Logistic Regression (Binary Classification).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7cfe804d",
   "metadata": {},
   "source": [
    "## Binary Classification by Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b5a658b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14fcba21",
   "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>age</th>\n",
       "      <th>bought_insurance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  bought_insurance\n",
       "0   22                 0\n",
       "1   25                 0\n",
       "2   47                 1\n",
       "3   52                 0\n",
       "4   46                 1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets\\insurance.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "085a50bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x238a6f2ead0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x= df.age, y = df.bought_insurance, marker='*',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5254b926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8e6e10d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts\n",
    "X_train, X_test, y_train, y_test = tts(df[['age']], df.bought_insurance, test_size=.1, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51352427",
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "3    52\n",
       "23   45\n",
       "10   18\n",
       "20   21\n",
       "21   26"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8d73b82e",
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "17   58\n",
       "14   49\n",
       "18   19"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3c8a50b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model_1 = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "14e75288",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2150c052",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model_1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d54a2a2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c39b3f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a168fa79",
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age\n",
       "17   58\n",
       "14   49\n",
       "18   19"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3998592b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.12628081]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c683f399",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.97348109])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e625ca3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08700173, 0.91299827],\n",
       "       [0.22894487, 0.77105513],\n",
       "       [0.92917899, 0.07082101]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34b0fcdb",
   "metadata": {},
   "source": [
    "# Apply with Bigger Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ffd34975",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Datasets\\HR_comma_sep.csv') #Importing the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d4307577",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14999, 10)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07ce4042",
   "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', 'Department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # Checking the features the dataset has"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b69d490",
   "metadata": {},
   "source": [
    "###### The dataset has 14999 instances and 10 features where 'Left' is our Target feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a917e570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11428\n",
       "1     3571\n",
       "Name: left, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.left.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3e6f147a",
   "metadata": {},
   "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   Department             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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7fbf008b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction_level       0\n",
       "last_evaluation          0\n",
       "number_project           0\n",
       "average_montly_hours     0\n",
       "time_spend_company       0\n",
       "Work_accident            0\n",
       "left                     0\n",
       "promotion_last_5years    0\n",
       "Department               0\n",
       "salary                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # Checking if our dataset contains any missing information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e49c38a8",
   "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>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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's have a look who are lefting the company\n",
    "df.groupby('left').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "748ae6bf",
   "metadata": {},
   "source": [
    "#### Some factors to Note from the above statistics:\n",
    "- The less the satisfaction_level employees are likely to leave the company\n",
    "- Imployees are not getting promotion could have an impact also for leaving the company\n",
    "- Working on more projects, spending more time but not getting promoted could be the reason also leaving company\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf5d5cd",
   "metadata": {},
   "source": [
    "### Some visualization for better understanding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ff4787b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='salary', ylabel='satisfaction_level'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "sns.barplot(data=df, x='salary', y='satisfaction_level', hue='left', capsize=0.2, ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1030d066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='salary'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.salary,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7cbab6b",
   "metadata": {},
   "source": [
    "#### Department wise retention rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "13852f77",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Department'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.Department,df.left).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a670ff2",
   "metadata": {},
   "source": [
    "##### Which deparment is the happiest demartment?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e302bb09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Department', ylabel='satisfaction_level'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.barplot(data=df, x='Department', y='satisfaction_level',  ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e610ff8b",
