Final Project Abstract

Abstarct

HR Analytics finds out the people-related trends in the data and helps the HR Department take the appropriate steps to keep the organization running smoothly and profitably. Attrition is a corporate setup is one of the complex challenges that the people managers and the HRs personnel have to deal with it.

In this research assignment, we investigated data on employee attrition of a company. This is a fictional data set created by IBM data scientists.

We have collected this dataset from Kaggle, using the below link: https://www.kaggle.com/pavansubhasht/ibm-hr-analytics-attrition-dataset

Problem Statement

The first question comes into our mind, what is attrition?

Attrition is a process in which the workforce dwindles at a company, following a period in which a number of people retire or resign, and are not replaced.

Second question, what are the reason for attrition?

This can happen for many reasons:

Employees looking for better opportunities
A negative working environment
Bad management
Excessive working hours

We will do the analysis based on Gender, Education, Income, Working Environment, and lastly, build a predictive model to determine whether an employee is going to quit or not.

Approaches follow

The first part will consider the data provided and attempt to identify trends and patterns. The data is then split into training and testing sets and using machine learning techniques identify the individuals that are more likely to leave the organization.

Response or Target feature is the Attrition which is going to be our feature of interest for the prediction - based on the independent features.

Dataset Overview

The dataset consists of 1470 observations (rows), 35 features (variables). There is no missing data! this will make it easier to work with the dataset. We only have two datatypes in this dataset: factors and integers.

Let’s have an overview of the dataset.

hr_data <- 
  read.csv("https://raw.githubusercontent.com/mharrisonbaker/DATA621_GroupWork2/main/Final%20Project/WA_Fn-UseC_-HR-Employee-Attrition.csv")
dim(hr_data)

## [1] 1470   35

str(hr_data)

## 'data.frame':    1470 obs. of  35 variables:
##  $ Age                     : int  41 49 37 33 27 32 59 30 38 36 ...
##  $ Attrition               : chr  "Yes" "No" "Yes" "No" ...
##  $ BusinessTravel          : chr  "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" "Travel_Frequently" ...
##  $ DailyRate               : int  1102 279 1373 1392 591 1005 1324 1358 216 1299 ...
##  $ Department              : chr  "Sales" "Research & Development" "Research & Development" "Research & Development" ...
##  $ DistanceFromHome        : int  1 8 2 3 2 2 3 24 23 27 ...
##  $ Education               : int  2 1 2 4 1 2 3 1 3 3 ...
##  $ EducationField          : chr  "Life Sciences" "Life Sciences" "Other" "Life Sciences" ...
##  $ EmployeeCount           : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ EmployeeNumber          : int  1 2 4 5 7 8 10 11 12 13 ...
##  $ EnvironmentSatisfaction : int  2 3 4 4 1 4 3 4 4 3 ...
##  $ Gender                  : chr  "Female" "Male" "Male" "Female" ...
##  $ HourlyRate              : int  94 61 92 56 40 79 81 67 44 94 ...
##  $ JobInvolvement          : int  3 2 2 3 3 3 4 3 2 3 ...
##  $ JobLevel                : int  2 2 1 1 1 1 1 1 3 2 ...
##  $ JobRole                 : chr  "Sales Executive" "Research Scientist" "Laboratory Technician" "Research Scientist" ...
##  $ JobSatisfaction         : int  4 2 3 3 2 4 1 3 3 3 ...
##  $ MaritalStatus           : chr  "Single" "Married" "Single" "Married" ...
##  $ MonthlyIncome           : int  5993 5130 2090 2909 3468 3068 2670 2693 9526 5237 ...
##  $ MonthlyRate             : int  19479 24907 2396 23159 16632 11864 9964 13335 8787 16577 ...
##  $ NumCompaniesWorked      : int  8 1 6 1 9 0 4 1 0 6 ...
##  $ Over18                  : chr  "Y" "Y" "Y" "Y" ...
##  $ OverTime                : chr  "Yes" "No" "Yes" "Yes" ...
##  $ PercentSalaryHike       : int  11 23 15 11 12 13 20 22 21 13 ...
##  $ PerformanceRating       : int  3 4 3 3 3 3 4 4 4 3 ...
##  $ RelationshipSatisfaction: int  1 4 2 3 4 3 1 2 2 2 ...
##  $ StandardHours           : int  80 80 80 80 80 80 80 80 80 80 ...
##  $ StockOptionLevel        : int  0 1 0 0 1 0 3 1 0 2 ...
##  $ TotalWorkingYears       : int  8 10 7 8 6 8 12 1 10 17 ...
##  $ TrainingTimesLastYear   : int  0 3 3 3 3 2 3 2 2 3 ...
##  $ WorkLifeBalance         : int  1 3 3 3 3 2 2 3 3 2 ...
##  $ YearsAtCompany          : int  6 10 0 8 2 7 1 1 9 7 ...
##  $ YearsInCurrentRole      : int  4 7 0 7 2 7 0 0 7 7 ...
##  $ YearsSinceLastPromotion : int  0 1 0 3 2 3 0 0 1 7 ...
##  $ YearsWithCurrManager    : int  5 7 0 0 2 6 0 0 8 7 ...

