Logistic Regression_HR_Analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "baa7f6c4",
   "metadata": {},
   "source": [
    "### Exploratory data analysis to figure out which variables have direct and clear impact on employee retention i.e. whether they leave the company or continue to work."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7414d9a",
   "metadata": {},
   "source": [
    "- Plot bar charts showing impact of employee salaries on retention\n",
    "\n",
    "- Plot bar charts showing corelation between department and employee retention\n",
    "\n",
    "- Build logistic regression model using variables that were narrowed down in step 1 Measure the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bb565c1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69b7ab94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  last_evaluation  number_project  average_montly_hours  \\\n",
       "0                0.38             0.53               2                   157   \n",
       "1                0.80             0.86               5                   262   \n",
       "2                0.11             0.88               7                   272   \n",
       "3                0.72             0.87               5                   223   \n",
       "4                0.37             0.52               2                   159   \n",
       "\n",
       "   time_spend_company  Work_accident  left  promotion_last_5years Department  \\\n",
       "0                   3              0     1                      0      sales   \n",
       "1                   6              0     1                      0      sales   \n",
       "2                   4              0     1                      0      sales   \n",
       "3                   5              0     1                      0      sales   \n",
       "4                   3              0     1                      0      sales   \n",
       "\n",
       "   salary  \n",
       "0     low  \n",
       "1  medium  \n",
       "2  medium  \n",
       "3     low  \n",
       "4     low  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"HR.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2067180a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c4e7c30",
   "metadata": {},
   "source": [
    "# EDA: no of employees left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca88aa16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_nos = df[df.left ==1]\n",
    "left_nos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604977b0",
   "metadata": {},
   "source": [
    "# EDA: no of employees retained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "464c5287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retained = df[df.left ==0]\n",
    "retained.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb45b41",
   "metadata": {},
   "source": [
    "### Finding average for left column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "93c775bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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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>promotion_last_5years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.666810</td>\n",
       "      <td>0.715473</td>\n",
       "      <td>3.786664</td>\n",
       "      <td>199.060203</td>\n",
       "      <td>3.380032</td>\n",
       "      <td>0.175009</td>\n",
       "      <td>0.026251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.440098</td>\n",
       "      <td>0.718113</td>\n",
       "      <td>3.855503</td>\n",
       "      <td>207.419210</td>\n",
       "      <td>3.876505</td>\n",
       "      <td>0.047326</td>\n",
       "      <td>0.005321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      satisfaction_level  last_evaluation  number_project  \\\n",
       "left                                                        \n",
       "0               0.666810         0.715473        3.786664   \n",
       "1               0.440098         0.718113        3.855503   \n",
       "\n",
       "      average_montly_hours  time_spend_company  Work_accident  \\\n",
       "left                                                            \n",
       "0               199.060203            3.380032       0.175009   \n",
       "1               207.419210            3.876505       0.047326   \n",
       "\n",
       "      promotion_last_5years  \n",
       "left                         \n",
       "0                  0.026251  \n",
       "1                  0.005321  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce738a7",
   "metadata": {},
   "source": [
    "**Conclusion**  <br>\n",
    "\n",
    "**Satisfaction Level:** Satisfaction level seems to be relatively low (0.44) in employees leaving the firm vs the retained                         ones (0.66)\n",
    "\n",
    "**Average Monthly Hours:** Average monthly hours are higher in employees leaving the firm\n",
    "\n",
    "**Promotion Last 5 Years:** Employees who are given promotion are likely to be retained at firm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b113eca7",
   "metadata": {},
   "source": [
    "## Plot bar charts showing impact of employee salaries on retention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed11fee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c058e560",
   "metadata": {},
   "source": [
    "## Plot bar charts showing corelation between department and employee retention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f78c43",
   "metadata": {},
   "source": [
    "**We use following variables as independant variables(X) in our model**\n",
    "\n",
    "- Satisfaction Level\n",
    "- Average Monthly Hours\n",
    "- Promotion Last 5 Years\n",
    "- Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63d3a9d5",
   "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>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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>151</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  salary\n",
       "0                    0.38                   157                      0     low\n",
       "1                    0.80                   262                      0  medium\n",
       "2                    0.11                   272                      0  medium\n",
       "3                    0.72                   223                      0     low\n",
       "4                    0.37                   159                      0     low\n",
       "...                   ...                   ...                    ...     ...\n",
       "14994                0.40                   151                      0     low\n",
       "14995                0.37                   160                      0     low\n",
       "14996                0.37                   143                      0     low\n",
       "14997                0.11                   280                      0     low\n",
       "14998                0.37                   158                      0     low\n",
       "\n",
       "[14999 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df[['satisfaction_level','average_montly_hours','promotion_last_5years','salary']]\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "020bc754",
   "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>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",
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       "      <th>1</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       salary_high  salary_low  salary_medium\n",
