('left', axis=1)\n",
    "y = df['left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature selection step decides which features are best option that can predict employee turnover with great accuracy or great performance.\n",
    "There are 17 features in X, we can select about 10 from them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\agraw\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass n_features_to_select=10 as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n",
      "c:\\users\\agraw\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\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]\n",
      "[1 1 2 8 1 1 1 1 4 1 1 5 7 6 3 1 1]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression()\n",
    "rfe = RFE(model, 10)\n",
    "\n",
    "rfe = rfe.fit(X,y)\n",
    "print(rfe.support_)\n",
    "print(rfe.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that feature selection chose the 10 variables for us, which are marked True in the support_ array and marked with a choice “1” in the ranking_array. Now lets have a look at these columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
       "       'promotion_last_5years', 'department_RandD', 'department_accounting',\n",
       "       'department_hr', 'department_management', 'department_marketing',\n",
       "       'department_product_mng', 'department_sales', 'department_technical',\n",
       "       'salary_low', 'salary_medium'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_cols = ['satisfaction_level', 'last_evaluation', 'time_spend_company', 'Work_accident', 'promotion_last_5years', 'department_RandD',\n",
    "       'department_hr', 'department_management', 'salary_low', 'salary_medium']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[new_cols]\n",
    "y = df['left']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state=92)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistics Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model_logistic = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7702638346509192"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7664444444444445"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logistic.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score of the logistic regression is about 0.76 , which is not so good."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model_tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9967615963425088"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9697777777777777"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score of the Decision Tree model is about 0.97. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model_rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.996666349176112"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9862222222222222"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy score of the Random Forest model is about 0.98. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I will construct a confusion matrix to visualize predictions made by our classifier and evaluate the accuracy of our machine learning classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      3439\n",
      "           1       0.97      0.98      0.97      1061\n",
      "\n",
      "    accuracy                           0.99      4500\n",
      "   macro avg       0.98      0.98      0.98      4500\n",
      "weighted avg       0.99      0.99      0.99      4500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test, model_rf.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\agraw\\appdata\\local\\programs\\python\\python38-32\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass labels=[1, 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Random Forest')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model_rf.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "forest_cm = confusion_matrix(y_pred, y_test, [1,0])\n",
    "\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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### AUC ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "#logistics\n",
    "logit_roc_auc = roc_auc_score(y_test, model_logistic.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, model_logistic.predict_proba(X_test)[:,1])\n",
    "# Random Forest\n",
    "rf_roc_auc = roc_auc_score(y_test, model_rf.predict(X_test))\n",
    "rf_fpr, rf_tpr, rf_thresholds = roc_curve(y_test, model_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",
    "\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.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The receiver operating characteristic (ROC) curve is a standard tool used with binary classifiers. The red dotted line represents the ROC curve of a purely random classifier; a good classifier stays as far away from that line as possible (toward the top-left corner)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, as we can see that the Random Forest Model has proven to be more useful in the prediction of employee turnover, now let’s have a look at the feature importance of our random forest classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.50328787, 0.18755061, 0.26592571, 0.01416888, 0.00255465,\n",
       "       0.00297385, 0.00306101, 0.0033782 , 0.01170796, 0.00539126])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "promotion_last_5years    -> 0.26%\n",
      "department_RandD    -> 0.30%\n",
      "department_hr    -> 0.31%\n",
      "department_management    -> 0.34%\n",
      "salary_low    -> 0.54%\n",
      "salary_high    -> 1.17%\n",
      "Work_accident    -> 1.42%\n",
      "last_evaluation    -> 18.76%\n",
      "time_spend_company    -> 26.59%\n",
      "satisfaction_level    -> 50.33%\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 = model_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)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to our Random Forest classification model, the above aspects show the most important features which will influence whether an employee will leave the company, in ascending order."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}