# missing values
hr_data[!complete.cases(hr_data),]

colnames(hr_data)

##  [1] "Age"                      "Attrition"               
##  [3] "BusinessTravel"           "DailyRate"               
##  [5] "Department"               "DistanceFromHome"        
##  [7] "Education"                "EducationField"          
##  [9] "EmployeeCount"            "EmployeeNumber"          
## [11] "EnvironmentSatisfaction"  "Gender"                  
## [13] "HourlyRate"               "JobInvolvement"          
## [15] "JobLevel"                 "JobRole"                 
## [17] "JobSatisfaction"          "MaritalStatus"           
## [19] "MonthlyIncome"            "MonthlyRate"             
## [21] "NumCompaniesWorked"       "Over18"                  
## [23] "OverTime"                 "PercentSalaryHike"       
## [25] "PerformanceRating"        "RelationshipSatisfaction"
## [27] "StandardHours"            "StockOptionLevel"        
## [29] "TotalWorkingYears"        "TrainingTimesLastYear"   
## [31] "WorkLifeBalance"          "YearsAtCompany"          
## [33] "YearsInCurrentRole"       "YearsSinceLastPromotion" 
## [35] "YearsWithCurrManager"

summary(hr_data)

##       Age         Attrition         BusinessTravel       DailyRate     
##  Min.   :18.00   Length:1470        Length:1470        Min.   : 102.0  
##  1st Qu.:30.00   Class :character   Class :character   1st Qu.: 465.0  
##  Median :36.00   Mode  :character   Mode  :character   Median : 802.0  
##  Mean   :36.92                                         Mean   : 802.5  
##  3rd Qu.:43.00                                         3rd Qu.:1157.0  
##  Max.   :60.00                                         Max.   :1499.0  
##   Department        DistanceFromHome   Education     EducationField    
##  Length:1470        Min.   : 1.000   Min.   :1.000   Length:1470       
##  Class :character   1st Qu.: 2.000   1st Qu.:2.000   Class :character  
##  Mode  :character   Median : 7.000   Median :3.000   Mode  :character  
##                     Mean   : 9.193   Mean   :2.913                     
##                     3rd Qu.:14.000   3rd Qu.:4.000                     
##                     Max.   :29.000   Max.   :5.000                     
##  EmployeeCount EmployeeNumber   EnvironmentSatisfaction    Gender         
##  Min.   :1     Min.   :   1.0   Min.   :1.000           Length:1470       
##  1st Qu.:1     1st Qu.: 491.2   1st Qu.:2.000           Class :character  
##  Median :1     Median :1020.5   Median :3.000           Mode  :character  
##  Mean   :1     Mean   :1024.9   Mean   :2.722                             
##  3rd Qu.:1     3rd Qu.:1555.8   3rd Qu.:4.000                             
##  Max.   :1     Max.   :2068.0   Max.   :4.000                             
##    HourlyRate     JobInvolvement    JobLevel       JobRole         
##  Min.   : 30.00   Min.   :1.00   Min.   :1.000   Length:1470       
##  1st Qu.: 48.00   1st Qu.:2.00   1st Qu.:1.000   Class :character  
##  Median : 66.00   Median :3.00   Median :2.000   Mode  :character  
##  Mean   : 65.89   Mean   :2.73   Mean   :2.064                     
##  3rd Qu.: 83.75   3rd Qu.:3.00   3rd Qu.:3.000                     
##  Max.   :100.00   Max.   :4.00   Max.   :5.000                     
##  JobSatisfaction MaritalStatus      MonthlyIncome    MonthlyRate   
##  Min.   :1.000   Length:1470        Min.   : 1009   Min.   : 2094  
##  1st Qu.:2.000   Class :character   1st Qu.: 2911   1st Qu.: 8047  
##  Median :3.000   Mode  :character   Median : 4919   Median :14236  
##  Mean   :2.729                      Mean   : 6503   Mean   :14313  
##  3rd Qu.:4.000                      3rd Qu.: 8379   3rd Qu.:20462  
##  Max.   :4.000                      Max.   :19999   Max.   :26999  
##  NumCompaniesWorked    Over18            OverTime         PercentSalaryHike
##  Min.   :0.000      Length:1470        Length:1470        Min.   :11.00    
##  1st Qu.:1.000      Class :character   Class :character   1st Qu.:12.00    
##  Median :2.000      Mode  :character   Mode  :character   Median :14.00    
##  Mean   :2.693                                            Mean   :15.21    
##  3rd Qu.:4.000                                            3rd Qu.:18.00    
##  Max.   :9.000                                            Max.   :25.00    
##  PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
##  Min.   :3.000     Min.   :1.000            Min.   :80    Min.   :0.0000  
##  1st Qu.:3.000     1st Qu.:2.000            1st Qu.:80    1st Qu.:0.0000  
##  Median :3.000     Median :3.000            Median :80    Median :1.0000  
##  Mean   :3.154     Mean   :2.712            Mean   :80    Mean   :0.7939  
##  3rd Qu.:3.000     3rd Qu.:4.000            3rd Qu.:80    3rd Qu.:1.0000  
##  Max.   :4.000     Max.   :4.000            Max.   :80    Max.   :3.0000  
##  TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany  
##  Min.   : 0.00     Min.   :0.000         Min.   :1.000   Min.   : 0.000  
##  1st Qu.: 6.00     1st Qu.:2.000         1st Qu.:2.000   1st Qu.: 3.000  
##  Median :10.00     Median :3.000         Median :3.000   Median : 5.000  
##  Mean   :11.28     Mean   :2.799         Mean   :2.761   Mean   : 7.008  
##  3rd Qu.:15.00     3rd Qu.:3.000         3rd Qu.:3.000   3rd Qu.: 9.000  
##  Max.   :40.00     Max.   :6.000         Max.   :4.000   Max.   :40.000  
##  YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
##  Min.   : 0.000     Min.   : 0.000          Min.   : 0.000      
##  1st Qu.: 2.000     1st Qu.: 0.000          1st Qu.: 2.000      
##  Median : 3.000     Median : 1.000          Median : 3.000      
##  Mean   : 4.229     Mean   : 2.188          Mean   : 4.123      
##  3rd Qu.: 7.000     3rd Qu.: 3.000          3rd Qu.: 7.000      
##  Max.   :18.000     Max.   :15.000          Max.   :17.000

Attrition Distribution

Below plot shows the distribution of attrition, the employee leave the company is 236 out of 1470. This is not a balanced dataset.

counts <- table(hr_data$Attrition)
print(counts)

## 
##   No  Yes 
## 1233  237

counts_per <- round(counts / 1470 * 100,1)
print(counts_per)

## 
##   No  Yes 
## 83.9 16.1