   "metadata": {},
   "source": [
    "##### Promotion with respect to the number of projects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b81ca376",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='number_project'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df.number_project,df.promotion_last_5years).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc14ad1",
   "metadata": {},
   "source": [
    "##### Satisfaction level with respect to accident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c85ee457",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Work_accident', ylabel='satisfaction_level'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "sns.barplot(data=df, x='Work_accident', y='satisfaction_level',  ci=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a9aa5be",
   "metadata": {},
   "source": [
    "#### Checking the percetages of employeed left with respect to different categories of Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "efb62b93",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "low_salary_left = df.salary[(df['salary'] == 'low') & (df['left'] == 1)].value_counts().values\n",
    "medium_salary_left = df.salary[(df['salary'] == 'medium') & (df['left'] == 1)].value_counts().values\n",
    "high_salary_left = df.salary[(df['salary'] == 'high') & (df['left'] == 1)].value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ff53e42a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Total_left = df.left[df['left']==1].value_counts().values # Total umber of people already left the company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe2f33c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2172], dtype=int64), array([1317], dtype=int64))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_salary_left, medium_salary_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b5641b50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of Low Salary Employeed who left the company: 60.82 %\n",
      "Percentage of Medium Salary Employeed who left the company: 36.88 %\n",
      "Percentage of High Salary Employeed who left the company: 2.3 %\n"
     ]
    }
   ],
   "source": [
    "low_salary_left_percentage = np.round(low_salary_left[0]/Total_left*100, 2).tolist()\n",
    "medium_salary_left_percentage = np.round(medium_salary_left[0]/Total_left*100, 2).tolist()\n",
    "high_salary_left_percentage = np.round(high_salary_left[0]/Total_left*100, 2).tolist()\n",
    "\n",
    "\n",
    "print(\"Percentage of Low Salary Employeed who left the company:\", *low_salary_left_percentage, \"%\")\n",
    "print(\"Percentage of Medium Salary Employeed who left the company:\", *medium_salary_left_percentage, \"%\")\n",
    "print(\"Percentage of High Salary Employeed who left the company:\", *high_salary_left_percentage, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0748bfe",
   "metadata": {},
   "source": [
    "##### So, we can clearly see that the low salary employeed have left the company most."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16bae66a",
   "metadata": {},
   "source": [
    "### Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "203c5831",
   "metadata": {
    "scrolled": true
   },
   "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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "92db2f9a",
   "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>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   left\n",
       "0     1\n",
       "1     1"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Separating the Target Feature from the dataset\n",
    "X = df.drop('left', axis = 1)\n",
    "y = df[['left']]\n",
    "y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ca48c569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       satisfaction_level  last_evaluation  number_project  \\\n",
      "satisfaction_level               1.000000         0.105021       -0.142970   \n",
      "last_evaluation                  0.105021         1.000000        0.349333   \n",
      "number_project                  -0.142970         0.349333        1.000000   \n",
      "average_montly_hours            -0.020048         0.339742        0.417211   \n",
      "time_spend_company              -0.100866         0.131591        0.196786   \n",
      "Work_accident                    0.058697        -0.007104       -0.004741   \n",
      "left                            -0.388375         0.006567        0.023787   \n",
      "promotion_last_5years            0.025605        -0.008684       -0.006064   \n",
      "\n",
      "                       average_montly_hours  time_spend_company  \\\n",
      "satisfaction_level                -0.020048           -0.100866   \n",
      "last_evaluation                    0.339742            0.131591   \n",
      "number_project                     0.417211            0.196786   \n",
      "average_montly_hours               1.000000            0.127755   \n",
      "time_spend_company                 0.127755            1.000000   \n",
      "Work_accident                     -0.010143            0.002120   \n",
      "left                               0.071287            0.144822   \n",
      "promotion_last_5years             -0.003544            0.067433   \n",
      "\n",
      "                       Work_accident      left  promotion_last_5years  \n",
      "satisfaction_level          0.058697 -0.388375               0.025605  \n",
      "last_evaluation            -0.007104  0.006567              -0.008684  \n",
      "number_project             -0.004741  0.023787              -0.006064  \n",
      "average_montly_hours       -0.010143  0.071287              -0.003544  \n",
      "time_spend_company          0.002120  0.144822               0.067433  \n",
      "Work_accident               1.000000 -0.154622               0.039245  \n",
      "left                       -0.154622  1.000000              -0.061788  \n",
      "promotion_last_5years       0.039245 -0.061788               1.000000  \n"
     ]
    }
   ],
   "source": [
    "# Calculate the correlation matrix\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "# Print the correlation matrix\n",
    "print(correlation_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f5d2a8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a heatmap using seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", linewidths=0.5)\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "884d4ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
