{
"cells": [
{
"cell_type": "markdown",
"id": "a1af8f38",
"metadata": {
"id": "a1af8f38"
},
"source": [
"## LVC 3 - Case Study\n",
"\n",
"### Context: \n",
"A local Hospital that has thousands of employees spread out across the state. The company believes in hiring the best talent available and retaining them for as long as possible. A huge amount of resources is spent on retaining existing employees through various initiatives. The Head of People Operations wants to bring down the cost of retaining employees. For this, he proposes limiting the incentives to only those employees who are at risk of attrition. As a recently hired Data Scientist in the People Operations Department, you have been asked to identify patterns in characteristics of employees who leave the organization. Also, you have to use this information to predict if an employee is at risk of attrition. This information will be used to target them with incentives.\n",
"\n",
"### Objective : \n",
"\n",
"* To identify the different factors that drive attrition\n",
"* To make a model to predict if an employee will attrite or not\n",
"\n",
"\n",
"### Dataset :\n",
"The data contains demographic details, work-related metrics and attrition flag.\n",
"\n",
"* **EmployeeNumber** - Employee Identifier\n",
"* **Attrition** - Did the employee attrite?\n",
"* **Age** - Age of the employee\n",
"* **BusinessTravel** - Travel commitments for the job\n",
"* **DailyRate** - Data description not available**\n",
"* **Department** - Employee Department\n",
"* **DistanceFromHome** - Distance from work to home (in km)\n",
"* **Education** - 1-Below College, 2-College, 3-Bachelor, 4-Master,5-Doctor\n",
"* **EducationField** - Field of Education\n",
"* **EnvironmentSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High\n",
"* **Gender** - Employee's gender\n",
"* **HourlyRate** - Data description not available**\n",
"* **JobInvolvement** - 1-Low, 2-Medium, 3-High, 4-Very High\n",
"* **JobLevel** - Level of job (1 to 5)\n",
"* **JobRole** - Job Roles\n",
"* **JobSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High\n",
"* **MaritalStatus** - Marital Status\n",
"* **MonthlyIncome** - Monthly Salary\n",
"* **MonthlyRate** - Data description not available**\n",
"* **NumCompaniesWorked** - Number of companies worked at\n",
"* **Over18** - Over 18 years of age?\n",
"* **OverTime** - Overtime?\n",
"* **PercentSalaryHike** - The percentage increase in salary last year\n",
"* **PerformanceRating** - 1-Low, 2-Good, 3-Excellent, 4-Outstanding\n",
"* **RelationshipSatisfaction** - 1-Low, 2-Medium, 3-High, 4-Very High\n",
"* **StandardHours** - Standard Hours\n",
"* **StockOptionLevel** - Stock Option Level\n",
"* **TotalWorkingYears** - Total years worked\n",
"* **TrainingTimesLastYear** - Number of training attended last year\n",
"* **WorkLifeBalance** - 1-Low, 2-Good, 3-Excellent, 4-Outstanding\n",
"* **YearsAtCompany** - Years at Company\n",
"* **YearsInCurrentRole** - Years in the current role\n",
"* **YearsSinceLastPromotion** - Years since the last promotion\n",
"* **YearsWithCurrManager** - Years with the current manager\n",
"\n",
"** In the real world, you will not find definitions for some of your variables. It is a part of the analysis to figure out what they might mean. "
]
},
{
"cell_type": "markdown",
"id": "d6700f10",
"metadata": {
"id": "d6700f10"
},
"source": [
"### Import the necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d60f33c8",
"metadata": {
"id": "d60f33c8"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"#to scale the data using z-score \n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#algorithms to use\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"#Metrics to evaluate the model\n",
"from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve\n",
"\n",
"#for tuning the model\n",
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"#to ignore warnings\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "markdown",
"id": "91a960cf",
"metadata": {
"id": "91a960cf"
},
"source": [
"### Read the dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "ff29bb95",
"metadata": {
"id": "ff29bb95"
},
"outputs": [],
"source": [
"#reading the dataset\n",
"df = pd.read_csv('HR_Employee_Attrition_Dataset.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6cc2e7f0",
"metadata": {
"id": "6cc2e7f0",
"outputId": "6d9d4fe1-754b-461c-a7b7-ae862ad32bcf"
},
"outputs": [
{
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Attrition
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Age
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BusinessTravel
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DailyRate
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Department
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Education
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...
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WorkLifeBalance
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Travel_Frequently
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Research & Development
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Life Sciences
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Research & Development
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5 rows × 34 columns
\n",
"
"
],
"text/plain": [
" EmployeeNumber Attrition Age BusinessTravel DailyRate \\\n",
"0 1 Yes 41 Travel_Rarely 1102 \n",
"1 2 No 49 Travel_Frequently 279 \n",
"2 3 Yes 37 Travel_Rarely 1373 \n",
"3 4 No 33 Travel_Frequently 1392 \n",
"4 5 No 27 Travel_Rarely 591 \n",
"\n",
" Department DistanceFromHome Education EducationField \\\n",
"0 Sales 1 2 Life Sciences \n",
"1 Research & Development 8 1 Life Sciences \n",
"2 Research & Development 2 2 Other \n",
"3 Research & Development 3 4 Life Sciences \n",
"4 Research & Development 2 1 Medical \n",
"\n",
" EnvironmentSatisfaction ... RelationshipSatisfaction StandardHours \\\n",
"0 2 ... 1 80 \n",
"1 3 ... 4 80 \n",
"2 4 ... 2 80 \n",
"3 4 ... 3 80 \n",
"4 1 ... 4 80 \n",
"\n",
" StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance \\\n",
"0 0 8 0 1 \n",
"1 1 10 3 3 \n",
"2 0 7 3 3 \n",
"3 0 8 3 3 \n",
"4 1 6 3 3 \n",
"\n",
" YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion \\\n",
"0 6 4 0 \n",
"1 10 7 1 \n",
"2 0 0 0 \n",
"3 8 7 3 \n",
"4 2 2 2 \n",
"\n",
" YearsWithCurrManager \n",
"0 5 \n",
"1 7 \n",
"2 0 \n",
"3 0 \n",
"4 2 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "f367a777",
"metadata": {
"id": "f367a777"
},
"source": [
"### Printing the info"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e366b601",
"metadata": {
"id": "e366b601",
"outputId": "7aeb37b3-a85d-49a0-ad18-3306a7fd84cb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"RangeIndex: 2940 entries, 0 to 2939\n",
"Data columns (total 34 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 EmployeeNumber 2940 non-null int64 \n",
" 1 Attrition 2940 non-null object\n",
" 2 Age 2940 non-null int64 \n",
" 3 BusinessTravel 2940 non-null object\n",
" 4 DailyRate 2940 non-null int64 \n",
" 5 Department 2940 non-null object\n",
" 6 DistanceFromHome 2940 non-null int64 \n",
" 7 Education 2940 non-null int64 \n",
" 8 EducationField 2940 non-null object\n",
" 9 EnvironmentSatisfaction 2940 non-null int64 \n",
" 10 Gender 2940 non-null object\n",
" 11 HourlyRate 2940 non-null int64 \n",
" 12 JobInvolvement 2940 non-null int64 \n",
" 13 JobLevel 2940 non-null int64 \n",
" 14 JobRole 2940 non-null object\n",
" 15 JobSatisfaction 2940 non-null int64 \n",
" 16 MaritalStatus 2940 non-null object\n",
" 17 MonthlyIncome 2940 non-null int64 \n",
" 18 MonthlyRate 2940 non-null int64 \n",
" 19 NumCompaniesWorked 2940 non-null int64 \n",
" 20 Over18 2940 non-null object\n",
" 21 OverTime 2940 non-null object\n",
" 22 PercentSalaryHike 2940 non-null int64 \n",
" 23 PerformanceRating 2940 non-null int64 \n",
" 24 RelationshipSatisfaction 2940 non-null int64 \n",
" 25 StandardHours 2940 non-null int64 \n",
" 26 StockOptionLevel 2940 non-null int64 \n",
" 27 TotalWorkingYears 2940 non-null int64 \n",
" 28 TrainingTimesLastYear 2940 non-null int64 \n",
" 29 WorkLifeBalance 2940 non-null int64 \n",
" 30 YearsAtCompany 2940 non-null int64 \n",
" 31 YearsInCurrentRole 2940 non-null int64 \n",
" 32 YearsSinceLastPromotion 2940 non-null int64 \n",
" 33 YearsWithCurrManager 2940 non-null int64 \n",
"dtypes: int64(25), object(9)\n",
"memory usage: 781.1+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "markdown",
"id": "f1f5de53",
"metadata": {
"id": "f1f5de53"
},
"source": [
"**Observation:**\n",
"- There are 2940 observations and 33 columns.\n",
"- All the column have 2940 non-null values i.e. there are no missing values in the data."
