Logistic Regression - predicting employee retention based on salary, department etc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80390bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "17c23664",
   "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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Desktop/HR_comma_sep.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0225d0e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='salary'>"
      ]
     },
     "execution_count": 7,
     "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') # Exploring and understating the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "57dcee47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Department'>"
      ]
     },
     "execution_count": 72,
     "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.Department,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "083b7834",
   "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>salary</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  salary  left\n",
       "0                0.38                   157     low     1\n",
       "1                0.80                   262  medium     1\n",
       "2                0.11                   272  medium     1\n",
       "3                0.72                   223     low     1\n",
       "4                0.37                   159     low     1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newdf = df[['satisfaction_level','average_montly_hours','salary','left']] # dropping out the columns that dont have affect on dependent variables\n",
    "newdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "331eda05",
   "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>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   high  low  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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies = pd.get_dummies(x['salary']) # #returning the dummy columns\n",
    "dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75c38d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>salary</th>\n",
       "      <th>left</th>\n",
       "      <th>high</th>\n",
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       "      <th>medium</th>\n",
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       "      <td>272</td>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  salary  left  high  low  medium\n",
       "0                0.38                   157     low     1     0    1       0\n",
       "1                0.80                   262  medium     1     0    0       1\n",
       "2                0.11                   272  medium     1     0    0       1\n",
       "3                0.72                   223     low     1     0    1       0\n",
       "4                0.37                   159     low     1     0    1       0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged = pd.concat([newdf,dummies],axis='columns') #merging the dummy columns with the main dataframe\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ffe03497",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>left</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  left  high  low  medium\n",
       "0                0.38                   157     1     0    1       0\n",
       "1                0.80                   262     1     0    0       1\n",
       "2                0.11                   272     1     0    0       1\n",
       "3                0.72                   223     1     0    1       0\n",
       "4                0.37                   159     1     0    1       0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = merged.drop(['salary'], axis='columns') #droppng the dummied column\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c8185f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\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>average_montly_hours</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
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       "      <td>262</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  high  low  medium\n",
       "0                0.38                   157     0    1       0\n",
       "1                0.80                   262     0    0       1\n",
       "2                0.11                   272     0    0       1\n",
       "3                0.72                   223     0    1       0\n",
       "4                0.37                   159     0    1       0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = final.drop(['left'],axis='columns') #assigning the independent variables\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f409d608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = final.left  # assigning the dependent variable\n",
    "y.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c5ff20ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split                            #importing the testrainspilt module\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=0.70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7a21bf15",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "46fb6378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e048cf32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7784444444444445"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)  #model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "be1385e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[0.35,350,0,1,0]])  #Prediction by entering the independent datas to know the dependent outcome"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3 (ipykernel)",
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