Project Title: Employee Attrition Analysis
NAME: Siddhant Mishra
COLLEGE: IIT (BHU), Varanasi
The analysis has been carried out on a dataset having information about 1470 Employees of an organization
Setting Working Directory
setwd("E:/Internship/Tasks/Project")
Reading and Viewing Data
Employeedata <-read.csv(paste("HR-Employee-Attrition.csv",sep=""))
View(Employeedata)
dim(Employeedata)
## [1] 1470 35
Summary Statistics
summary(Employeedata)
## ï..Age Attrition BusinessTravel DailyRate
## Min. :18.00 No :1233 Non-Travel : 150 Min. : 102.0
## 1st Qu.:30.00 Yes: 237 Travel_Frequently: 277 1st Qu.: 465.0
## Median :36.00 Travel_Rarely :1043 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
## Human Resources : 63 Min. : 1.000 Min. :1.000
## Research & Development:961 1st Qu.: 2.000 1st Qu.:2.000
## Sales :446 Median : 7.000 Median :3.000
## Mean : 9.193 Mean :2.913
## 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :29.000 Max. :5.000
##
## EducationField EmployeeCount EmployeeNumber
## Human Resources : 27 Min. :1 Min. : 1.0
## Life Sciences :606 1st Qu.:1 1st Qu.: 491.2
## Marketing :159 Median :1 Median :1020.5
## Medical :464 Mean :1 Mean :1024.9
## Other : 82 3rd Qu.:1 3rd Qu.:1555.8
## Technical Degree:132 Max. :1 Max. :2068.0
##
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 Female:588 Min. : 30.00 Min. :1.00
## 1st Qu.:2.000 Male :882 1st Qu.: 48.00 1st Qu.:2.00
## Median :3.000 Median : 66.00 Median :3.00
## Mean :2.722 Mean : 65.89 Mean :2.73
## 3rd Qu.:4.000 3rd Qu.: 83.75 3rd Qu.:3.00
## Max. :4.000 Max. :100.00 Max. :4.00
##
## JobLevel JobRole JobSatisfaction
## Min. :1.000 Sales Executive :326 Min. :1.000
## 1st Qu.:1.000 Research Scientist :292 1st Qu.:2.000
## Median :2.000 Laboratory Technician :259 Median :3.000
## Mean :2.064 Manufacturing Director :145 Mean :2.729
## 3rd Qu.:3.000 Healthcare Representative:131 3rd Qu.:4.000
## Max. :5.000 Manager :102 Max. :4.000
## (Other) :215
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked
## Divorced:327 Min. : 1009 Min. : 2094 Min. :0.000
## Married :673 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000
## Single :470 Median : 4919 Median :14236 Median :2.000
## Mean : 6503 Mean :14313 Mean :2.693
## 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000
## Max. :19999 Max. :26999 Max. :9.000
##
## Over18 OverTime PercentSalaryHike PerformanceRating
## Y:1470 No :1054 Min. :11.00 Min. :3.000
## Yes: 416 1st Qu.:12.00 1st Qu.:3.000
## Median :14.00 Median :3.000
## Mean :15.21 Mean :3.154
## 3rd Qu.:18.00 3rd Qu.:3.000
## Max. :25.00 Max. :4.000
##
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## Min. :1.000 Min. :80 Min. :0.0000 Min. : 0.00
## 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.00
## Median :3.000 Median :80 Median :1.0000 Median :10.00
## Mean :2.712 Mean :80 Mean :0.7939 Mean :11.28
## 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.:15.00
## Max. :4.000 Max. :80 Max. :3.0000 Max. :40.00
##
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :0.000 Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.000 Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.799 Mean :2.761 Mean : 7.008 Mean : 4.229