]
},
{
"cell_type": "markdown",
"id": "09b353cb",
"metadata": {
"id": "09b353cb"
},
"source": [
"**Let's check the unique values in each column** "
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9b8f1776",
"metadata": {
"id": "9b8f1776",
"outputId": "568e3320-e9ca-4c72-e267-b6773f1461c1"
},
"outputs": [
{
"data": {
"text/plain": [
"EmployeeNumber 2940\n",
"Attrition 2\n",
"Age 43\n",
"BusinessTravel 3\n",
"DailyRate 886\n",
"Department 3\n",
"DistanceFromHome 29\n",
"Education 5\n",
"EducationField 6\n",
"EnvironmentSatisfaction 4\n",
"Gender 2\n",
"HourlyRate 71\n",
"JobInvolvement 4\n",
"JobLevel 5\n",
"JobRole 9\n",
"JobSatisfaction 4\n",
"MaritalStatus 3\n",
"MonthlyIncome 1349\n",
"MonthlyRate 1427\n",
"NumCompaniesWorked 10\n",
"Over18 1\n",
"OverTime 2\n",
"PercentSalaryHike 15\n",
"PerformanceRating 2\n",
"RelationshipSatisfaction 4\n",
"StandardHours 1\n",
"StockOptionLevel 4\n",
"TotalWorkingYears 40\n",
"TrainingTimesLastYear 7\n",
"WorkLifeBalance 4\n",
"YearsAtCompany 37\n",
"YearsInCurrentRole 19\n",
"YearsSinceLastPromotion 16\n",
"YearsWithCurrManager 18\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#checking unique values in each column\n",
"df.nunique()"
]
},
{
"cell_type": "markdown",
"id": "8182513f",
"metadata": {
"id": "8182513f"
},
"source": [
"**Observation:**\n",
"- Employee number is an identifier which is unique for each employee and we can drop this column as it would not add any value to our analysis.\n",
"- Over18 and StandardHours have only 1 unique value. These column will not add any value to our model hence we can drop them.\n",
"- On the basis of number of unique values in each column and the data description, we can identify the continuous and categorical columns in the data.\n",
"\n",
"Let's drop the columns mentioned above and define lists for numerical and categorical columns to apply explore them separately."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2b73820f",
"metadata": {
"id": "2b73820f"
},
"outputs": [],
"source": [
"#dropping the columns \n",
"df=df.drop(['EmployeeNumber','Over18','StandardHours'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "188e22ec",
"metadata": {
"id": "188e22ec"
},
"outputs": [],
"source": [
"#Creating numerical columns\n",
"num_cols=['DailyRate','Age','DistanceFromHome','MonthlyIncome','MonthlyRate','PercentSalaryHike','TotalWorkingYears',\n",
" 'YearsAtCompany','NumCompaniesWorked','HourlyRate',\n",
" 'YearsInCurrentRole','YearsSinceLastPromotion','YearsWithCurrManager','TrainingTimesLastYear']\n",
"\n",
"#Creating categorical variables \n",
"cat_cols= ['Attrition','OverTime','BusinessTravel', 'Department','Education', 'EducationField','JobSatisfaction','EnvironmentSatisfaction','WorkLifeBalance',\n",
" 'StockOptionLevel','Gender', 'PerformanceRating', 'JobInvolvement','JobLevel', 'JobRole', 'MaritalStatus','RelationshipSatisfaction']"
]
},
{
"cell_type": "markdown",
"id": "7c1a436f",
"metadata": {
"id": "7c1a436f"
},
"source": [
"### Let's start with univariate analysis of numerical columns"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fb41a488",
"metadata": {
"id": "fb41a488",
"outputId": "32820b4b-fad7-48d1-a0d7-b045a8cf3bbc",
"scrolled": false
},
"outputs": [
{
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std
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75%
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403.440447
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102.0
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465.0
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MonthlyRate
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7116.575021
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8045.0
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20462.0
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2940.0
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11.0
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12.0
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14.0
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25.0
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7.779458
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0.0
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6.0
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10.0
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15.0
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40.0
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YearsAtCompany
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2940.0
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7.008163
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5.0
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9.0
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40.0
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NumCompaniesWorked
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2940.0
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2.693197
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2.497584
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1.0
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4.0
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9.0
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HourlyRate
\n",
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2940.0
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65.891156
\n",
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20.325969
\n",
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30.0
\n",
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48.0
\n",
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66.0
\n",
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84.0
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100.0
\n",
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\n",
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\n",
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YearsInCurrentRole
\n",
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2940.0
\n",
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4.229252
\n",
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3.622521
\n",
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0.0
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2.0
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3.0
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7.0
\n",
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18.0
\n",
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YearsSinceLastPromotion
\n",
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2940.0
\n",
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2.187755
\n",
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3.221882
\n",
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0.0
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0.0
\n",
"
1.0
\n",
"
3.0
\n",
"
15.0
\n",
"
\n",
"
\n",
"
YearsWithCurrManager
\n",
"
2940.0
\n",
"
4.123129
\n",
"
3.567529
\n",
"
0.0
\n",
"
2.0
\n",
"
3.0
\n",
"
7.0
\n",
"
17.0
\n",
"
\n",
"
\n",
"
TrainingTimesLastYear
\n",
"
2940.0
\n",
"
2.799320
\n",
"
1.289051
\n",
"
0.0
\n",
"
2.0
\n",
"
3.0
\n",
"
3.0
\n",
"
6.0
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" count mean std min 25% \\\n",
"DailyRate 2940.0 802.485714 403.440447 102.0 465.0 \n",
"Age 2940.0 36.923810 9.133819 18.0 30.0 \n",
"DistanceFromHome 2940.0 9.192517 8.105485 1.0 2.0 \n",
"MonthlyIncome 2940.0 6502.931293 4707.155770 1009.0 2911.0 \n",
"MonthlyRate 2940.0 14313.103401 7116.575021 2094.0 8045.0 \n",
"PercentSalaryHike 2940.0 15.209524 3.659315 11.0 12.0 \n",
"TotalWorkingYears 2940.0 11.279592 7.779458 0.0 6.0 \n",
"YearsAtCompany 2940.0 7.008163 6.125483 0.0 3.0 \n",
"NumCompaniesWorked 2940.0 2.693197 2.497584 0.0 1.0 \n",
"HourlyRate 2940.0 65.891156 20.325969 30.0 48.0 \n",
"YearsInCurrentRole 2940.0 4.229252 3.622521 0.0 2.0 \n",
"YearsSinceLastPromotion 2940.0 2.187755 3.221882 0.0 0.0 \n",
"YearsWithCurrManager 2940.0 4.123129 3.567529 0.0 2.0 \n",
"TrainingTimesLastYear 2940.0 2.799320 1.289051 0.0 2.0 \n",
"\n",
" 50% 75% max \n",
"DailyRate 802.0 1157.0 1499.0 \n",
"Age 36.0 43.0 60.0 \n",
"DistanceFromHome 7.0 14.0 29.0 \n",
"MonthlyIncome 4919.0 8380.0 19999.0 \n",
"MonthlyRate 14235.5 20462.0 26999.0 \n",
"PercentSalaryHike 14.0 18.0 25.0 \n",
"TotalWorkingYears 10.0 15.0 40.0 \n",
"YearsAtCompany 5.0 9.0 40.0 \n",
"NumCompaniesWorked 2.0 4.0 9.0 \n",
"HourlyRate 66.0 84.0 100.0 \n",
"YearsInCurrentRole 3.0 7.0 18.0 \n",
"YearsSinceLastPromotion 1.0 3.0 15.0 \n",
"YearsWithCurrManager 3.0 7.0 17.0 \n",
"TrainingTimesLastYear 3.0 3.0 6.0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Checking summary statistics\n",
"df[num_cols].describe().T"
]
},
{
"cell_type": "markdown",
"id": "3fa3d39e",
"metadata": {
"id": "3fa3d39e"
},
"source": [
"- **Average employee age is around 37 years**. It has a high range, from 18 years to 60, indicating good age diversity in the organization.\n",
"- **At least 50% of the employees live within a 7 km radius** from the organization. However there are some extreme values, seeing as the maximum value is 29 km.\n",
"- **The average monthly income of an employee is USD 6500.** It has a high range of values from 1K-20K, which is to be expected for any organization's income distribution. There is a big difference between the 3rd quartile value (around USD 8400) and the maximum value (nearly USD 20000), showing that the **company's highest earners have a disproportionately large income** in comparison to the rest of the employees. Again, this is fairly common in most organizations.\n",
"- **Average salary hike of an employee is around 15%.** At least 50% of employees got a salary hike 14% or less, with the maximum salary hike being 25%.\n",
"- Average number of years an employee is associated with the company is 7. \n",
"- **On average, the number of years since an employee got a promotion is 2.18**. The majority of employees have been promoted since the last year."
]
},
{
"cell_type": "markdown",
"id": "3283a6be",
"metadata": {
"id": "3283a6be"
},
"source": [
"Let's explore these variables in some more depth by observing their distributions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6b1e0479",
"metadata": {
"id": "6b1e0479",
"outputId": "ff5338bb-e8e4-4b73-f87b-3ef683ce0059"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#creating histograms\n",
"df[num_cols].hist(figsize=(14,14))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ec26cfc8",
"metadata": {
"id": "ec26cfc8"
},
"source": [
"**Observations:**\n",
"\n",
"- **The age distribution is close to a normal distribution** with the majority of employees between the ages of 25 and 50.\n",
"\n",
"- **The percentage salary hike is skewed to the right**, which means employees are mostly getting lower percentage salary increases.\n",
"\n",
"- **MonthlyIncome and TotalWorkingYears are skewed to the right**, indicating that the majority of workers are in entry / mid-level positions in the organization.\n",
"\n",
"- **DistanceFromHome also has a right skewed distribution**, meaning most employees live close to work but there are a few that live further away.\n",
"\n",
"- **On average, an employee has worked at 2.5 companies.** Most employees have worked at only 1 company.\n",
"\n",
"- **The YearsAtCompany variable distribution shows a good proportion of workers with 10+ years**, indicating a significant number of loyal employees at the organization. \n",
"\n",
"- **The YearsInCurrentRole distribution has three peaks at 0, 2, and 7.** There are a few employees that have even stayed in the same role for 15 years and more.\n",
"\n",
"- **The YearsSinceLastPromotion variable distribution indicates that some employees have not received a promotion in 10-15 years and are still working in the organization.** These employees are assumed to be high work-experience employees in upper-management roles, such as co-founders, C-suite employees and the like.\n",
"\n",
"- The distributions of DailyRate, HourlyRate and MonthlyRate appear to be uniform and do not provide much information. It could be that daily rate refers to the income earned per extra day worked while hourly rate could refer to the same concept applied for extra hours worked per day. Since these rates tend to be broadly similiar for multiple employees in the same department, that explains the uniform distribution they show. "
]
},
{
"cell_type": "markdown",
"id": "6e605786",
"metadata": {
"id": "6e605786"
},
"source": [
"### Univariate analysis for categorical variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1902184e",
"metadata": {
"id": "1902184e",
"outputId": "94897560-79b9-47f1-d61f-71d290e3c7f2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No 0.838776\n",
"Yes 0.161224\n",
"Name: Attrition, dtype: float64\n",
"****************************************\n",
"No 0.717007\n",
"Yes 0.282993\n",
"Name: OverTime, dtype: float64\n",
"****************************************\n",
"Travel_Rarely 0.709524\n",
"Travel_Frequently 0.188435\n",
"Non-Travel 0.102041\n",
"Name: BusinessTravel, dtype: float64\n",
"****************************************\n",
"Research & Development 0.653741\n",
"Sales 0.303401\n",
"Human Resources 0.042857\n",
"Name: Department, dtype: float64\n",
"****************************************\n",
"3 0.389116\n",
"4 0.270748\n",
"2 0.191837\n",
"1 0.115646\n",
"5 0.032653\n",
"Name: Education, dtype: float64\n",
"****************************************\n",
"Life Sciences 0.412245\n",
"Medical 0.315646\n",
"Marketing 0.108163\n",
"Technical Degree 0.089796\n",
"Other 0.055782\n",
"Human Resources 0.018367\n",
"Name: EducationField, dtype: float64\n",
"****************************************\n",
"4 0.312245\n",
"3 0.300680\n",
"1 0.196599\n",
"2 0.190476\n",
"Name: JobSatisfaction, dtype: float64\n",
"****************************************\n",
"3 0.308163\n",
"4 0.303401\n",
"2 0.195238\n",
"1 0.193197\n",
"Name: EnvironmentSatisfaction, dtype: float64\n",
"****************************************\n",
"3 0.607483\n",
"2 0.234014\n",
"4 0.104082\n",
"1 0.054422\n",
"Name: WorkLifeBalance, dtype: float64\n",
"****************************************\n",
"0 0.429252\n",
"1 0.405442\n",
"2 0.107483\n",
"3 0.057823\n",
"Name: StockOptionLevel, dtype: float64\n",
"****************************************\n",
"Male 0.6\n",
"Female 0.4\n",
"Name: Gender, dtype: float64\n",
"****************************************\n",
"3 0.846259\n",
"4 0.153741\n",
"Name: PerformanceRating, dtype: float64\n",
"****************************************\n",
"3 0.590476\n",
"2 0.255102\n",
"4 0.097959\n",
"1 0.056463\n",
"Name: JobInvolvement, dtype: float64\n",
"****************************************\n",
"1 0.369388\n",
"2 0.363265\n",
"3 0.148299\n",
"4 0.072109\n",
"5 0.046939\n",
"Name: JobLevel, dtype: float64\n",
"****************************************\n",
"Sales Executive 0.221769\n",
"Research Scientist 0.198639\n",
"Laboratory Technician 0.176190\n",
"Manufacturing Director 0.098639\n",
"Healthcare Representative 0.089116\n",
"Manager 0.069388\n",
"Sales Representative 0.056463\n",
"Research Director 0.054422\n",
"Human Resources 0.035374\n",
"Name: JobRole, dtype: float64\n",
"****************************************\n",
"Married 0.457823\n",
"Single 0.319728\n",
"Divorced 0.222449\n",
"Name: MaritalStatus, dtype: float64\n",
"****************************************\n",
"3 0.312245\n",
"4 0.293878\n",
"2 0.206122\n",
"1 0.187755\n",
"Name: RelationshipSatisfaction, dtype: float64\n",
"****************************************\n"
]
}
],
"source": [
"#Printing the % sub categories of each category\n",
"for i in cat_cols:\n",
" print(df[i].value_counts(normalize=True))\n",
" print('*'*40)"
]
},
{
"cell_type": "markdown",
"id": "c093aae2",
"metadata": {
"id": "c093aae2"
},
"source": [
"**Observations:**\n",
"\n",
"- **The employee attrition rate is 16%.**\n",
"- **Around 28% of the employees are working overtime.** This number appears to be on the higher side, and might indicate a stressed employee work-life.\n",
"- 71% of the employees have traveled rarely, while around 19% have to travel frequently.\n",
"- Around 73% of the employees come from an educational background in the Life Sciences and Medical fields. \n",
"- Over 65% of employees work in the Research & Development department of the organization.\n",
"- **Nearly 40% of the employees have low (1) or medium-low (2) job satisfaction** and environment satisfaction in the organization, indicating that the morale of the company appears to be somewhat low.\n",
"- **Over 30% of the employees show low (1) to medium-low (2) job involvement.** \n",
"- Over 80% of the employees either have none or very less stock options. \n",
"- **In terms of performance ratings, none of the employees have rated lower than 3 (excellent).** About 85% of employees have a performance rating equal to 3 (excellent), while the remaining have a rating of 4 (outstanding). This could either mean that the majority of employees are top performers, or the more likely scenerio is that the organization could be highly lenient with its performance appraisal process."