       "0                0           1              0\n",
       "1                0           0              1\n",
       "2                0           0              1\n",
       "3                0           1              0\n",
       "4                0           1              0\n",
       "...            ...         ...            ...\n",
       "14994            0           1              0\n",
       "14995            0           1              0\n",
       "14996            0           1              0\n",
       "14997            0           1              0\n",
       "14998            0           1              0\n",
       "\n",
       "[14999 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy = pd.get_dummies(new_df.salary, prefix=\"salary\")\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8119de3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "    <tr>\n",
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       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
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       "      <th>14994</th>\n",
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       "      <td>0</td>\n",
       "      <td>low</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                    0.38                   157                      0   \n",
       "1                    0.80                   262                      0   \n",
       "2                    0.11                   272                      0   \n",
       "3                    0.72                   223                      0   \n",
       "4                    0.37                   159                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "14994                0.40                   151                      0   \n",
       "14995                0.37                   160                      0   \n",
       "14996                0.37                   143                      0   \n",
       "14997                0.11                   280                      0   \n",
       "14998                0.37                   158                      0   \n",
       "\n",
       "       salary  salary_high  salary_low  salary_medium  \n",
       "0         low            0           1              0  \n",
       "1      medium            0           0              1  \n",
       "2      medium            0           0              1  \n",
       "3         low            0           1              0  \n",
       "4         low            0           1              0  \n",
       "...       ...          ...         ...            ...  \n",
       "14994     low            0           1              0  \n",
       "14995     low            0           1              0  \n",
       "14996     low            0           1              0  \n",
       "14997     low            0           1              0  \n",
       "14998     low            0           1              0  \n",
       "\n",
       "[14999 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummy = pd.concat([new_df,dummy],axis='columns')\n",
    "df_with_dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c61ee7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "      <td>0.72</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                0.38                   157                      0   \n",
       "1                0.80                   262                      0   \n",
       "2                0.11                   272                      0   \n",
       "3                0.72                   223                      0   \n",
       "4                0.37                   159                      0   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0            0           1              0  \n",
       "1            0           0              1  \n",
       "2            0           0              1  \n",
       "3            0           1              0  \n",
       "4            0           1              0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_with_dummy.drop('salary',axis='columns',inplace=True)\n",
    "df_with_dummy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0adbca56",
   "metadata": {},
   "source": [
    "# Training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef924ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                    0.38                   157                      0   \n",
       "1                    0.80                   262                      0   \n",
       "2                    0.11                   272                      0   \n",
       "3                    0.72                   223                      0   \n",
       "4                    0.37                   159                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "14994                0.40                   151                      0   \n",
       "14995                0.37                   160                      0   \n",
       "14996                0.37                   143                      0   \n",
       "14997                0.11                   280                      0   \n",
       "14998                0.37                   158                      0   \n",
       "\n",
       "       salary_high  salary_low  salary_medium  \n",
       "0                0           1              0  \n",
       "1                0           0              1  \n",
       "2                0           0              1  \n",
       "3                0           1              0  \n",
       "4                0           1              0  \n",
       "...            ...         ...            ...  \n",
       "14994            0           1              0  \n",
       "14995            0           1              0  \n",
       "14996            0           1              0  \n",
       "14997            0           1              0  \n",
       "14998            0           1              0  \n",
       "\n",
       "[14999 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_with_dummy\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7b498d42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "14994    1\n",
       "14995    1\n",
       "14996    1\n",
       "14997    1\n",
       "14998    1\n",
       "Name: left, Length: 14999, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.left\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61347e97",
   "metadata": {},
   "source": [
    "## Spliting the Training and Testing data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ebe1d3c",
   "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,train_size=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610c4676",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e2995763",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "df6b1847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       float64\n",
       "average_montly_hours       int64\n",
       "promotion_last_5years      int64\n",
       "salary_high                uint8\n",
       "salary_low                 uint8\n",
       "salary_medium              uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25ceb3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e5d7fe06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54d0a9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7713333333333333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  }
 ],
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