Employee Attrition.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "0f7860e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e48550a2",
   "metadata": {},
   "source": [
    "### Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56fc0a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/zekelabs/data-science-complete-tutorial/master/Data/HR_comma_sep.csv.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c04e2328",
   "metadata": {
    "scrolled": true
   },
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(url)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af82c6da",
   "metadata": {},
   "source": [
    "### Checking data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee063fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "67c7cc6f",
   "metadata": {
    "scrolled": true
   },
   "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: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "56a362b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>department_IT</th>\n",
       "      <th>department_RandD</th>\n",
       "      <th>...</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_support</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</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",
       "      <td>14999.000000</td>\n",
       "      <td>14999.000000</td>\n",
       "      <td>...</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",
       "      <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>0.490889</td>\n",
       "      <td>3.498233</td>\n",
       "      <td>0.144610</td>\n",
       "      <td>0.238083</td>\n",
       "      <td>0.021268</td>\n",
       "      <td>0.081805</td>\n",
       "      <td>0.052470</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049270</td>\n",
       "      <td>0.042003</td>\n",
       "      <td>0.057204</td>\n",
       "      <td>0.060137</td>\n",
       "      <td>0.276018</td>\n",
       "      <td>0.148610</td>\n",
       "      <td>0.181345</td>\n",
       "      <td>0.082472</td>\n",
       "      <td>0.487766</td>\n",
       "      <td>0.429762</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>0.233379</td>\n",
       "      <td>1.460136</td>\n",
       "      <td>0.351719</td>\n",
       "      <td>0.425924</td>\n",
       "      <td>0.144281</td>\n",
       "      <td>0.274077</td>\n",
       "      <td>0.222981</td>\n",
       "      <td>...</td>\n",
       "      <td>0.216438</td>\n",
       "      <td>0.200602</td>\n",
       "      <td>0.232239</td>\n",
       "      <td>0.237749</td>\n",
       "      <td>0.447041</td>\n",
       "      <td>0.355715</td>\n",
       "      <td>0.385317</td>\n",
       "      <td>0.275092</td>\n",
       "      <td>0.499867</td>\n",
       "      <td>0.495059</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>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.280374</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.485981</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>0.696262</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</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>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 34 columns</p>\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               0.490889            3.498233       0.144610      0.238083   \n",
       "std                0.233379            1.460136       0.351719      0.425924   \n",
       "min                0.000000            2.000000       0.000000      0.000000   \n",
       "25%                0.280374            3.000000       0.000000      0.000000   \n",
       "50%                0.485981            3.000000       0.000000      0.000000   \n",
       "75%                0.696262            4.000000       0.000000      0.000000   \n",
       "max                1.000000           10.000000       1.000000      1.000000   \n",
       "\n",
       "       promotion_last_5years  department_IT  department_RandD  ...  \\\n",
       "count           14999.000000   14999.000000      14999.000000  ...   \n",
       "mean                0.021268       0.081805          0.052470  ...   \n",
       "std                 0.144281       0.274077          0.222981  ...   \n",
       "min                 0.000000       0.000000          0.000000  ...   \n",
       "25%                 0.000000       0.000000          0.000000  ...   \n",
       "50%                 0.000000       0.000000          0.000000  ...   \n",
       "75%                 0.000000       0.000000          0.000000  ...   \n",
       "max                 1.000000       1.000000          1.000000  ...   \n",
       "\n",
       "       department_hr  department_management  department_marketing  \\\n",
       "count   14999.000000           14999.000000          14999.000000   \n",
       "mean        0.049270               0.042003              0.057204   \n",
       "std         0.216438               0.200602              0.232239   \n",
       "min         0.000000               0.000000              0.000000   \n",
       "25%         0.000000               0.000000              0.000000   \n",
       "50%         0.000000               0.000000              0.000000   \n",
       "75%         0.000000               0.000000              0.000000   \n",
       "max         1.000000               1.000000              1.000000   \n",
       "\n",
       "       department_product_mng  department_sales  department_support  \\\n",
       "count            14999.000000      14999.000000        14999.000000   \n",
       "mean                 0.060137          0.276018            0.148610   \n",
       "std                  0.237749          0.447041            0.355715   \n",
       "min                  0.000000          0.000000            0.000000   \n",
       "25%                  0.000000          0.000000            0.000000   \n",
       "50%                  0.000000          0.000000            0.000000   \n",
       "75%                  0.000000          1.000000            0.000000   \n",
       "max                  1.000000          1.000000            1.000000   \n",
       "\n",
       "       department_technical   salary_high    salary_low  salary_medium  \n",
       "count          14999.000000  14999.000000  14999.000000   14999.000000  \n",
       "mean               0.181345      0.082472      0.487766       0.429762  \n",
       "std                0.385317      0.275092      0.499867       0.495059  \n",