]
},
{
"cell_type": "markdown",
"id": "bdb2961d",
"metadata": {
"id": "bdb2961d"
},
"source": [
"### Bivariate and Multivariate analysis"
]
},
{
"cell_type": "markdown",
"id": "643dd47d",
"metadata": {
"id": "643dd47d"
},
"source": [
"**We have analyzed different categorical and numerical variables.** \n",
"\n",
"**Let's now check how does attrition rate is related with other categorical variables**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "796c2b07",
"metadata": {
"id": "796c2b07",
"outputId": "1ace5a61-5f02-4c1f-87a4-f50e1688354a",
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"for i in cat_cols:\n",
" if i!='Attrition':\n",
" (pd.crosstab(df[i],df['Attrition'],normalize='index')*100).plot(kind='bar',figsize=(8,4),stacked=True)\n",
" plt.ylabel('Percentage Attrition %')"
]
},
{
"cell_type": "markdown",
"id": "3f91face",
"metadata": {
"id": "3f91face"
},
"source": [
"**Observations:**\n",
" \n",
"- **Employees working overtime have more than a 30% chance of attrition**, \n",
"which is very high compared to the 10% chance of attrition for employees who do not work extra hours.\n",
"- As seen earlier, the majority of employees work for the R&D department. The chance of attrition there is ~15%\n",
"- **Employees working as sales representatives have an attrition rate of around 40%** while HRs and Technicians have an attrition rate of around 25%. The sales and HR departments have higher attrition rates in comparison to an academic department like Research & Development, an observation that makes intuitive sense keeping in mind the differences in those job profiles. The high-pressure and incentive-based nature of Sales and Marketing roles may be contributing to their higher attrition rates.\n",
"- **The lower the employee's job involvement, the higher their attrition chances appear to be, with 1-rated JobInvolvement employees attriting at 35%.** The reason for this could be that employees with lower job involvement might feel left out or less valued and have already started to explore new options, leading to a higher attrition rate.\n",
"- **Employees at a lower job level also attrite more,** with 1-rated JobLevel employees showing a nearly 25% chance of attrition. These may be young employees who tend to explore more options in the initial stages of their careers. \n",
"- **A low work-life balance rating clearly leads employees to attrite**; 30% of those in the 1-rated category show attrition."
]
},
{
"cell_type": "markdown",
"id": "93b053a6",
"metadata": {
"id": "93b053a6"
},
"source": [
"**Let's check the relationship between attrition and Numerical variables**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a6dbb256",
"metadata": {
"id": "a6dbb256",
"outputId": "b92ad475-2f23-43f7-ac41-e61702a4fc91"
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
DailyRate
\n",
"
Age
\n",
"
DistanceFromHome
\n",
"
MonthlyIncome
\n",
"
MonthlyRate
\n",
"
PercentSalaryHike
\n",
"
TotalWorkingYears
\n",
"
YearsAtCompany
\n",
"
NumCompaniesWorked
\n",
"
HourlyRate
\n",
"
YearsInCurrentRole
\n",
"
YearsSinceLastPromotion
\n",
"
YearsWithCurrManager
\n",
"
TrainingTimesLastYear
\n",
"
\n",
"
\n",
"
Attrition
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
"
\n",
"
No
\n",
"
812.504461
\n",
"
37.561233
\n",
"
8.915653
\n",
"
6832.739659
\n",
"
14265.779400
\n",
"
15.231144
\n",
"
11.862936
\n",
"
7.369019
\n",
"
2.645580
\n",
"
65.952149
\n",
"
4.484185
\n",
"
2.234388
\n",
"
4.367397
\n",
"
2.832928
\n",
"
\n",
"
\n",
"
Yes
\n",
"
750.362869
\n",
"
33.607595
\n",
"
10.632911
\n",
"
4787.092827
\n",
"
14559.308017
\n",
"
15.097046
\n",
"
8.244726
\n",
"
5.130802
\n",
"
2.940928
\n",
"
65.573840
\n",
"
2.902954
\n",
"
1.945148
\n",
"
2.852321
\n",
"
2.624473
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" DailyRate Age DistanceFromHome MonthlyIncome \\\n",
"Attrition \n",
"No 812.504461 37.561233 8.915653 6832.739659 \n",
"Yes 750.362869 33.607595 10.632911 4787.092827 \n",
"\n",
" MonthlyRate PercentSalaryHike TotalWorkingYears YearsAtCompany \\\n",
"Attrition \n",
"No 14265.779400 15.231144 11.862936 7.369019 \n",
"Yes 14559.308017 15.097046 8.244726 5.130802 \n",
"\n",
" NumCompaniesWorked HourlyRate YearsInCurrentRole \\\n",
"Attrition \n",
"No 2.645580 65.952149 4.484185 \n",
"Yes 2.940928 65.573840 2.902954 \n",
"\n",
" YearsSinceLastPromotion YearsWithCurrManager \\\n",
"Attrition \n",
"No 2.234388 4.367397 \n",
"Yes 1.945148 2.852321 \n",
"\n",
" TrainingTimesLastYear \n",
"Attrition \n",
"No 2.832928 \n",
"Yes 2.624473 "
]
},
"execution_count": 12,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#Mean of numerical varibles grouped by attrition\n",
"df.groupby(['Attrition'])[num_cols].mean()"
]
},
{
"cell_type": "markdown",
"id": "d286e106",
"metadata": {
"id": "d286e106"
},
"source": [
"**Observations:**\n",
"- **Employees leaving the company have a nearly 30% lower average income and 30% lesser work experience than those who are not.** These could be the employees looking to explore new options and/or increase their salary with a company switch. \n",
"- **Employees showing attrition also tend to live 16% further from the office than those who are not**. The longer commute to and from work could mean they have to spend more time/money every day, amd this could be leading to job dissatisfaction and wanting to leave the organization."
]
},
{
"cell_type": "markdown",
"id": "df253009",
"metadata": {
"id": "df253009"
},
"source": [
"**We have found out what kind of employees are leaving the company more.**\n",
"\n",
"### Let's check the relationship between different numerical variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5da1e8ed",
"metadata": {
"id": "5da1e8ed",
"outputId": "ff2bf703-25d9-42e2-cfd2-c005e8eff08b"
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {
"tags": []
},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#plotting the correation between numerical variables\n",
"plt.figure(figsize=(15,8))\n",
"sns.heatmap(df[num_cols].corr(),annot=True, fmt='0.2f', cmap='YlGnBu')"
]
},
{
"cell_type": "markdown",
"id": "231d85ef",
"metadata": {
"id": "231d85ef"
},
"source": [
"**Observations:**\n",
"\n",
"- **Total work experience, monthly income, years at company and years with current manager are highly correlated with each other and with employee age** which is easy to understand as these variables show an increase with age for most employees. \n",
"- Years at company and years in current role are correlated with years since last promotion which means that the company is not giving promotions at the right time."
]
},
{
"cell_type": "markdown",
"id": "62b1c31c",
"metadata": {
"id": "62b1c31c"
},
"source": [
"**Now that we have explored our data. Let's build the model**"
]
},
{
"cell_type": "markdown",
"id": "c87d8b8c",
"metadata": {
"id": "c87d8b8c"
},
"source": [
"## Model Building - Approach\n",
"1. Prepare data for modeling\n",
"2. Partition the data into train and test set.\n",
"3. Build model on the train data.\n",
"4. Tune the model if required.\n",
"5. Test the data on test set."
]
},
{
"cell_type": "markdown",
"id": "9854fafc",
"metadata": {
"id": "9854fafc"
},
"source": [
"### Preparing data for modeling"
]
},
{
"cell_type": "markdown",
"id": "c73bfd10",
"metadata": {
"id": "c73bfd10"
},
"source": [
"**Creating dummy variables for categorical Variables**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10038cc3",
"metadata": {
"id": "10038cc3"
},
"outputs": [],
"source": [
"#creating list of dummy columns\n",
"to_get_dummies_for = ['BusinessTravel', 'Department','Education', 'EducationField','EnvironmentSatisfaction', 'Gender', 'JobInvolvement','JobLevel', 'JobRole', 'MaritalStatus' ]\n",
"\n",
"#creating dummy variables\n",
"df = pd.get_dummies(data = df, columns= to_get_dummies_for, drop_first= True) \n",
"\n",
"#mapping overtime and attrition\n",
"dict_OverTime = {'Yes': 1, 'No':0}\n",
"dict_attrition = {'Yes': 1, 'No': 0}\n",
"\n",
"\n",
"df['OverTime'] = df.OverTime.map(dict_OverTime)\n",
"df['Attrition'] = df.Attrition.map(dict_attrition)"
]
},
{
"cell_type": "markdown",
"id": "8cf5d26f",
"metadata": {
"id": "8cf5d26f"
},
"source": [
"**Separating the independent variables (X) and the dependent variable (Y)**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "76d1cc91",
"metadata": {
"id": "76d1cc91"
},
"outputs": [],
"source": [
"#Separating target variable and other variables\n",
"Y= df.Attrition\n",
"X= df.drop(columns = ['Attrition'])"
]
},
{
"cell_type": "markdown",
"id": "c389ff82",
"metadata": {
"id": "c389ff82"
},
"source": [
"### Scaling the data\n",
"\n",
"The independent variables in this dataset have different scales. When features have differing scales from each other, there is a chance that a higher weightage will be given to features which have a higher magnitude, and they will dominate over other features whose magnitude changes may be smaller but whose percentage changes may be just as significant or even larger. This will impact the performance of our machine learning algorithm, and we do not want our algorithm to be biased towards one feature. \n",
"\n",
"The solution to this issue is **Feature Scaling**, i.e. scaling the dataset so as to give every transformed variable a comparable scale.\n",
"\n",
"In this problem, we will use the **Standard Scaler** method, which centers and scales the dataset using the Z-Score.\n",
"\n",
"It standardizes features by subtracting the mean and scaling it to have unit variance.\n",
"\n",
"The standard score of a sample x is calculated as:\n",
"\n",
"**z = (x - u) / s**\n",
"\n",
"where **u** is the mean of the training samples (zero) and **s** is the standard deviation of the training samples."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e99f9cb",
"metadata": {
"id": "0e99f9cb"
},
"outputs": [],
"source": [
"#Scaling the data\n",
"sc=StandardScaler()\n",
"X_scaled=sc.fit_transform(X)\n",
"X_scaled=pd.DataFrame(X_scaled, columns=X.columns)"
]
},
{
"cell_type": "markdown",
"id": "0d508522",
"metadata": {
"id": "0d508522"
},
"source": [
"### Splitting the data into 70% train and 30% test set"
]
},
{
"cell_type": "markdown",
"id": "d5a7a233",
"metadata": {
"id": "d5a7a233"
},
"source": [
"Some classification problems can exhibit a large imbalance in the distribution of the target classes: for instance there could be several times more negative samples than positive samples. In such cases it is recommended to use the **stratified sampling** technique to ensure that relative class frequencies are approximately preserved in each train and validation fold."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "96bfa6a3",
"metadata": {
"id": "96bfa6a3"
},
"outputs": [],
"source": [
"#splitting the data\n",
"x_train,x_test,y_train,y_test=train_test_split(X_scaled,Y,test_size=0.3,random_state=1,stratify=Y)"
]
},
{
"cell_type": "markdown",
"id": "77645a49",
"metadata": {
"id": "77645a49"
},
"source": [
"### Model evaluation criterion\n",
"\n",
"#### The model can make two types of wrong predictions:\n",
"1. Predicting an employee will attrite when the employee doesn't attrite\n",
"2. Predicting an employee will not attrite and the employee actually attrites\n",
"\n",
"#### Which case is more important? \n",
"* **Predicting that the employee will not attrite but the employee attrites** i.e. losing out on a valuable employee or asset. This would be considered a major miss for any employee attrition predictor, and is hence the more important case of wrong predictions.\n",
"\n",
"#### How to reduce this loss i.e the need to reduce False Negatives?\n",
"* **The company would want the Recall to be maximized**, the greater the Recall, the higher the chances of minimizing false negatives. Hence, the focus should be on increasing Recall (minimizing the false negatives) or in other words identifying the true positives (i.e. Class 1) very well, so that the company can provide incentives to control attrition rate especially for top-performers. This would help in optimizing the overall project cost towards retaining the best talent."