       "       'average_montly_hours', 'time_spend_company', 'Work_accident',\n",
       "       'promotion_last_5years', 'Department', 'salary'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "51f8935c",
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By observing the correlation between the features we can choose the following features from the dataset\n",
    "\n",
    "selected_features = ['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']\n",
    "X = X[selected_features]\n",
    "X.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d25fdbf",
   "metadata": {},
   "source": [
    "### Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1efda208",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform one-hot encoding\n",
    "one_hot_encoded = pd.get_dummies(X['salary'], prefix=\"salary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bc54d300",
   "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",
       "      <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>157</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>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0            0           1              0  \n",
       "1            0           0              1  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate the original DataFrame (X) with the one-hot encoded columns\n",
    "X = pd.concat([X, one_hot_encoded], axis=1)\n",
    "X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e94c2a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the original Salary feature column which contains categorical values\n",
    "X.drop('salary', axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "29a7e85c",
   "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_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>157</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</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>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>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  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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ba36db",
   "metadata": {},
   "source": [
    "### Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "554840d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.31868132, 0.28504673, 0.        , 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.78021978, 0.77570093, 0.        , 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       [0.02197802, 0.82242991, 0.        , 0.        , 0.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.30769231, 0.21962617, 0.        , 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.02197802, 0.85981308, 0.        , 0.        , 1.        ,\n",
       "        0.        ],\n",
       "       [0.30769231, 0.28971963, 0.        , 0.        , 1.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Create a MinMaxScaler object\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Perform min-max scaling\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "fc1960bb",
   "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_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>0.318681</td>\n",
       "      <td>0.285047</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>0.780220</td>\n",
       "      <td>0.775701</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>0.021978</td>\n",
       "      <td>0.822430</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>0.692308</td>\n",
       "      <td>0.593458</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>0.307692</td>\n",
       "      <td>0.294393</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",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0            0.318681              0.285047                    0.0   \n",
       "1            0.780220              0.775701                    0.0   \n",
       "2            0.021978              0.822430                    0.0   \n",
       "3            0.692308              0.593458                    0.0   \n",
       "4            0.307692              0.294393                    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",
       "4          0.0         1.0            0.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = X.columns\n",
    "# Convert the array to a DataFrame\n",
    "X_scaled = pd.DataFrame(X_scaled, columns = col_names)\n",
    "X_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b009a3",
   "metadata": {},
   "source": [
    "### Split dataset for training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "376f638e",
   "metadata": {},
   "outputs": [],
   "source": [
    "XX_train, XX_test, yy_train, yy_test = tts (X_scaled, y, test_size = .25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7842c80e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14480</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1934</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       left\n",
       "14480     1\n",
       "1934      1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "13a8ba67",
   "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_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14480</th>\n",
       "      <td>0.318681</td>\n",
       "      <td>0.200935</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>1934</th>\n",
       "      <td>0.758242</td>\n",
       "      <td>0.602804</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "14480            0.318681              0.200935                    0.0   \n",
       "1934             0.758242              0.602804                    0.0   \n",
       "\n",
       "       salary_high  salary_low  salary_medium  \n",
       "14480          0.0         0.0            1.0  \n",
       "1934           0.0         0.0            1.0  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX_train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "3b4ed803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2 = LogisticRegression()\n",
    "model_2.fit(XX_train, yy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ecc4d540",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predict the target variable for the test data\n",
    "yy_pred = model_2.predict(XX_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41ca0f7e",
   "metadata": {},
   "source": [
    "#### Accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "dc01454b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 76.32 %\n"
     ]
    }
   ],