       "min                0.000000      0.000000      0.000000       0.000000  \n",
       "25%                0.000000      0.000000      0.000000       0.000000  \n",
       "50%                0.000000      0.000000      0.000000       0.000000  \n",
       "75%                0.000000      0.000000      1.000000       1.000000  \n",
       "max                1.000000      1.000000      1.000000       1.000000  \n",
       "\n",
       "[8 rows x 34 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c19b77f",
   "metadata": {
    "scrolled": false
   },
   "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": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "542ea7fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
       "       'promotion_last_5years', 'sales', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108c30c1",
   "metadata": {},
   "source": [
    "### Dropping duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4c47f8a1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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",
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       "      <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",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11995</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>3</td>\n",
       "      <td>259</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>management</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11996</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.95</td>\n",
       "      <td>5</td>\n",
       "      <td>266</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>management</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11997</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.54</td>\n",
       "      <td>3</td>\n",
       "      <td>185</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>management</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11998</th>\n",
       "      <td>0.33</td>\n",
       "      <td>0.65</td>\n",
       "      <td>3</td>\n",
       "      <td>172</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>marketing</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11999</th>\n",
       "      <td>0.50</td>\n",
       "      <td>0.73</td>\n",
       "      <td>4</td>\n",
       "      <td>180</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IT</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11991 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "11995                0.90             0.55               3   \n",
       "11996                0.74             0.95               5   \n",
       "11997                0.85             0.54               3   \n",
       "11998                0.33             0.65               3   \n",
       "11999                0.50             0.73               4   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "11995                   259                  10              1     0   \n",
       "11996                   266                  10              0     0   \n",
       "11997                   185                  10              0     0   \n",
       "11998                   172                  10              0     0   \n",
       "11999                   180                   3              0     0   \n",
       "\n",
       "       promotion_last_5years       sales  salary  \n",
       "0                          0       sales     low  \n",
       "1                          0       sales  medium  \n",
       "2                          0       sales  medium  \n",
       "3                          0       sales     low  \n",
       "4                          0       sales     low  \n",
       "...                      ...         ...     ...  \n",
       "11995                      1  management    high  \n",
       "11996                      1  management    high  \n",
       "11997                      1  management    high  \n",
       "11998                      1   marketing    high  \n",
       "11999                      0          IT     low  \n",
       "\n",
       "[11991 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5b6c278",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2bbaa535",
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {'sales':'department'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fabc4cd2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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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>department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
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       "    </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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <td>low</td>\n",
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       "      <th>...</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.rename(dic,axis=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd96e0d2",
   "metadata": {},
   "source": [
    "### Data visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d122cea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = data['department'],y=data['satisfaction_level'])\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b5054b01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = data['department'],y=data['satisfaction_level'], hue=data['salary'])\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.25, 1))\n",
    "plt.xticks(rotation=45)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6c2be70c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data['department'])\n",
    "plt.xticks(rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "80e0ae57",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average_montly_hours', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data['average_montly_hours'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "1f12f00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time_spend_company', ylabel='Density'>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data['time_spend_company'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a7a910bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Work_accident', ylabel='Density'>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data['Work_accident'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ced61088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.corr(),annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25d4ef8c",