]
},
{
"cell_type": "markdown",
"id": "67963d69",
"metadata": {
"id": "67963d69"
},
"source": [
"Also, let's create a function to calculate and print the classification report and confusion matrix so that we don't have to rewrite the same code repeatedly for each model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7b305008",
"metadata": {
"id": "7b305008"
},
"outputs": [],
"source": [
"#creating metric function \n",
"def metrics_score(actual, predicted):\n",
" print(classification_report(actual, predicted))\n",
" cm = confusion_matrix(actual, predicted)\n",
" plt.figure(figsize=(8,5))\n",
" sns.heatmap(cm, annot=True, fmt='.2f', xticklabels=['Not Attrite', 'Attrite'], yticklabels=['Not Attrite', 'Attrite'])\n",
" plt.ylabel('Actual')\n",
" plt.xlabel('Predicted')\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"id": "38857a84",
"metadata": {
"id": "38857a84"
},
"source": [
"#### Building the model\n",
"\n",
"We will be building 4 different models:\n",
"- **Linear Discriminant Analysis (LDA)**\n",
"- **Quadratic Discriminant Analysis (QDA)**\n",
"- **Logistic Regression**\n",
"- **K Nearest Neighbors (KNN)**"
]
},
{
"cell_type": "markdown",
"id": "20330dad",
"metadata": {
"id": "20330dad"
},
"source": [
"### Linear Discriminant Analysis"
]
},
{
"cell_type": "markdown",
"id": "7b4ba67c",
"metadata": {
"id": "7b4ba67c"
},
"source": [
"Linear discriminant analysis (LDA) is generally used to classify patterns between two classes; however, it can be extended to classify multiple patterns. LDA assumes that all classes are linearly separable and according to this, multiple linear discrimination functions representing several hyperplanes in the feature space are created to distinguish between the classes. If there are two classes, then the LDA draws one hyperplane and projects the data onto this hyperplane in such a way as to maximize the separation of the two categories. This hyperplane is created according to two criteria considered simultaneously:\n",
"\n",
"- Maximizing the distance between the means of two classes;\n",
"- Minimizing the variation between each category."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2658d2f",
"metadata": {
"id": "e2658d2f",
"outputId": "6ac6e132-e619-4e64-84b3-8ba9e4727c76",
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"LinearDiscriminantAnalysis()"
]
},
"execution_count": 19,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#fitting lda model\n",
"lda = LinearDiscriminantAnalysis()\n",
"lda.fit(x_train, y_train)"
]
},
{
"cell_type": "markdown",
"id": "68727572",
"metadata": {
"id": "68727572"
},
"source": [
"**Checking Model Performance**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bda7f9e4",
"metadata": {
"id": "bda7f9e4",
"outputId": "fc0e9e98-c87a-46f7-bb4d-0238671ece6e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.91 0.98 0.94 1726\n",
" 1 0.80 0.52 0.63 332\n",
"\n",
" accuracy 0.90 2058\n",
" macro avg 0.86 0.75 0.78 2058\n",
"weighted avg 0.89 0.90 0.89 2058\n",
"\n"
]
},
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#checking model performace of lda\n",
"y_pred_train_lda = lda.predict(x_train)\n",
"metrics_score(y_train, y_pred_train_lda)"
]
},
{
"cell_type": "markdown",
"id": "a871f9cb",
"metadata": {
"id": "a871f9cb"
},
"source": [
"- The reported average includes the macro average which averages the unweighted mean per label, and the weighted average i.e. averaging the support-weighted mean per label.\n",
"- In classification, the class of interest is considered the positive class. Here, the class of interest is 1 i.e. identifying the employees at risk of attrition.\n",
"\n",
"**Reading the confusion matrix (clockwise):**\n",
"\n",
"* True Negative (Actual=0, Predicted=0): Model predicts that an employee would not attrite and the employee does not attrite \n",
"\n",
"* False Positive (Actual=0, Predicted=1): Model predicts that an employee would attrite but the employee does not attrite\n",
"\n",
"* False Negative (Actual=1, Predicted=0): Model predicts that an employee would not attrite but the employee attrites\n",
"\n",
"* True Positive (Actual=1, Predicted=1): Model predicts that an employee would attrite and the employee actually attrites"
]
},
{
"cell_type": "markdown",
"id": "d96c4a64",
"metadata": {
"id": "d96c4a64"
},
"source": [
"**Observations:**\n",
"- The model is performing well in terms of accuracy.\n",
"- The recall for class 1 is quite low, which implies that this model will not perform well in differentiating out the employees who have a high chance of leaving the company, and hence this model would not help reduce the attrition rate. \n",
"- The model is giving a decent average precision. A precision of ~0.75 suggests that there is a 25% (1 - 0.75) chance that the model will predict that a person is going to leave even though he/she would not, and the company would waste their time and energy on these employees who are not at risk of attrition."
]
},
{
"cell_type": "markdown",
"id": "321f2eaf",
"metadata": {
"id": "321f2eaf"
},
"source": [
"We have built the LDA model. \n",
"\n",
"**Now, let's check the coefficients and find which variables are leading to attrition and which can help to reduce the attrition**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b444b77b",
"metadata": {
"id": "b444b77b",
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OverTime
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1.041698
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Department_Research & Development
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0.795546
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Department_Sales
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0.649234
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BusinessTravel_Travel_Frequently
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0.583047
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MaritalStatus_Single
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0.565419
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NumCompaniesWorked
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0.456036
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JobRole_Sales Executive
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0.406011
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YearsSinceLastPromotion
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0.353791
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YearsAtCompany
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0.351596
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JobRole_Human Resources
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0.342187
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JobRole_Sales Representative
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0.335370
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DistanceFromHome
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0.331483
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BusinessTravel_Travel_Rarely
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0.283220
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JobLevel_5
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0.274886
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Education_3
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0.230501
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Education_2
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0.198261
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MaritalStatus_Married
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0.182050
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Education_4
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0.173300
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JobRole_Laboratory Technician
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0.148185
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JobRole_Manager
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0.144605
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Gender_Male
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0.128920
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JobLevel_4
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0.127368
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Education_5
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0.084847
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JobRole_Manufacturing Director
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0.059837
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MonthlyRate
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0.030803
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JobLevel_3
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0.013900
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JobRole_Research Director
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0.010330
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PerformanceRating
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-0.025318
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HourlyRate
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-0.045710
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PercentSalaryHike
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-0.059350
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EducationField_Technical Degree
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-0.061963
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DailyRate
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-0.063601
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StockOptionLevel
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-0.102784
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WorkLifeBalance
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-0.175842
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TrainingTimesLastYear
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-0.202162
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EducationField_Marketing
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-0.220459
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JobRole_Research Scientist
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-0.252660
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YearsWithCurrManager
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-0.270493
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RelationshipSatisfaction
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-0.274757
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YearsInCurrentRole
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-0.290026
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EducationField_Other
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-0.323048
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TotalWorkingYears
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-0.332565
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Age
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-0.345372
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JobSatisfaction
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-0.358574
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EnvironmentSatisfaction_2
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-0.508781
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EnvironmentSatisfaction_3
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-0.562208
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JobLevel_2
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-0.604475
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EducationField_Life Sciences
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-0.614948
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EnvironmentSatisfaction_4
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-0.622288
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EducationField_Medical
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-0.648146
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JobInvolvement_2
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-0.732200
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MonthlyIncome
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-0.745046
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JobInvolvement_4
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-0.779225
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JobInvolvement_3
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-1.055171
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"text/plain": [
" 0\n",
"OverTime 1.041698\n",
"Department_Research & Development 0.795546\n",
"Department_Sales 0.649234\n",
"BusinessTravel_Travel_Frequently 0.583047\n",
"MaritalStatus_Single 0.565419\n",
"NumCompaniesWorked 0.456036\n",
"JobRole_Sales Executive 0.406011\n",
"YearsSinceLastPromotion 0.353791\n",
"YearsAtCompany 0.351596\n",
"JobRole_Human Resources 0.342187\n",
"JobRole_Sales Representative 0.335370\n",
"DistanceFromHome 0.331483\n",
"BusinessTravel_Travel_Rarely 0.283220\n",
"JobLevel_5 0.274886\n",
"Education_3 0.230501\n",
"Education_2 0.198261\n",
"MaritalStatus_Married 0.182050\n",
"Education_4 0.173300\n",
"JobRole_Laboratory Technician 0.148185\n",
"JobRole_Manager 0.144605\n",
"Gender_Male 0.128920\n",
"JobLevel_4 0.127368\n",
"Education_5 0.084847\n",
"JobRole_Manufacturing Director 0.059837\n",
"MonthlyRate 0.030803\n",
"JobLevel_3 0.013900\n",
"JobRole_Research Director 0.010330\n",
"PerformanceRating -0.025318\n",
"HourlyRate -0.045710\n",
"PercentSalaryHike -0.059350\n",
"EducationField_Technical Degree -0.061963\n",
"DailyRate -0.063601\n",
"StockOptionLevel -0.102784\n",
"WorkLifeBalance -0.175842\n",
"TrainingTimesLastYear -0.202162\n",
"EducationField_Marketing -0.220459\n",
"JobRole_Research Scientist -0.252660\n",
"YearsWithCurrManager -0.270493\n",
"RelationshipSatisfaction -0.274757\n",
"YearsInCurrentRole -0.290026\n",
"EducationField_Other -0.323048\n",
"TotalWorkingYears -0.332565\n",
"Age -0.345372\n",
"JobSatisfaction -0.358574\n",
"EnvironmentSatisfaction_2 -0.508781\n",
"EnvironmentSatisfaction_3 -0.562208\n",
"JobLevel_2 -0.604475\n",
"EducationField_Life Sciences -0.614948\n",
"EnvironmentSatisfaction_4 -0.622288\n",
"EducationField_Medical -0.648146\n",
"JobInvolvement_2 -0.732200\n",
"MonthlyIncome -0.745046\n",
"JobInvolvement_4 -0.779225\n",
"JobInvolvement_3 -1.055171"
]
},
"execution_count": 21,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#creating list of column names\n",
"cols=X.columns\n",
"\n",
"#saving coefficients of lda model\n",
"coef_lda=lda.coef_\n",
"\n",
"#printing the cofficients of lda\n",
"pd.DataFrame(coef_lda,columns=cols).T.sort_values(by=0,ascending=False)"
]
},
{
"cell_type": "markdown",
"id": "24ca3b21",
"metadata": {
"id": "24ca3b21"
},
"source": [
"**Some features which positively affect the Attrition rate are:**\n",
"- OverTime\n",
"- Department_Research & Development \n",
"- BusinessTravel_Travel_Frequently\n",
"- Department_Sales\n",
"- MaritalStatus_Single \n",
"- BusinessTravel_Travel_Rarely\n",
"- NumCompaniesWorked\n",
"- YearsSinceLastPromotion\t\n",
"- JobRole_Human Resources\t\n",
"- JobRole_Sales Executive\t\n",
"- YearsAtCompany\n",
"- DistanceFromHome\n",
"\n",
"**Some features which negatively affect the Attrition rate are:**\n",
"- JobInvolvement_3\n",
"- EducationField_Life Sciences\t\n",
"- JobInvolvement_2\n",
"- MonthlyIncome\n",
"- EducationField_Medical\t\n",
"- JobInvolvement_4\t\n",
"- JobLevel_2\n",
"- EnvironmentSatisfaction_4\t\n",
"- EnvironmentSatisfaction_3\n",
"- EnvironmentSatisfaction_2\t\n",
"- JobSatisfaction\t"
]
},
{
"cell_type": "markdown",
"id": "jZL4An5c_9es",
"metadata": {
"id": "jZL4An5c_9es"
},
"source": [
"**Observations:**\n",
"\n",
"- Based on the LDA model, **Overtime is the most important feature** in detecting whether an employee would attrite or not.\n",
"- **This model also suggests that attrition is dependent on the employee's department.** Belonging to Sales or HR is shown to have a higher attrition rate which is understood, but the model also seems to suggest beloging to R&D contributes to a higher attrition rate, which is counter-intuitive. This could be because more than 65% of the employees are working in R&D, so the absolute number of employees who attrite from the company working in R&D will be significant even with a lower percentage. This is an example of the Simpson's paradox, and is evidence that a more powerful non-linear model may be necessary to accurately map the relationship between Department_Research & Development and the target variable. \n",
"- **Business traveling is an important variable in predicting the attrition rate.** Employees who either travel a lot or travel rarely have high attrition rate. This could be because those who travel often might feel overworked and dissatisfied with their role, whereas employees travelling rarely (in an organisation where nearly 90% of all employees are travelling) could be a sign of them feeling undervalued and disinterested and hence attriting more.\n",
"- **The number of companies the employee has worked for in the past also appears to impact the likelihood of attrition** - the greater the number the higher the chance the employee will attrite. This suggests that employees who have worked for a higher number of companies may probably not stay loyal and may continue switching companies.\n",
"- Other features which appear to affect the chances of attrition are the number of years at the current company and the distance from home, both with positive correlations to attrition likelihood.\n",
"- The Job Involvement features being negatively correlated with attrition signify that **employees who are more involved in their jobs tend to attrite less.** This could probably be because a high degree of job involvement might make employees feel they are more important to the company, and hence discourage them from attrition.\n",
"- The model also captures the **inverse relation between income and attrition** - suggesting attrition rates can be controlled by increasing employee salary.\n",
"- **Employees who are satisfied with the environment and culture of the organization show a lower chance of attrition**, a conclusion that makes sense since a good work environment is likely to keep employees happy and prevent them from attriting.\n",
"- **Employees with higher total work experience and a good position in the organization are also less likely to attrite**, probably because working at the organization for several years and/or occupying a good position tends to promote job stability and discourages volatility."