   "source": [
    "# Calculate the accuracy score\n",
    "accuracy = accuracy_score(yy_test, yy_pred)\n",
    "\n",
    "print(\"Accuracy:\", accuracy*100, \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9d7f27",
   "metadata": {},
   "source": [
    "### Let's try with Scaling the data with Standardscaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2b8c3b9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "s_scaler = StandardScaler()\n",
    "S_X_scaler = s_scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f8cf37ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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_high</th>\n",
       "      <th>salary_low</th>\n",
       "      <th>salary_medium</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>-0.936495</td>\n",
       "      <td>-0.882040</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>-0.299809</td>\n",
       "      <td>1.024775</td>\n",
       "      <td>-0.868132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.752814</td>\n",
       "      <td>1.220423</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>-0.299809</td>\n",
       "      <td>-0.975824</td>\n",
       "      <td>1.151898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.022479</td>\n",
       "      <td>1.420657</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>-0.299809</td>\n",
       "      <td>-0.975824</td>\n",
       "      <td>1.151898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.431041</td>\n",
       "      <td>0.439508</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>-0.299809</td>\n",
       "      <td>1.024775</td>\n",
       "      <td>-0.868132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.976716</td>\n",
       "      <td>-0.841993</td>\n",
       "      <td>-0.147412</td>\n",
       "      <td>-0.299809</td>\n",
       "      <td>1.024775</td>\n",
       "      <td>-0.868132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0           -0.936495             -0.882040              -0.147412   \n",
       "1            0.752814              1.220423              -0.147412   \n",
       "2           -2.022479              1.420657              -0.147412   \n",
       "3            0.431041              0.439508              -0.147412   \n",
       "4           -0.976716             -0.841993              -0.147412   \n",
       "\n",
       "   salary_high  salary_low  salary_medium  \n",
       "0    -0.299809    1.024775      -0.868132  \n",
       "1    -0.299809   -0.975824       1.151898  \n",
       "2    -0.299809   -0.975824       1.151898  \n",
       "3    -0.299809    1.024775      -0.868132  \n",
       "4    -0.299809    1.024775      -0.868132  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = X.columns\n",
    "# Convert the array to a DataFrame\n",
    "S_X_scaler = pd.DataFrame(S_X_scaler, columns = col_names)\n",
    "S_X_scaler.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ae4aa661",
   "metadata": {},
   "outputs": [],
   "source": [
    "XX_train, XX_test, yy_train, yy_test = tts (S_X_scaler, y, test_size = .25, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8636043a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 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-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 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-3 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-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 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-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 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-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" 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-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" 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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3 = LogisticRegression()\n",
    "model_3.fit(XX_train, yy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cdfeeb73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predict the target variable for the test data\n",
    "yy_pred = model_3.predict(XX_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5e77e050",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 76.27 %\n"
     ]
    }
   ],
   "source": [
    "# Calculate the accuracy score\n",
    "accuracy = accuracy_score(yy_test, yy_pred)\n",
    "\n",
    "print(\"Accuracy:\", round(accuracy*100,2), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e2ef8c9",
   "metadata": {},
   "source": [
    "### Extra: Ridge Regression\n",
    "**Note:** It is a regression algorithm used for solving regression problems, specifically for linear regression tasks.\n",
    "\n",
    "Ridge Regression is an extension of linear regression that incorporates a regularization term to prevent overfitting. It is primarily used for continuous target variables, where the goal is to predict a numerical value. The algorithm estimates the relationship between the features (independent variables) and the target variable (dependent variable) by minimizing the sum of squared differences between the predicted and actual values, while also adding a penalty term to the loss function based on the magnitudes of the regression coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2031c6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "ridge_reg = Ridge(alpha=1, solver='cholesky')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8da31c6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge(alpha=1, solver=&#x27;cholesky&#x27;)</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-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge(alpha=1, solver=&#x27;cholesky&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "Ridge(alpha=1, solver='cholesky')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_reg.fit(XX_train, yy_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "b83d3b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "da6797fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 0.15228428900724886\n",
      "R-squared: 0.16319662958608505\n"
     ]
    }
   ],
   "source": [
    "yy_pred = ridge_reg.predict(XX_test)\n",
    "\n",
    "\n",
    "# Calculate the Mean Squared Error (MSE)\n",
    "mse = mean_squared_error(yy_test, yy_pred)\n",
    "print(\"Mean Squared Error:\", mse)\n",
    "\n",
    "# Calculate the R-squared value\n",
    "r2 = r2_score(yy_test, yy_pred)\n",
    "print(\"R-squared:\", r2)"
   ]
  }
 ],
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