   "metadata": {},
   "source": [
    "### Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9bad93f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "mms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7adb35c",
   "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</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>0.285047</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>0.775701</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>0.822430</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>0.593458</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>0.294393</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
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       "    <tr>\n",
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       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>0.219626</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>0.859813</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>0.289720</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                  0.285047                   3              0     1   \n",
       "1                  0.775701                   6              0     1   \n",
       "2                  0.822430                   4              0     1   \n",
       "3                  0.593458                   5              0     1   \n",
       "4                  0.294393                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994              0.257009                   3              0     1   \n",
       "14995              0.299065                   3              0     1   \n",
       "14996              0.219626                   3              0     1   \n",
       "14997              0.859813                   4              0     1   \n",
       "14998              0.289720                   3              0     1   \n",
       "\n",
       "       promotion_last_5years department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['average_montly_hours'] = mms.fit_transform(data[['average_montly_hours']])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd34251",
   "metadata": {},
   "source": [
    "### Feature Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "536a6247",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(sparse=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7dc81ca1",
   "metadata": {},
   "outputs": [],
   "source": [
    "ohetransform = ohe.fit_transform(data[['department','salary']])\n",
    "columnames = ohe.get_feature_names_out(['department', 'salary']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bd819abb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>department_IT</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_support</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "<p>14999 rows × 13 columns</p>\n",
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      ],
      "text/plain": [
       "       department_IT  department_RandD  department_accounting  department_hr  \\\n",
       "0                0.0               0.0                    0.0            0.0   \n",
       "1                0.0               0.0                    0.0            0.0   \n",
       "2                0.0               0.0                    0.0            0.0   \n",
       "3                0.0               0.0                    0.0            0.0   \n",
       "4                0.0               0.0                    0.0            0.0   \n",
       "...              ...               ...                    ...            ...   \n",
       "14994            0.0               0.0                    0.0            0.0   \n",
       "14995            0.0               0.0                    0.0            0.0   \n",
       "14996            0.0               0.0                    0.0            0.0   \n",
       "14997            0.0               0.0                    0.0            0.0   \n",
       "14998            0.0               0.0                    0.0            0.0   \n",
       "\n",
       "       department_management  department_marketing  department_product_mng  \\\n",
       "0                        0.0                   0.0                     0.0   \n",
       "1                        0.0                   0.0                     0.0   \n",
       "2                        0.0                   0.0                     0.0   \n",
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       "14996                    0.0                   0.0                     0.0   \n",
       "14997                    0.0                   0.0                     0.0   \n",
       "14998                    0.0                   0.0                     0.0   \n",
       "\n",
       "       department_sales  department_support  department_technical  \\\n",
       "0                   1.0                 0.0                   0.0   \n",
       "1                   1.0                 0.0                   0.0   \n",
       "2                   1.0                 0.0                   0.0   \n",
       "3                   1.0                 0.0                   0.0   \n",
       "4                   1.0                 0.0                   0.0   \n",
       "...                 ...                 ...                   ...   \n",
       "14994               0.0                 1.0                   0.0   \n",
       "14995               0.0                 1.0                   0.0   \n",
       "14996               0.0                 1.0                   0.0   \n",
       "14997               0.0                 1.0                   0.0   \n",
       "14998               0.0                 1.0                   0.0   \n",
       "\n",
       "       salary_high  salary_low  salary_medium  \n",
       "0              0.0         1.0            0.0  \n",
       "1              0.0         0.0            1.0  \n",
       "2              0.0         0.0            1.0  \n",
       "3              0.0         1.0            0.0  \n",
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       "14997          0.0         1.0            0.0  \n",
       "14998          0.0         1.0            0.0  \n",
       "\n",
       "[14999 rows x 13 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df = pd.DataFrame(ohetransform, columns=columnames)\n",