]
},
{
"cell_type": "markdown",
"id": "fdb458e1",
"metadata": {
"id": "fdb458e1"
},
"source": [
"#### Precision-Recall Curve for LDA\n",
"\n",
"**Precision-Recall curves summarize the trade-off between the true positive rate and the positive predictive value for a predictive model using different probability thresholds.**"
]
},
{
"cell_type": "code",
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"y_scores_lda=lda.predict_proba(x_train) #predict_proba gives the probability of each observation belonging to each class\n",
"\n",
"\n",
"precisions_lda, recalls_lda, thresholds_lda = precision_recall_curve(y_train, y_scores_lda[:,1])\n",
"\n",
"#Plot values of precisions, recalls, and thresholds\n",
"plt.figure(figsize=(10,7))\n",
"plt.plot(thresholds_lda, precisions_lda[:-1], 'b--', label='precision')\n",
"plt.plot(thresholds_lda, recalls_lda[:-1], 'g--', label = 'recall')\n",
"plt.xlabel('Threshold')\n",
"plt.legend(loc='upper left')\n",
"plt.ylim([0,1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "8c3b87cd",
"metadata": {
"id": "8c3b87cd"
},
"source": [
"**Observation:**\n",
"- We can see that precision and recall are balanced for a threshold of about ~0.35."
]
},
{
"cell_type": "markdown",
"id": "2f4523c6",
"metadata": {
"id": "2f4523c6"
},
"source": [
"**Let's check the model performance at this threshold**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a9f5e56f",
"metadata": {
"id": "a9f5e56f",
"outputId": "8519c78b-4581-4d28-9c9c-70652d5a64fb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.93 0.93 1726\n",
" 1 0.63 0.60 0.62 332\n",
"\n",
" accuracy 0.88 2058\n",
" macro avg 0.78 0.77 0.77 2058\n",
"weighted avg 0.88 0.88 0.88 2058\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"optimal_threshold1=.35\n",
"y_pred_train_lda = lda.predict_proba(x_train)\n",
"metrics_score(y_train, y_pred_train_lda[:,1]>optimal_threshold1)"
]
},
{
"cell_type": "markdown",
"id": "153db513",
"metadata": {
"id": "153db513"
},
"source": [
"- The precision has dropped but **the recall for class 1 has increased to 0.60**; the class and metric of interest here.\n",
"- **The model is able to identify the majority of employees who are at risk of attrition,** and would hence be a more useful model than the previous iteration with the defaut threshold.\n",
"\n",
"Let;s check the model performance on the test data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfe45211",
"metadata": {
"id": "dfe45211",
"outputId": "f6b8508f-00a5-4bfa-aa4b-ad8b229edab5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.93 0.93 740\n",
" 1 0.63 0.61 0.62 142\n",
"\n",
" accuracy 0.88 882\n",
" macro avg 0.78 0.77 0.78 882\n",
"weighted avg 0.88 0.88 0.88 882\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"optimal_threshold1=.35\n",
"y_pred_test_lda = lda.predict_proba(x_test)\n",
"metrics_score(y_test, y_pred_test_lda[:,1]>optimal_threshold1)"
]
},
{
"cell_type": "markdown",
"id": "5e82f11b",
"metadata": {
"id": "5e82f11b"
},
"source": [
"**Observation:**\n",
"- The model is giving **similar performance on the test and train data**, meaning the model has generalized well.\n",
"- **The average recall and precision for the model are good**, but let's see if we can get a better performance using other algorithms. "
]
},
{
"cell_type": "markdown",
"id": "25142e0a",
"metadata": {
"id": "25142e0a"
},
"source": [
"### Quadratic Discriminant Analysis"
]
},
{
"cell_type": "markdown",
"id": "98a4d421",
"metadata": {
"id": "98a4d421"
},
"source": [
"Quadratic discriminant analysis (QDA) is a probabilistic parametric classification technique which represents an evolution of LDA for nonlinear class separations. QDA, like LDA, is based on the hypothesis that the probability density distributions are multivariate normal but, in this case, the dispersion is not the same for all of the categories."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47253a63",
"metadata": {
"id": "47253a63",
"outputId": "30143ea7-a604-4261-e061-4975cb2613c3"
},
"outputs": [
{
"data": {
"text/plain": [
"QuadraticDiscriminantAnalysis()"
]
},
"execution_count": 25,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#fitting qda model\n",
"qda = QuadraticDiscriminantAnalysis()\n",
"qda.fit(x_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "21f5948e",
"metadata": {
"id": "21f5948e",
"outputId": "35ec3310-6771-4d43-9a03-2a23f83e78d1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 0.13 0.24 1726\n",
" 1 0.18 1.00 0.31 332\n",
"\n",
" accuracy 0.27 2058\n",
" macro avg 0.59 0.57 0.27 2058\n",
"weighted avg 0.87 0.27 0.25 2058\n",
"\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#checking performance of the model on the test data\n",
"y_pred_test_qda = qda.predict(x_test)\n",
"metrics_score(y_test, y_pred_test_qda)"
]
},
{
"cell_type": "markdown",
"id": "0d67cebc",
"metadata": {
"id": "0d67cebc"
},
"source": [
"**Observations:**\n",
"\n",
"- QDA gives a very high recall for class 1 but the recall for class 0 is very poor which makes the **overall recall and accuracy very low**.\n",
"- **The model has a high number of false positives**, i.e the model will predict that the employee would leave even though he/she would not.\n",
"- This is a poor model that will be of no use to the company.\n",
"\n",
"Let's move on to another model - Logistic Regression"
]
},
{
"cell_type": "markdown",
"id": "1de66d84",
"metadata": {
"id": "1de66d84"
},
"source": [
"### Logistic Regression Model "
]
},
{
"cell_type": "markdown",
"id": "2a192a78",
"metadata": {
"id": "2a192a78"
},
"source": [
"- Logistic Regression is a supervised learning algorithm which is used for **binary classification problems** i.e. where the dependent variable is categorical and has only two possible values. In logistic regression, we use the sigmoid function to calculate the probability of an event y, given some features x as:\n",
"\n",
" P(y)=1/exp(1 + exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1c3fec96",
"metadata": {
"id": "1c3fec96",
"outputId": "9bca79ca-b15a-4868-e3b2-dbf62e54fd14"
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression()"
]
},
"execution_count": 28,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#fitting logistic regression model\n",
"lg=LogisticRegression()\n",
"lg.fit(x_train,y_train)"
]
},
{
"cell_type": "markdown",
"id": "f45ec38a",
"metadata": {
"id": "f45ec38a"
},
"source": [
"**Checking model performance**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a3062aa",
"metadata": {
"id": "9a3062aa",
"outputId": "a41ccb8d-7640-4ab5-d4da-5fd22c1edce5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.91 0.98 0.94 1726\n",
" 1 0.81 0.50 0.62 332\n",
"\n",
" accuracy 0.90 2058\n",
" macro avg 0.86 0.74 0.78 2058\n",
"weighted avg 0.89 0.90 0.89 2058\n",
"\n"
]
},
{
"data": {
