logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "<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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23</td>\n",
       "      <td>0</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\n",
       "5    56                 1\n",
       "6    55                 0\n",
       "7    60                 1\n",
       "8    62                 1\n",
       "9    61                 1\n",
       "10   18                 0\n",
       "11   28                 0\n",
       "12   27                 0\n",
       "13   29                 0\n",
       "14   49                 1\n",
       "15   55                 1\n",
       "16   25                 1\n",
       "17   58                 1\n",
       "18   19                 0\n",
       "19   18                 0\n",
       "20   21                 0\n",
       "21   26                 0\n",
       "22   40                 1\n",
       "23   45                 1\n",
       "24   50                 1\n",
       "25   54                 1\n",
       "26   23                 0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insurance = pd.read_csv('logistic_regression\\insurance_data.csv')\n",
    "insurance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18376640ad0>"
      ]
     },
     "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(insurance.age,insurance.bought_insurance,marker='+',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = insurance[['age']]\n",
    "y = insurance.bought_insurance\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test = train_test_split(insurance[['age']],insurance.bought_insurance,train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20    0\n",
       "0     0\n",
       "25    1\n",
       "14    1\n",
       "11    0\n",
       "1     0\n",
       "Name: bought_insurance, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test,y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise\n",
    "\n",
    "Download employee retention dataset from here: https://www.kaggle.com/giripujar/hr-analytics.\n",
    "\n",
    "1. Now do some exploratory data analysis to figure out which variables have direct and clear impact on employee retention (i.e. whether they leave the company or continue to work)\n",
    "\n",
    "2. Plot bar charts showing impact of employee salaries on retention\n",
    "\n",
    "3. Plot bar charts showing corelation between department and employee retention\n",
    "\n",
    "4. Now build logistic regression model using variables that were narrowed down in step 1\n",
    "\n",
    "5. Measure the accuracy of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
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       "      <th>salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>14995</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>14997</th>\n",
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       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
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       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years Department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data = pd.read_csv('logistic_regression\\HR_comma_sep.csv')\n",
    "hr_data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration and Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3571, 10)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data[hr_data.left==1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11428, 10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_data[hr_data.left==0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years  Department  salary  \n",
       "0                          0           7       1  \n",
       "1                          0           7       2  \n",
       "2                          0           7       2  \n",
       "3                          0           7       1  \n",
       "4                          0           7       1  \n",
       "...                      ...         ...     ...  \n",
       "14994                      0           8       1  \n",
       "14995                      0           8       1  \n",
       "14996                      0           8       1  \n",
       "14997                      0           8       1  \n",
       "14998                      0           8       1  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "hr_edata = hr_data.copy()\n",
    "hr_edata['Department'] = le.fit_transform(hr_data['Department'])\n",
    "hr_edata['salary'] = le.fit_transform(hr_data['salary'])\n",
    "hr_edata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(hr_edata.corr(), cmap =\"YlGnBu\", linewidths = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Original Labels  Encoded Labels\n",
      "0             low               1\n",
      "1          medium               2\n",
      "2            high               0\n"
     ]
    }
   ],
   "source": [
    "legend_1 = pd.DataFrame({'Original Labels': hr_data['salary'], 'Encoded Labels': hr_edata['salary']}).drop_duplicates().reset_index(drop=True)\n",
    "print(legend_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Original Labels  Encoded Labels\n",
      "0           sales               7\n",
      "1      accounting               2\n",
      "2              hr               3\n",
      "3       technical               9\n",
      "4         support               8\n",
      "5      management               4\n",
      "6              IT               0\n",
      "7     product_mng               6\n",
      "8       marketing               5\n",
      "9           RandD               1\n"
     ]
    }
   ],
   "source": [
    "legend_2 = pd.DataFrame({'Original Labels': hr_data['Department'], 'Encoded Labels': hr_edata['Department']}).drop_duplicates().reset_index(drop=True)\n",
    "print(legend_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
       "      <th>Department</th>\n",
       "      <th>salary</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",
       "      <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",
       "      <td>5.819041</td>\n",
       "      <td>1.347742</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",
       "      <td>6.035284</td>\n",
       "      <td>1.345842</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  Department    salary  \n",
       "left                                               \n",
       "0                  0.026251    5.819041  1.347742  \n",
       "1                  0.005321    6.035284  1.345842  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hr_edata.groupby('left').mean()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From above table we can draw following conclusions,\n",
    "\n",
    "**1. Satisfaction Level**: Satisfaction level seems to be relatively low (0.44) in employees leaving the firm vs the retained ones (0.66)\n",
    "\n",
    "**2. Average Monthly Hours**: Average monthly hours are higher in employees leaving the firm (199 vs 207)\n",
    "\n",
    "**3. Promotion Last 5 Years**: Employees who are given promotion are likely to be retained at firm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='salary'>"
      ]
     },
     "execution_count": 21,
     "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(hr_data.salary,hr_data.left).plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graph above it is quite clear that the attrition rate amongst the employees with a low salary is high when compared to the ones with a higher salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Department'>"
      ]
     },
     "execution_count": 22,
     "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(hr_data.Department,hr_data.left).plot(kind='bar')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can say that the department of an employee doesn't impact much on the attrition rate."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the data analysis so far we can conclude that we will use following variables as independant variables in our model\n",
    "\n",
    "**1. Satisfaction Level**\n",
    "\n",
    "**2. Average Monthly Hours**\n",
    "\n",
    "**3. Promotion Last 5 Years**\n",
    "\n",
    "**4. Salary**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.80e-01, 1.57e+02, 0.00e+00, 1.00e+00],\n",
       "       [8.00e-01, 2.62e+02, 0.00e+00, 2.00e+00],\n",
       "       [1.10e-01, 2.72e+02, 0.00e+00, 2.00e+00],\n",
       "       ...,\n",
       "       [3.70e-01, 1.43e+02, 0.00e+00, 1.00e+00],\n",
       "       [1.10e-01, 2.80e+02, 0.00e+00, 1.00e+00],\n",
       "       [3.70e-01, 1.58e+02, 0.00e+00, 1.00e+00]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = hr_edata[['satisfaction_level', 'average_montly_hours', 'promotion_last_5years', 'salary']].values\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.00e+00, 0.00e+00, 3.80e-01, 1.57e+02, 0.00e+00],\n",
       "       [0.00e+00, 1.00e+00, 8.00e-01, 2.62e+02, 0.00e+00],\n",
       "       [0.00e+00, 1.00e+00, 1.10e-01, 2.72e+02, 0.00e+00],\n",
       "       ...,\n",
       "       [1.00e+00, 0.00e+00, 3.70e-01, 1.43e+02, 0.00e+00],\n",
       "       [1.00e+00, 0.00e+00, 1.10e-01, 2.80e+02, 0.00e+00],\n",
       "       [1.00e+00, 0.00e+00, 3.70e-01, 1.58e+02, 0.00e+00]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "column_transformer = ColumnTransformer([('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n",
    "X_encoded = column_transformer.fit_transform(x)\n",
    "X_encoded = np.delete(X_encoded, 0, axis=1)\n",
    "X_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = hr_edata.left.values\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(X_encoded,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_model = LogisticRegression()\n",
    "retention_model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7736666666666666"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retention_model.score(x_test,y_test)"
   ]
  }
 ],
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