    "encoded_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1fc0c316",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data, encoded_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fc93e6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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_IT</th>\n",
       "      <th>department_RandD</th>\n",
       "      <th>...</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_support</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_high</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>0.285047</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.775701</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.822430</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</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>0.593458</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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>0.294393</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>0.257009</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>0.299065</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>0.219626</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>0.859813</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>0.289720</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                  0.285047                   3              0     1   \n",
       "1                  0.775701                   6              0     1   \n",
       "2                  0.822430                   4              0     1   \n",
       "3                  0.593458                   5              0     1   \n",
       "4                  0.294393                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994              0.257009                   3              0     1   \n",
       "14995              0.299065                   3              0     1   \n",
       "14996              0.219626                   3              0     1   \n",
       "14997              0.859813                   4              0     1   \n",
       "14998              0.289720                   3              0     1   \n",
       "\n",
       "       promotion_last_5years  department_IT  department_RandD  ...  \\\n",
       "0                          0            0.0               0.0  ...   \n",
       "1                          0            0.0               0.0  ...   \n",
       "2                          0            0.0               0.0  ...   \n",
       "3                          0            0.0               0.0  ...   \n",
       "4                          0            0.0               0.0  ...   \n",
       "...                      ...            ...               ...  ...   \n",
       "14994                      0            0.0               0.0  ...   \n",
       "14995                      0            0.0               0.0  ...   \n",
       "14996                      0            0.0               0.0  ...   \n",
       "14997                      0            0.0               0.0  ...   \n",
       "14998                      0            0.0               0.0  ...   \n",
       "\n",
       "       department_hr  department_management  department_marketing  \\\n",
       "0                0.0                    0.0                   0.0   \n",
       "1                0.0                    0.0                   0.0   \n",
       "2                0.0                    0.0                   0.0   \n",
       "3                0.0                    0.0                   0.0   \n",
       "4                0.0                    0.0                   0.0   \n",
       "...              ...                    ...                   ...   \n",
       "14994            0.0                    0.0                   0.0   \n",
       "14995            0.0                    0.0                   0.0   \n",
       "14996            0.0                    0.0                   0.0   \n",
       "14997            0.0                    0.0                   0.0   \n",
       "14998            0.0                    0.0                   0.0   \n",
       "\n",
       "       department_product_mng  department_sales  department_support  \\\n",
       "0                         0.0               1.0                 0.0   \n",
       "1                         0.0               1.0                 0.0   \n",
       "2                         0.0               1.0                 0.0   \n",
       "3                         0.0               1.0                 0.0   \n",
       "4                         0.0               1.0                 0.0   \n",
       "...                       ...               ...                 ...   \n",
       "14994                     0.0               0.0                 1.0   \n",
       "14995                     0.0               0.0                 1.0   \n",
       "14996                     0.0               0.0                 1.0   \n",
       "14997                     0.0               0.0                 1.0   \n",
       "14998                     0.0               0.0                 1.0   \n",
       "\n",
       "       department_technical  salary_high  salary_low  salary_medium  \n",
       "0                       0.0          0.0         1.0            0.0  \n",
       "1                       0.0          0.0         0.0            1.0  \n",
       "2                       0.0          0.0         0.0            1.0  \n",
       "3                       0.0          0.0         1.0            0.0  \n",
       "4                       0.0          0.0         1.0            0.0  \n",
       "...                     ...          ...         ...            ...  \n",
       "14994                   0.0          0.0         1.0            0.0  \n",
       "14995                   0.0          0.0         1.0            0.0  \n",
       "14996                   0.0          0.0         1.0            0.0  \n",
       "14997                   0.0          0.0         1.0            0.0  \n",
       "14998                   0.0          0.0         1.0            0.0  \n",
       "\n",
       "[14999 rows x 34 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc7ecf6",
   "metadata": {},
   "source": [
    "### Splitting Train Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "957bddaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['left'],axis=1)\n",