"image/png": 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+kXV5ZmbWyOUwpaGk4cDeQDtJM4GLSEYBtwTGpd+aeToiTouIKZJGAlNJmogHRcSy9Fank4wwbkUyq+DYWsvOcXWbP0TE4GKv9+o2Vg68uo2VizxXt1l4xkH1/n2//pCxTXJ1m12rZkgan2N5ZmbWGHl1m5pJWhtYl6T63YYvR1W1BjpnXZ6ZmVlDkseo4B8BZ5ME0ef4MrB+Bvw9h/LMzKwRy6tLslQyD6wRcQVwhaQzI+LKwmOSOmZdnpmZNXINvGm3vnL7HuvyoCppA+B7wLHAVtRh1gozM2tCHFhrJ6kVcChJMN0JWJ9kfsWJeZRnZmaN15qcIGJNyHxUsKRhJKsG9AGuAjYFPo6IRyPKbDVbMzNbfR4VXKttgY+BacBrEbFMUsP+UzAzs9IpsypXHoOXvilpS5Jm4IclzQPWl7RxRHyQdXlmZta4uSm4DiLitYj4VURsAZxDsobdJEn/zqM8MzNrxNwUXD8RMRmYLOlcYK+8yzMzs0bGTcHFSZfneWxNlWdmZo1DuTUFr7HAamZmVq0yq7HmNgl/uqZdrXlmZta0RWXUOzVkea5uc1c1eXfmWJ6ZmTVGlUWkBiyP1W22BLYBNpB0eMGh1sDaWZdnZmaNW7lNHZRHH+sWwCHAhkDfgvyFwKk5lGdmZo2ZA2vNImI0MFrSbhHxVNb3NzOz8lJuNdY8+1jfl3S3pHmS5kq6S1LXHMszMzMruTwD643AGJIFz7sA96Z5ZmZmXyqzwUt5BtYOEXFjRCxN001A+xzLMzOzRigq658asjwD63xJx0mqSNNxwIc5lmdmZo2QA2vd/QA4CvgAmAMckeaZmZmtUG6BNbcpDSPiPeDQvO5vZmZlIlTqJ8hUHhNE/KqGwxERv826TDMza7zyqIFKuoFkToV5EbFtmtcWuB3YFHgHOCoiPk6PDQZOAZYBZ0XEg2n+zsBNQCvgAeAn6aIyq5RHU/Dn1STSBz4/h/LMzKwRi0rVO9XBTcCBVfIuAMZHRA9gfLqPpK2BASSzBh4IDJFUkV5zNTAQ6JGmqvdcSR4TRPx5+bak9YGfACcDI4A/r+o6MzNrmvKosUbEREmbVsnuB+ydbt8MPEpS4esHjIiIRcAMSdOBXSW9A7RePtmRpFuA/sDYmsrOZfCSpLaSfge8TBK8d4qI8yNiXh7lmZlZ4xWheidJAyVNLkgD61BUx4iYk5QZc4AOaX4X4P2C82ameV3S7ar5Ncqjj/VS4HBgKLBdRPwn6zLMzKx8FFNjjYihJHEmC9W1LUcN+TXKo8b6M5LZli4EZkv6LE0LJX2WQ3lmZtaI5dTHWp25kjoBpD+Xt6LOBLoVnNcVmJ3md60mv0aZB9aIaBYRrSJi/YhoXZDWj4jWWZdnZmaNW0T9U5HGACem2ycCowvyB0hqKak7ySClSWlz8UJJvSQJOKHgmlXK7XusZmZmdbEaNdBVkjScZKBSO0kzgYuAS4CRkk4B3gOOBIiIKZJGAlOBpcCgiFiW3up0vvy6zVhqGbgEoFq+jlMySxa83TAfzKweWnXes9SPYJaJpYtn5TaLwzs77F/v3/ebvjiuwc4q4RqrmZmVVAOt3xXNgdXMzEoqj6bgUspzEn4zM7MmxzVWMzMrqWgqk/BLupIavggbEWfl8kRmZtakNPRl4Oqrphrr5DX2FGZm1mRVNpUaa0TcvCYfxMzMmqYm0xS8nKT2JLP/bw2svTw/IvbJ8bnMzKyJaIqjgocB04DuwP+RLA77bI7PZGZmTcganNJwjahLYN0oIq4HlkTEYxHxA6BXzs9lZmZNxBqchH+NqMvXbZakP+dI+i7JzP5dazjfzMyszprM4KUCv5O0AclycFcCrYFzcn0qMzNrMprc4KWIuC/d/BTone/jmJlZU9PQ+0zrqy6jgm+kmoki0r5WMzOz1dIUm4LvK9heGziMOqygbmZmVhdNsSn4rsL9dPHYh3N7IjMza1KaXFNwNXoAX8v6Qar6+ub98i7CLHed12tb6kcwa/CaXFOwpIV8tY/1A5KZmMzMzFZbU2wKXn9NPIiZmTVN5VZjrXXmJUnj65JnZmZmNa/HujawDtBOUhtg+T8pWgOd18CzmZlZE1BmY5dqbAr+EXA2SRB9ji8D62fA33N+LjMzayLKrSm4pvVYrwCukHRmRFy5Bp/JzMyakHIbvFSX1W0qJW24fEdSG0ln5PhMZmbWhFQWkepC0jmSpkh6VdJwSWtLaitpnKQ3059tCs4fLGm6pNclHVDs+9QlsJ4aEZ8s34mIj4FTiy3QzMysUKB6p9pI6gKcBfSMiG2BCmAAcAEwPiJ6AOPTfSRtnR7fBjgQGCKpopj3qUtgbSZpxVukBa1VTGFmZmZVVUb9Ux01B1pJak4yGHc20A+4OT1+M9A/3e4HjIiIRRExA5gO7FrM+9QlsD4IjJS0r6R9gOHA2GIKMzMzq6oS1TvVJiJmAZcB7wFzgE8j4iGgY0TMSc+ZA3RIL+kCvF9wi5lpXr3VJbCeT1JdPh0YBLwMtCqmMDMzs6qKaQqWNFDS5II0sPCead9pP6A7ybdb1pV0XA2PUV20LuqbQHWZealS0tPAZsDRQFvgrpqvMjMzq5u6DkYqFBFDgaE1nLIfMCMi5gNIGgXsDsyV1Cki5kjqBMxLz58JdCu4vitFruS2yhqrpM0l/UrSNOAq0ipyRPSOiKuKKczMzKyqPAYvkTQB95K0TjpOaF9gGjAGODE950RgdLo9BhggqaWk7iQLzkwq5n1qqrG+BjwO9I2I6ZAMXS6mEDMzs1UppsZam4h4RtKdwPPAUuAFkhrueiTjhk4hCb5HpudPkTQSmJqePygilhVTdk2B9XskQ48nSPoXMILq26DNzMyKlkdgBYiIi4CLqmQvIqm9Vnf+xcDFq1vuKpuCI+LuiDga2BJ4FDgH6Cjpakl9VrdgMzMzyK0puGRqHRUcEZ9HxLCIOISkM/dF0i/UmpmZra5K1T81ZLWOCi4UER8B16bJzMxstdXle6mNSb0Cq5mZWdbKbdm4ukwQYWZmZnXkGquZmZVUXqOCS8WB1czMSqpS7mM1MzPLTLn1sTqwmplZSbkp2MzMLEMN/Xup9eXAamZmJeXvsZqZmWXIfaxmZmYZclOwmZlZhjx4yczMLENuCjYzM8uQm4LNzMwy5KZgMzOzDDmwmpmZZSjcFGxmZpYd11jNzMwy5MBqZmaWoXL7uk2zUj+AmZlZOXGN1czMSqrcvsfqGquZmZVUZRGpLiRtKOlOSa9JmiZpN0ltJY2T9Gb6s03B+YMlTZf0uqQDin0fB1YzMyupvAIrcAXwr4jYEvgmMA24ABgfET2A8ek+krYGBgDbAAcCQyRVFPM+DqxmZlZSUUSqjaTWwF7A9QARsTgiPgH6ATenp90M9E+3+wEjImJRRMwApgO7FvM+DqxmZlZSlap/qoPNgPnAjZJekHSdpHWBjhExByD92SE9vwvwfsH1M9O8enNgNTOzkiqmKVjSQEmTC9LAKrdtDuwEXB0ROwKfkzb7rkJ14bqobwJ5VLCZmZVUMdErIoYCQ2s4ZSYwMyKeSffvJAmscyV1iog5kjoB8wrO71ZwfVdgdhGP5hqrmZmVViVR71SbiPgAeF/SFmnWvsBUYAxwYpp3IjA63R4DDJDUUlJ3oAcwqZj3cY3VzMxKKscpDc8EhklaC3gbOJmkQjlS0inAe8CRABExRdJIkuC7FBgUEcuKKdSB1czMSiqvKQ0j4kWgZzWH9l3F+RcDF69uuQ6sZmZWUp6E38zMLEPlNqWhA6uZmZVUXQYjNSYOrGZmVlLlFVYdWM3MrMTcx2pmZpahcmsK9gQRZmZmGXKN1czMSqq86qsOrGZmVmLuYzUzM8tQufWxOrCamVlJlVdYdWA1M7MSc1OwmZlZhqLM6qwOrGZmVlKusZqZmWWo3AYveYKIRuzSK3/D868/yrgnR30l/6RTj2XCM2N4+N9384tfn7Mif8utN+fuB//Jw/++m4eeGEXLlmutdM8NNmzNsFFDeezZ+xg2aigbbNB6xbFBZ5/CxMn3M+GZMey1z+75vZg1Of4sN21RRGrIHFgbsTtuG80JR57+lbzdvr0LfQ7qzQF7fo/9dj+Ma6+6GYCKigquuPYP/OKnv2G/3Q/jqL4ns2TJ0pXuOejsU3jysWf4zi6H8ORjz3DG2acA0GOLzeh7+EHst3t/TjjydC6+9EKaNfPHx7Lhz3LTVknUOzVk/jQ1YpOeeo5PPv70K3nH/+BohlxxPYsXLwHgwwUfAbBX792ZNuUNpk15A4BPPv6UysqVezb2P6g3d44YDcCdI0bT5+DeAPQ5qDf3jhrL4sVLeP+9Wbwz4z122Hm73N7NmhZ/lpu2yiJSQ+bAWma6f30Tdt1tJ0aPG8bIe29k+x23AWCzb2wCEdx65zXcP+F2Tjvz5Gqvb9dhI+bNXQDAvLkLaNd+IwA6durI7FlzV5w3Z/ZcNu7UIee3sabMn+WmI4r4ryHLbfCSpM2Bq4GOEbGtpO2BQyPid3mVadC8eQUbbNCafvt/n2/utC1DbriMb+94EBXNK+jZa0f67nsMX3zxP4bfcx2vvDSVJyc+U6f7SlopL6Jhf7itcfNnuelo6DXQ+sqzxvoPYDCwBCAiXgYG1HSBpIGSJkua/J9FH+X4aOVrzuy5jL3vYQBeev5VojJou1Eb5syeyzNPPsfHH33C/774HxPGPc6239xqpesXzPuQDh3bAdChYzsWzP8QgA9mf0DnLh1XnNepc0fmfjB/DbyRNVX+LDcd5VZjzTOwrhMRk6rkrTzCoEBEDI2InhHRc72WbXN8tPL10P2PsPte3wKSprQWa7Xgow8/ZuL4f7PlNj1Yu9XaVFRU0Gv3nrz52lsrXT/uX49yxIB+ABwxoB/jxk5Ykd/38INYa60WdPtaF7pvtgkvPvfKmnsxa3L8WW46yq2PNc/vsS6Q9HXSkdGSjgDm5Fhek3PlP/7IbnvsQpuNNuSZVx/m8kv+zu3D7ubSK3/LuCdHsXjxEn56xi8B+PTTz7huyK3cN344EcGEcY/zyLjHAfjjFb9m2I0jefnFqQz56/VcfcNlHH3cYcyeOYfTTv4ZAG+89hb33fMg458azdKlS7nw5xdXO2DErBj+LDdtlWXWFK+8+hYkbQYMBXYHPgZmAN+PiHfrcv3X2m5XXn/SZmaN2HsfvbJy53RGjt/k8Hr/vr/13VG5Pc/qyrMpOCJiP6A9sGVEfDvn8szMrBHKc4IISRWSXpB0X7rfVtI4SW+mP9sUnDtY0nRJr0s6oNj3yTPQ3QUQEZ9HxMI0784cyzMzs0Yo5wkifgJMK9i/ABgfET2A8ek+krYmGWC7DXAgMERSRTHvk3kfq6QtSR5sA0mHFxxqDayddXlmZta45TXKV1JX4LvAxcBP0+x+wN7p9s3Ao8D5af6IiFgEzJA0HdgVeKq+5eYxeGkL4BBgQ6BvQf5C4NQcyjMzs0Ysx6FjfwV+DqxfkNcxIuYARMQcSctnB+kCPF1w3sw0r94yD6wRMRoYLWm3iKh3pDczs6almLl/JQ0EBhZkDY2IoQXHDwHmRcRzkvauyy2rySuqKp1HU/DPI+JPwLGSjql6PCLOyrpMMzNrvIppCk6D6NAaTtkDOFTSwSTdkK0l/ROYK6lTWlvtBMxLz58JdCu4viswu94PRj6Dl5Z3Ek8GnqsmmZmZrZDHBBERMTgiukbEpiSDkh6JiOOAMcCJ6WknAqPT7THAAEktJXUHegBVJzmqkzyagu9NR1JtGxHnZX1/MzMrL2t4ruZLgJGSTgHeA45Mn2GKpJHAVJJZAgdFxLJiCshl5qWIWCZp5zzubWZm5SXv9VUj4lGS0b9ExIfAvqs472KSEcSrJc8pDV+QNAa4A/h8eWZEjMqxTDMza2TKbULJPANrW+BDYJ+CvAAcWM3MbIWGvlpNfeUZWK+LiCcLMyTtkWN5ZmbWCOXdFLym5Tml4ZV1zDMzsyYsIuqdGrI8vse6G8mKNu0l/bTgUGugqHkXzcysfLmPtXZrAeul9y6cRuoz4IgcyjMzs0bMfay1iIjHgMckfZHOwLSCpCOBN7Mu08zMGi/3sdbdgGryBudYnpmZWcnl0cd6EHAw0EXS36MRmVcAAAl0SURBVAoOrQ8sybo8MzNr3Br6YKT6yqOPdTbJnMCH8tW5gTcB/ptDeWZm1oiVW1NwHn2sLwEvSRpGsuD5scBRwAzgrqzLMzOzxs2Dl2ohaXOS/tVjSGZeuh1QRPTOuiwzM2v8Kt0UXKvXgMeBvhExHUDSOTmUY2ZmZaC8wmo+o4K/B3wATJD0D0n7Uv3K7GZmZlQS9U4NWeaBNSLujoijgS1Jluk5B+go6WpJfbIuz8zMGjcH1jqKiM8jYlhEHAJ0BV4ELsirPDMza5w8V3ARIuIj4No0mZmZrdDQa6D1tUYCq5mZ2ar46zZmZmYZauhNu/XlwGpmZiXlpmAzM7MMucZqZmaWIddYzczMMuTBS2ZmZhkqt7mC81zo3MzMrCQkdZM0QdI0SVMk/STNbytpnKQ3059tCq4ZLGm6pNclHVBs2Q6sZmZWUlHEf3WwFPhZRGwF9AIGSdqaZAbA8RHRAxif7pMeG0Cy3OmBwBBJFcW8jwOrmZmVVGVEvVNtImJORDyfbi8EpgFdgH7AzelpNwP90+1+wIiIWBQRM4DpwK7FvI8Dq5mZlVRONdYVJG0K7Ag8A3SMiDmQBF+gQ3paF+D9gstmpnn15sFLZmZWUsUMXpI0EBhYkDU0IoZWc956wF3A2RHxmbTKVUyrO1DUqCoHVjMzK6livm6TBtGVAmkhSS1IguqwiBiVZs+V1Cki5kjqBMxL82cC3Qou7wrMrveD4aZgMzMrsTz6WJVUTa8HpkXE5QWHxgAnptsnAqML8gdIaimpO9ADmFTM+7jGamZmJZXTBBF7AMcDr0h6Mc37BXAJMFLSKcB7wJEAETFF0khgKsmI4kERsayYgh1YzcyspCIqc7hnPEH1/aYA+67imouBi1e3bAdWMzMrKc8VbGZmliGvbmNmZpYh11jNzMwy5BqrmZlZhsptdRsHVjMzKymvx2pmZpYhNwWbmZllyIOXzMzMMlRuNVbPFWxmZpYh11jNzKykPCrYzMwsQ+XWFOzAamZmJeXBS2ZmZhlyjdXMzCxD7mM1MzPLkGdeMjMzy5BrrGZmZhlyH6uZmVmG3BRsZmaWIddYzczMMuTAamZmlqHyCqugcvuXgtWdpIERMbTUz2G2uvxZtobEq9s0bQNL/QBmGfFn2RoMB1YzM7MMObCamZllyIG1aXOflJULf5atwfDgJTMzswy5xmpmZpYhB9YGSlJI+nPB/rmSfl3LNf0lbV3LOS9JGl4l72xJ6xTs/6KWezwgacM0nVHji5jVg6TD0s/+lun+DpIOLji+t6Tda7j+UEkXpNu1/n0wy4MDa8O1CDhcUrt6XNMfWOUvEklbkfw/30vSugWHzgbWKdivNrAq0SwiDo6IT4ANAQdWy9IxwBPAgHR/B+DgguN7A9UGVknNI2JMRFySZtX498EsL+5jbaAk/Qe4GFgvIn4p6dx0+9eSNgFuANoD84GTga7AfcCnafpeRLxV5Z6/BRYCWwEPRcRwSWcBlwGvAwuAZ4DzgFeAKcAvgbHABGA3kl9WjwE9gauAfum14yLiPEnnAUcBLYG7I+KiPP58rPxIWo/ks9QbGANsD0wHWgGzgOHAOcAyks/9mcApwEfAjsDzJJ/bnsBtVPn7kBbzd5K/N/8FTo2I19bAq1lTExFODTAB/wFaA+8AGwDnAr9Oj90LnJhu/wC4J92+CTiihnu+AWwC9AHGFOS/A7QrLLtge1OgEuhV9fz02KsF+X1IRmeKpGZ8H7BXqf8snRpHAo4Drk+3/w3sBJwEXFVwzq+Bcwv2b0o/ZxXp/orzq/59AMYDPdLtbwGPlPqdncozea7gBiwiPpN0C3AW8EXBod2Aw9PtW4E/1XYvSbsA8yPiXUkzgRsktYmIj+vwKO9GxNN1OK9Pml5I99cDegAT63Ct2THAX9PtEen+lDpcd0dELKvphLQ2vDtwh6Tl2S2LfE6zGjmwNnx/JWniurGGc+rSnn8MsKWkd9L91iTNY9fV4drP63AOJDXVP0TEtXU83wwASRsB+wDbSgqgguRzXZeuhLp8PpsBn0TEDsU/pVndePBSAxcRHwEjSfqSlvs3Xw7u+D7JYA9I+k/Xr3oPSc2AI4HtI2LTiNiUpG/0mFVct0RSizo8XtXrHgR+kNYOkNRFUoc63MfsCOCWiNgk/Yx2A2YAX+Orn7FqP+OrsOLciPgMmCHpSFgxEO+bmT29WQEH1sbhzyR9msudBZws6WXgeOAnaf4I4DxJL0j6esH5ewGzImJWQd5EYGtJnUj6RcdKmpAeGwq8LGlYTQ8VER8CT0p6VdKlEfEQyaCRpyS9AtxJ3X8JWtN2DHB3lby7gI1JPqcvSjqaZHzBYen+nrXcs+rfh+8Dp0h6iaSJuV+2r2CW8KhgMzOzDLnGamZmliEHVjMzsww5sJqZmWXIgdXMzCxDDqxmZmYZcmA1AyQtS7/C8aqkOwpX+yniXjdJOiLdvq6mFVZqW62lhuveqecCDWa2hjiwmiW+iIgdImJbYDFwWuFBSRXF3DQifhgRU2s4ZW9WsVqLmTVODqxmK3sc+EZam5wg6TbgFUkVki6V9KyklyX9CFbM4nOVpKmS7gdWzDYl6VFJPdPtAyU9n66JO17SpiQB/JzlEx5Iai/prrSMZyXtkV67kaSH0skOriWZPtLMGiDPFWxWQFJz4CDgX2nWrsC2ETFD0kDg04jYRVJLklmnHiJZsmwLYDugIzCVZFm/wvu2B/5BstrPDEltI+IjSdeQrCZ0WXrebcBfIuIJSV8jmSZyK5I5c5+IiN9I+i4wMNc/CDMrmgOrWaKVpBfT7ceB60maaCdFxIw0vw+w/fL+U5Ll/HqQTBk5PF1hZbakR6q5fy9g4vJ7pXNAV2c/kin8lu+3lrR+Wsbh6bX3S6rLqkRmVgIOrGaJL6qufJIGt8KVUwScGREPVjnvYGpfYUh1OAeS7pndIqJwmcDlz+L5R80aAfexmtXdg8Dpy1f+kbS5pHVJFjQYkPbBdgJ6V3PtU8B3JHVPr22b5lddreUh4MfLdyQtD/YTSSaRR9JBQJvM3srMMuXAalZ315H0nz4v6VXgWpJWn7uBN4FXgKuBx6peGBHzSfpFR6Wrq9yeHqq6WstZQM90cNRUvhyd/H/AXpKeJ2mSfi+ndzSz1eTVbczMzDLkGquZmVmGHFjNzMwy5MBqZmaWIQdWMzOzDDmwmpmZZciB1czMLEMOrGZmZhlyYDUzM8vQ/weP5pUmWP4a8wAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light",