    "y = data['left']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "abf2c91d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection 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": 77,
   "id": "a83612e7",
   "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",
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       "      <th>promotion_last_5years</th>\n",
       "      <th>department_IT</th>\n",
       "      <th>department_RandD</th>\n",
       "      <th>department_accounting</th>\n",
       "      <th>...</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_support</th>\n",
       "      <th>department_technical</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>10438</th>\n",
       "      <td>0.53</td>\n",
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       "    <tr>\n",
       "      <th>9236</th>\n",
       "      <td>0.77</td>\n",
       "      <td>0.53</td>\n",
       "      <td>5</td>\n",
       "      <td>0.747664</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>818</th>\n",
       "      <td>0.89</td>\n",
       "      <td>0.79</td>\n",
       "      <td>3</td>\n",
       "      <td>0.247664</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11503</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.63</td>\n",
       "      <td>3</td>\n",
       "      <td>0.280374</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11721</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.74</td>\n",
       "      <td>4</td>\n",
       "      <td>0.257009</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13123</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.57</td>\n",
       "      <td>3</td>\n",
       "      <td>0.813084</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3264</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.79</td>\n",
       "      <td>4</td>\n",
       "      <td>0.528037</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9845</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5</td>\n",
       "      <td>0.761682</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10799</th>\n",
       "      <td>0.78</td>\n",
       "      <td>0.59</td>\n",
       "      <td>5</td>\n",
       "      <td>0.654206</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2732</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.68</td>\n",
       "      <td>5</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10499 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "10438                0.53             0.52               2   \n",
       "9236                 0.77             0.53               5   \n",
       "818                  0.89             0.79               3   \n",
       "11503                0.64             0.63               3   \n",
       "11721                0.98             0.74               4   \n",
       "...                   ...              ...             ...   \n",
       "13123                0.52             0.57               3   \n",
       "3264                 0.59             0.79               4   \n",
       "9845                 0.69             0.94               5   \n",
       "10799                0.78             0.59               5   \n",
       "2732                 0.56             0.68               5   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  \\\n",
       "10438              0.182243                   4              0   \n",
       "9236               0.747664                   3              0   \n",
       "818                0.247664                   2              0   \n",
       "11503              0.280374                   6              1   \n",
       "11721              0.257009                   3              0   \n",
       "...                     ...                 ...            ...   \n",
       "13123              0.813084                   3              0   \n",
       "3264               0.528037                   2              0   \n",
       "9845               0.761682                   2              0   \n",
       "10799              0.654206                   3              0   \n",
       "2732               0.500000                   2              0   \n",
       "\n",
       "       promotion_last_5years  department_IT  department_RandD  \\\n",
       "10438                      0            0.0               0.0   \n",
       "9236                       0            0.0               0.0   \n",
       "818                        0            0.0               0.0   \n",
       "11503                      0            0.0               0.0   \n",
       "11721                      0            0.0               0.0   \n",
       "...                      ...            ...               ...   \n",
       "13123                      0            0.0               1.0   \n",
       "3264                       0            0.0               0.0   \n",
       "9845                       0            1.0               0.0   \n",
       "10799                      0            0.0               0.0   \n",
       "2732                       0            1.0               0.0   \n",
       "\n",
       "       department_accounting  ...  department_hr  department_management  \\\n",
       "10438                    0.0  ...            0.0                    0.0   \n",
       "9236                     1.0  ...            0.0                    0.0   \n",
       "818                      0.0  ...            0.0                    0.0   \n",
       "11503                    0.0  ...            0.0                    0.0   \n",
       "11721                    0.0  ...            0.0                    0.0   \n",
       "...                      ...  ...            ...                    ...   \n",
       "13123                    0.0  ...            0.0                    0.0   \n",
       "3264                     0.0  ...            0.0                    0.0   \n",
       "9845                     0.0  ...            0.0                    0.0   \n",
       "10799                    0.0  ...            0.0                    0.0   \n",
       "2732                     0.0  ...            0.0                    0.0   \n",
       "\n",
       "       department_marketing  department_product_mng  department_sales  \\\n",
       "10438                   0.0                     0.0               0.0   \n",
       "9236                    0.0                     0.0               0.0   \n",