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"#checking the performance on the test dataset\n",
"y_pred_test = lg.predict(x_test)\n",
"metrics_score(y_test, y_pred_test)"
]
},
{
"cell_type": "markdown",
"id": "382c8e52",
"metadata": {
"id": "382c8e52"
},
"source": [
"**Observations:**\n",
"- **We are getting an accuracy of around 90%** on train and test dataset.\n",
"- However, **the recall for this model is only around 50% for class 1 on train and 46% on test.**\n",
"- As the recall is low, **this model will not perform well** in differentiating out those employees who have a high chance of leaving the company, meaning it will eventually not help in reducing the attrition rate. \n",
"- As we can see from the Confusion Matrix, **this model fails to identify the majority of employees who are at risk of attrition.**"
]
},
{
"cell_type": "markdown",
"id": "dc744c23",
"metadata": {
"id": "dc744c23"
},
"source": [
"**Let's check the coefficients and find which variables are leading to attrition and which can help to reduce the attrition**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82beae47",
"metadata": {
"id": "82beae47",
"outputId": "ced49131-018c-47b9-b523-a1ec728ea249"
},
"outputs": [
{
"data": {
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"
\n",
"\n",
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" \n",
"
\n",
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\n",
"
0
\n",
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\n",
" \n",
" \n",
"
\n",
"
OverTime
\n",
"
0.958034
\n",
"
\n",
"
\n",
"
BusinessTravel_Travel_Frequently
\n",
"
0.716046
\n",
"
\n",
"
\n",
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MaritalStatus_Single
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0.618145
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YearsSinceLastPromotion
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"
0.552935
\n",
"
\n",
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YearsAtCompany
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"
0.523238
\n",
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\n",
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NumCompaniesWorked
\n",
"
0.501137
\n",
"
\n",
"
\n",
"
Department_Sales
\n",
"
0.483346
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Department_Research & Development
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"
0.482820
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BusinessTravel_Travel_Rarely
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"
0.441384
\n",
"
\n",
"
\n",
"
DistanceFromHome
\n",
"
0.384346
\n",
"
\n",
"
\n",
"
JobRole_Sales Executive
\n",
"
0.383153
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"
\n",
"
\n",
"
MaritalStatus_Married
\n",
"
0.288340
\n",
"
\n",
"
\n",
"
JobRole_Human Resources
\n",
"
0.282114
\n",
"
\n",
"
\n",
"
JobLevel_5
\n",
"
0.269649
\n",
"
\n",
"
\n",
"
JobRole_Laboratory Technician
\n",
"
0.177910
\n",
"
\n",
"
\n",
"
JobRole_Sales Representative
\n",
"
0.173305
\n",
"
\n",
"
\n",
"
Gender_Male
\n",
"
0.165803
\n",
"
\n",
"
\n",
"
Education_3
\n",
"
0.158748
\n",
"
\n",
"
\n",
"
Education_2
\n",
"
0.131061
\n",
"
\n",
"
\n",
"
Education_4
\n",
"
0.113254
\n",
"
\n",
"
\n",
"
JobRole_Manufacturing Director
\n",
"
0.112275
\n",
"
\n",
"
\n",
"
Education_5
\n",
"
0.092054
\n",
"
\n",
"
\n",
"
EducationField_Technical Degree
\n",
"
0.083282
\n",
"
\n",
"
\n",
"
MonthlyRate
\n",
"
0.059920
\n",
"
\n",
"
\n",
"
HourlyRate
\n",
"
0.048010
\n",
"
\n",
"
\n",
"
JobLevel_3
\n",
"
0.007039
\n",
"
\n",
"
\n",
"
EducationField_Marketing
\n",
"
-0.013725
\n",
"
\n",
"
\n",
"
JobRole_Manager
\n",
"
-0.032051
\n",
"
\n",
"
\n",
"
PerformanceRating
\n",
"
-0.032545
\n",
"
\n",
"
\n",
"
PercentSalaryHike
\n",
"
-0.074595
\n",
"
\n",
"
\n",
"
DailyRate
\n",
"
-0.095750
\n",
"
\n",
"
\n",
"
StockOptionLevel
\n",
"
-0.107451
\n",
"
\n",
"
\n",
"
EducationField_Other
\n",
"
-0.138263
\n",
"
\n",
"
\n",
"
JobLevel_4
\n",
"
-0.161223
\n",
"
\n",
"
\n",
"
WorkLifeBalance
\n",
"
-0.212611
\n",
"
\n",
"
\n",
"
JobRole_Research Scientist
\n",
"
-0.233311
\n",
"
\n",
"
\n",
"
TrainingTimesLastYear
\n",
"
-0.240552
\n",
"
\n",
"
\n",
"
Age
\n",
"
-0.275176
\n",
"
\n",
"
\n",
"
RelationshipSatisfaction
\n",
"
-0.312201
\n",
"
\n",
"
\n",
"
EducationField_Life Sciences
\n",
"
-0.319031
\n",
"
\n",
"
\n",
"
EducationField_Medical
\n",
"
-0.354189
\n",
"
\n",
"
\n",
"
JobRole_Research Director
\n",
"
-0.359223
\n",
"
\n",
"
\n",
"
JobSatisfaction
\n",
"
-0.373627
\n",
"
\n",
"
\n",
"
YearsWithCurrManager
\n",
"
-0.382881
\n",
"
\n",
"
\n",
"
YearsInCurrentRole
\n",
"
-0.438966
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_2
\n",
"
-0.444969
\n",
"
\n",
"
\n",
"
JobInvolvement_2
\n",
"
-0.485360
\n",
"
\n",
"
\n",
"
TotalWorkingYears
\n",
"
-0.497228
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_3
\n",
"
-0.501917
\n",
"
\n",
"
\n",
"
MonthlyIncome
\n",
"
-0.599868
\n",
"
\n",
"
\n",
"
JobInvolvement_4
\n",
"
-0.641244
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_4
\n",
"
-0.651612
\n",
"
\n",
"
\n",
"
JobLevel_2
\n",
"
-0.714008
\n",
"
\n",
"
\n",
"
JobInvolvement_3
\n",
"
-0.750455
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" 0\n",
"OverTime 0.958034\n",
"BusinessTravel_Travel_Frequently 0.716046\n",
"MaritalStatus_Single 0.618145\n",
"YearsSinceLastPromotion 0.552935\n",
"YearsAtCompany 0.523238\n",
"NumCompaniesWorked 0.501137\n",
"Department_Sales 0.483346\n",
"Department_Research & Development 0.482820\n",
"BusinessTravel_Travel_Rarely 0.441384\n",
"DistanceFromHome 0.384346\n",
"JobRole_Sales Executive 0.383153\n",
"MaritalStatus_Married 0.288340\n",
"JobRole_Human Resources 0.282114\n",
"JobLevel_5 0.269649\n",
"JobRole_Laboratory Technician 0.177910\n",
"JobRole_Sales Representative 0.173305\n",
"Gender_Male 0.165803\n",
"Education_3 0.158748\n",
"Education_2 0.131061\n",
"Education_4 0.113254\n",
"JobRole_Manufacturing Director 0.112275\n",
"Education_5 0.092054\n",
"EducationField_Technical Degree 0.083282\n",
"MonthlyRate 0.059920\n",
"HourlyRate 0.048010\n",
"JobLevel_3 0.007039\n",
"EducationField_Marketing -0.013725\n",
"JobRole_Manager -0.032051\n",
"PerformanceRating -0.032545\n",
"PercentSalaryHike -0.074595\n",
"DailyRate -0.095750\n",
"StockOptionLevel -0.107451\n",
"EducationField_Other -0.138263\n",
"JobLevel_4 -0.161223\n",
"WorkLifeBalance -0.212611\n",
"JobRole_Research Scientist -0.233311\n",
"TrainingTimesLastYear -0.240552\n",
"Age -0.275176\n",
"RelationshipSatisfaction -0.312201\n",
"EducationField_Life Sciences -0.319031\n",
"EducationField_Medical -0.354189\n",
"JobRole_Research Director -0.359223\n",
"JobSatisfaction -0.373627\n",
"YearsWithCurrManager -0.382881\n",
"YearsInCurrentRole -0.438966\n",
"EnvironmentSatisfaction_2 -0.444969\n",
"JobInvolvement_2 -0.485360\n",
"TotalWorkingYears -0.497228\n",
"EnvironmentSatisfaction_3 -0.501917\n",
"MonthlyIncome -0.599868\n",
"JobInvolvement_4 -0.641244\n",
"EnvironmentSatisfaction_4 -0.651612\n",
"JobLevel_2 -0.714008\n",
"JobInvolvement_3 -0.750455"
]
},
"execution_count": 31,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"#printing the coefficients of logistic regression\n",
"cols=X.columns\n",
"\n",
"coef_lg=lg.coef_\n",
"\n",
"pd.DataFrame(coef_lg,columns=cols).T.sort_values(by=0,ascending=False)"
]
},
{
"cell_type": "markdown",
"id": "163545b6",
"metadata": {
"id": "163545b6"
},
"source": [
"**Observations:**\n",
"\n",
"\n",
"**Features which string positive affect on the attrition rate are:**\n",
"- OverTime\t\n",
"- BusinessTravel_Travel_Frequently\t\n",
"- Department_Research & Development\t\n",
"- JobRole_Sales Executive\t\n",
"- MaritalStatus_Single\t\n",
"- Department_Sales\t\n",
"- NumCompaniesWorked\t\n",
"- YearsSinceLastPromotion\n",
"- JobLevel_5\t\n",
"- BusinessTravel_Travel_Rarely\n",
"- DistanceFromHome\n",
"- YearsAtCompany\t\n",
"- JobRole_Human Resources\t\n",
"- JobRole_Sales Representative\n",
"\n",
"**Features which string negative affect on the attrition rate are:**\n",
"- MonthlyIncome\t\n",
"- JobInvolvement_3\t\n",
"- JobLevel_2\t\n",
"- EnvironmentSatisfaction_4\t\n",
"- JobInvolvement_4\t\n",
"- JobInvolvement_2\t\n",
"- EnvironmentSatisfaction_3\t\n",
"- EducationField_Life Sciences\t\n",
"- EnvironmentSatisfaction_2\t\n",
"- YearsWithCurrManager\t\n",
"- JobRole_Research Director\t\n",
"- TotalWorkingYears\t\n",
"- JobSatisfaction\t\n",
"\n",
"**The coefficients which are positively and negatively affecting the attrition rate seem to be quite similar for logistic regression and LDA. This means they are capturing the same pattern and giving nearly the same conclusions from the dataset.**"
]
},
{
"cell_type": "markdown",
"id": "4291d05e",
"metadata": {
"id": "4291d05e"
},
"source": [
"The coefficients of the logistic regression model give us the log of odds, which is hard to interpret in the real world. We can convert the log of odds into real odds by taking its exponential."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d48313b",
"metadata": {
"id": "9d48313b",
"outputId": "33a2e695-0f22-400e-f8b8-1827c63821da"
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
odds
\n",
"
\n",
" \n",
" \n",
"
\n",
"
OverTime
\n",
"
2.606567
\n",
"
\n",
"
\n",
"
BusinessTravel_Travel_Frequently
\n",
"
2.046326
\n",
"
\n",
"
\n",
"
MaritalStatus_Single
\n",
"
1.855483
\n",
"
\n",
"
\n",
"
YearsSinceLastPromotion
\n",
"
1.738348
\n",
"
\n",
"
\n",
"
YearsAtCompany
\n",
"
1.687483
\n",
"
\n",
"
\n",
"
NumCompaniesWorked
\n",
"
1.650597
\n",
"
\n",
"
\n",
"
Department_Sales
\n",
"
1.621491
\n",
"
\n",
"
\n",
"
Department_Research & Development
\n",
"
1.620638
\n",
"
\n",
"
\n",
"
BusinessTravel_Travel_Rarely
\n",
"
1.554858
\n",
"
\n",
"
\n",
"
DistanceFromHome
\n",
"
1.468653
\n",
"
\n",
"
\n",
"
JobRole_Sales Executive
\n",
"
1.466903
\n",
"
\n",
"
\n",
"
MaritalStatus_Married
\n",
"
1.334210
\n",
"
\n",
"
\n",
"
JobRole_Human Resources
\n",
"
1.325929
\n",
"
\n",
"
\n",
"
JobLevel_5
\n",
"
1.309504
\n",
"
\n",
"
\n",
"
JobRole_Laboratory Technician
\n",
"
1.194718
\n",
"
\n",
"
\n",
"
JobRole_Sales Representative
\n",
"
1.189229
\n",
"
\n",
"
\n",
"
Gender_Male
\n",
"
1.180341
\n",
"
\n",
"
\n",
"
Education_3
\n",
"
1.172042
\n",
"
\n",
"
\n",
"
Education_2
\n",
"
1.140037
\n",
"
\n",
"
\n",
"
Education_4
\n",
"
1.119916
\n",
"
\n",
"
\n",
"
JobRole_Manufacturing Director
\n",
"
1.118820
\n",
"
\n",
"
\n",
"
Education_5
\n",
"
1.096424
\n",
"
\n",
"
\n",
"
EducationField_Technical Degree
\n",
"
1.086849
\n",
"
\n",
"
\n",
"
MonthlyRate
\n",
"
1.061752
\n",
"
\n",
"
\n",
"
HourlyRate
\n",
"
1.049181
\n",
"
\n",
"
\n",
"
JobLevel_3
\n",
"
1.007064
\n",
"
\n",
"
\n",
"
EducationField_Marketing
\n",
"
0.986369
\n",
"
\n",
"
\n",
"
JobRole_Manager
\n",
"
0.968457
\n",
"
\n",
"
\n",
"
PerformanceRating
\n",
"
0.967979
\n",
"
\n",
"
\n",
"
PercentSalaryHike
\n",
"
0.928119
\n",
"
\n",
"
\n",
"
DailyRate
\n",
"
0.908691
\n",
"
\n",
"
\n",
"
StockOptionLevel
\n",
"
0.898120
\n",
"
\n",
"
\n",
"
EducationField_Other
\n",
"
0.870870
\n",
"
\n",
"
\n",
"
JobLevel_4
\n",
"
0.851102
\n",
"
\n",
"
\n",
"
WorkLifeBalance
\n",
"
0.808470
\n",
"
\n",
"
\n",
"
JobRole_Research Scientist
\n",
"
0.791907
\n",
"
\n",
"
\n",
"
TrainingTimesLastYear
\n",
"
0.786194
\n",
"
\n",
"
\n",
"
Age
\n",
"
0.759438
\n",
"
\n",
"
\n",
"
RelationshipSatisfaction
\n",
"
0.731835
\n",
"
\n",
"
\n",
"
EducationField_Life Sciences
\n",
"
0.726853
\n",
"
\n",
"
\n",
"
EducationField_Medical
\n",
"
0.701742
\n",
"
\n",
"
\n",
"
JobRole_Research Director
\n",
"
0.698219
\n",
"
\n",
"
\n",
"
JobSatisfaction
\n",
"
0.688234
\n",
"
\n",
"
\n",
"
YearsWithCurrManager
\n",
"
0.681894
\n",
"
\n",
"
\n",
"
YearsInCurrentRole
\n",
"
0.644703
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_2
\n",
"
0.640844
\n",
"
\n",
"
\n",
"
JobInvolvement_2
\n",
"
0.615475
\n",
"
\n",
"
\n",
"
TotalWorkingYears
\n",
"
0.608214
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_3
\n",
"
0.605369
\n",
"
\n",
"
\n",
"
MonthlyIncome
\n",
"
0.548884
\n",
"
\n",
"
\n",
"
JobInvolvement_4
\n",
"
0.526637
\n",
"
\n",
"
\n",
"
EnvironmentSatisfaction_4
\n",
"
0.521205
\n",
"
\n",
"
\n",
"
JobLevel_2
\n",
"
0.489678
\n",
"
\n",
"
\n",
"
JobInvolvement_3
\n",
"
0.472151
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" odds\n",
"OverTime 2.606567\n",
"BusinessTravel_Travel_Frequently 2.046326\n",
"MaritalStatus_Single 1.855483\n",
"YearsSinceLastPromotion 1.738348\n",
"YearsAtCompany 1.687483\n",
"NumCompaniesWorked 1.650597\n",
"Department_Sales 1.621491\n",
"Department_Research & Development 1.620638\n",
"BusinessTravel_Travel_Rarely 1.554858\n",
"DistanceFromHome 1.468653\n",
"JobRole_Sales Executive 1.466903\n",
"MaritalStatus_Married 1.334210\n",
"JobRole_Human Resources 1.325929\n",
"JobLevel_5 1.309504\n",
"JobRole_Laboratory Technician 1.194718\n",
"JobRole_Sales Representative 1.189229\n",
"Gender_Male 1.180341\n",
"Education_3 1.172042\n",
"Education_2 1.140037\n",
"Education_4 1.119916\n",
"JobRole_Manufacturing Director 1.118820\n",
"Education_5 1.096424\n",
"EducationField_Technical Degree 1.086849\n",
"MonthlyRate 1.061752\n",
"HourlyRate 1.049181\n",
"JobLevel_3 1.007064\n",
"EducationField_Marketing 0.986369\n",
"JobRole_Manager 0.968457\n",
"PerformanceRating 0.967979\n",
"PercentSalaryHike 0.928119\n",
"DailyRate 0.908691\n",
"StockOptionLevel 0.898120\n",
"EducationField_Other 0.870870\n",
"JobLevel_4 0.851102\n",
"WorkLifeBalance 0.808470\n",
"JobRole_Research Scientist 0.791907\n",
"TrainingTimesLastYear 0.786194\n",
"Age 0.759438\n",
"RelationshipSatisfaction 0.731835\n",
"EducationField_Life Sciences 0.726853\n",
"EducationField_Medical 0.701742\n",
"JobRole_Research Director 0.698219\n",
"JobSatisfaction 0.688234\n",
"YearsWithCurrManager 0.681894\n",
"YearsInCurrentRole 0.644703\n",
"EnvironmentSatisfaction_2 0.640844\n",
"JobInvolvement_2 0.615475\n",
"TotalWorkingYears 0.608214\n",
"EnvironmentSatisfaction_3 0.605369\n",
"MonthlyIncome 0.548884\n",
"JobInvolvement_4 0.526637\n",
"EnvironmentSatisfaction_4 0.521205\n",
"JobLevel_2 0.489678\n",
"JobInvolvement_3 0.472151"
]
},
"execution_count": 32,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"odds = np.exp(lg.coef_[0]) #finding the odds\n",
"\n",
"# adding the odds to a dataframe and sorting the values\n",
"pd.DataFrame(odds, x_train.columns, columns=['odds']).sort_values(by='odds', ascending=False) "
]
},
{
"cell_type": "markdown",
"id": "8f068e40",
"metadata": {
"id": "8f068e40"
},
"source": [
"- The odds of an employee working overtime to attrite are **2.6 times** the odds of one who is not, probably due to the fact that working overtime is not sustainable for an extended duration for any employee, and may lead to burnout and job dissatisfaction.\n",
"- The odds of an employee travelling frequently to attrite are **double** the odds of an employee who doesn't travel as often.\n",
"- The odds of single employees attriting are **1.8 times (80% higher than)** the odds of an employee with another marital status."
]
},
{
"cell_type": "markdown",
"id": "991566fe",
"metadata": {
"id": "991566fe"
},
"source": [
"**Precision-Recall Curve for logistic regression**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ee22ec1",
"metadata": {
"id": "9ee22ec1",
"outputId": "b8f98181-3e96-4fc1-d0e5-aab4f68c1140"
},
"outputs": [
{
"data": {
"image/png": 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\n",
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