       "818                     0.0                     0.0               0.0   \n",
       "11503                   0.0                     0.0               0.0   \n",
       "11721                   0.0                     0.0               1.0   \n",
       "...                     ...                     ...               ...   \n",
       "13123                   0.0                     0.0               0.0   \n",
       "3264                    0.0                     0.0               0.0   \n",
       "9845                    0.0                     0.0               0.0   \n",
       "10799                   0.0                     0.0               0.0   \n",
       "2732                    0.0                     0.0               0.0   \n",
       "\n",
       "       department_support  department_technical  salary_high  salary_low  \\\n",
       "10438                 0.0                   1.0          0.0         0.0   \n",
       "9236                  0.0                   0.0          0.0         0.0   \n",
       "818                   1.0                   0.0          0.0         0.0   \n",
       "11503                 1.0                   0.0          0.0         1.0   \n",
       "11721                 0.0                   0.0          0.0         0.0   \n",
       "...                   ...                   ...          ...         ...   \n",
       "13123                 0.0                   0.0          0.0         1.0   \n",
       "3264                  0.0                   1.0          0.0         0.0   \n",
       "9845                  0.0                   0.0          0.0         0.0   \n",
       "10799                 0.0                   1.0          1.0         0.0   \n",
       "2732                  0.0                   0.0          0.0         1.0   \n",
       "\n",
       "       salary_medium  \n",
       "10438            1.0  \n",
       "9236             1.0  \n",
       "818              1.0  \n",
       "11503            0.0  \n",
       "11721            1.0  \n",
       "...              ...  \n",
       "13123            0.0  \n",
       "3264             1.0  \n",
       "9845             1.0  \n",
       "10799            0.0  \n",
       "2732             0.0  \n",
       "\n",
       "[10499 rows x 33 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "86708816",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1670     1\n",
       "13378    0\n",
       "10233    0\n",
       "4719     0\n",
       "7003     0\n",
       "        ..\n",
       "11275    0\n",
       "3828     0\n",
       "4645     0\n",
       "6069     0\n",
       "2506     0\n",
       "Name: left, Length: 4500, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b21f8e",
   "metadata": {},
   "source": [
    "### Implementing models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5b8d67",
   "metadata": {},
   "source": [
    "### LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c8158700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(positive=True)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression(fit_intercept=True,positive=True,copy_X= True)\n",
    "lr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e4bfe271",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error,r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0050933d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1690532768840591"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = lr.predict(X_test)\n",
    "mean_squared_error(y_true= y_test,y_pred=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "ff68fd7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04737011002411062"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true = y_test,y_pred = y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b104bc45",
   "metadata": {},
   "source": [
    "### GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d6d1cfbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=0.01, max_depth=5, subsample=0.8)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "gbr = GradientBoostingRegressor(learning_rate= 0.01, max_depth =  5, n_estimators = 100, subsample = 0.8)\n",
    "gbr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c20fc700",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04360662551982308"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_pred = gbr.predict(X_test)\n",
    "mean_squared_error(y_true=y_test,y_pred=gb_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "b1f75ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.754272879906027"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true = y_test,y_pred = gb_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1686daf",
   "metadata": {},
   "source": [
    "### DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "cd61071e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=5, min_samples_leaf=4, min_samples_split=10)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt = DecisionTreeRegressor(max_depth =5, min_samples_leaf = 4,min_samples_split = 10)\n",
    "dt.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c5155172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025832959789858327"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_pred = dt.predict(X_test)\n",
    "mean_squared_error(y_test,dt_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "ab6b59cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.854429029144215"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true = y_test,y_pred=dt_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e009e5",
   "metadata": {},
   "source": [
    "### RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "35952436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)\n",
    "rf_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b3e605cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012043288888888887"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_pred = rf_regressor.predict(X_test)\n",
    "mean_squared_error(y_test, rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "06283f3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.932135021688823"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_true = y_test,y_pred=rf_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dd5ceaa",
   "metadata": {},
   "source": [
    "### From the above 4 models RandomForestRegressor has best accuracy score of 0.93"
   ]
  }
 ],
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