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :6.000 Max. :4.000 Max. :40.000 Max. :18.000
##
## YearsSinceLastPromotion YearsWithCurrManager
## Min. : 0.000 Min. : 0.000
## 1st Qu.: 0.000 1st Qu.: 2.000
## Median : 1.000 Median : 3.000
## Mean : 2.188 Mean : 4.123
## 3rd Qu.: 3.000 3rd Qu.: 7.000
## Max. :15.000 Max. :17.000
##
Summary Statstics using Describe()
library("psych", lib.loc="~/R/win-library/3.4")
describe(Employeedata)
## vars n mean sd median trimmed
## ï..Age 1 1470 36.92 9.14 36.0 36.47
## Attrition* 2 1470 1.16 0.37 1.0 1.08
## BusinessTravel* 3 1470 2.61 0.67 3.0 2.76
## DailyRate 4 1470 802.49 403.51 802.0 803.83
## Department* 5 1470 2.26 0.53 2.0 2.25
## DistanceFromHome 6 1470 9.19 8.11 7.0 8.08
## Education 7 1470 2.91 1.02 3.0 2.98
## EducationField* 8 1470 3.25 1.33 3.0 3.10
## EmployeeCount 9 1470 1.00 0.00 1.0 1.00
## EmployeeNumber 10 1470 1024.87 602.02 1020.5 1023.40
## EnvironmentSatisfaction 11 1470 2.72 1.09 3.0 2.78
## Gender* 12 1470 1.60 0.49 2.0 1.62
## HourlyRate 13 1470 65.89 20.33 66.0 66.02
## JobInvolvement 14 1470 2.73 0.71 3.0 2.74
## JobLevel 15 1470 2.06 1.11 2.0 1.90
## JobRole* 16 1470 5.46 2.46 6.0 5.61
## JobSatisfaction 17 1470 2.73 1.10 3.0 2.79
## MaritalStatus* 18 1470 2.10 0.73 2.0 2.12
## MonthlyIncome 19 1470 6502.93 4707.96 4919.0 5667.24
## MonthlyRate 20 1470 14313.10 7117.79 14235.5 14286.48
## NumCompaniesWorked 21 1470 2.69 2.50 2.0 2.36
## Over18* 22 1470 1.00 0.00 1.0 1.00
## OverTime* 23 1470 1.28 0.45 1.0 1.23
## PercentSalaryHike 24 1470 15.21 3.66 14.0 14.80
## PerformanceRating 25 1470 3.15 0.36 3.0 3.07
## RelationshipSatisfaction 26 1470 2.71 1.08 3.0 2.77
## StandardHours 27 1470 80.00 0.00 80.0 80.00
## StockOptionLevel 28 1470 0.79 0.85 1.0 0.67
## TotalWorkingYears 29 1470 11.28 7.78 10.0 10.37
## TrainingTimesLastYear 30 1470 2.80 1.29 3.0 2.72
## WorkLifeBalance 31 1470 2.76 0.71 3.0 2.77
## YearsAtCompany 32 1470 7.01 6.13 5.0 5.99
## YearsInCurrentRole 33 1470 4.23 3.62 3.0 3.85
## YearsSinceLastPromotion 34 1470 2.19 3.22 1.0 1.48
## YearsWithCurrManager 35 1470 4.12 3.57 3.0 3.77
## mad min max range skew kurtosis se
## ï..Age 8.90 18 60 42 0.41 -0.41 0.24
## Attrition* 0.00 1 2 1 1.84 1.39 0.01
## BusinessTravel* 0.00 1 3 2 -1.44 0.69 0.02
## DailyRate 510.01 102 1499 1397 0.00 -1.21 10.52
## Department* 0.00 1 3 2 0.17 -0.40 0.01
## DistanceFromHome 7.41 1 29 28 0.96 -0.23 0.21
## Education 1.48 1 5 4 -0.29 -0.56 0.03
## EducationField* 1.48 1 6 5 0.55 -0.69 0.03
## EmployeeCount 0.00 1 1 0 NaN NaN 0.00
## EmployeeNumber 790.97 1 2068 2067 0.02 -1.23 15.70
## EnvironmentSatisfaction 1.48 1 4 3 -0.32 -1.20 0.03
## Gender* 0.00 1 2 1 -0.41 -1.83 0.01
## HourlyRate 26.69 30 100 70 -0.03 -1.20 0.53
## JobInvolvement 0.00 1 4 3 -0.50 0.26 0.02
## JobLevel 1.48 1 5 4 1.02 0.39 0.03
## JobRole* 2.97 1 9 8 -0.36 -1.20 0.06
## JobSatisfaction 1.48 1 4 3 -0.33 -1.22 0.03
## MaritalStatus* 1.48 1 3 2 -0.15 -1.12 0.02
## MonthlyIncome 3260.24 1009 19999 18990 1.37 0.99 122.79
## MonthlyRate 9201.76 2094 26999 24905 0.02 -1.22 185.65
## NumCompaniesWorked 1.48 0 9 9 1.02 0.00 0.07
## Over18* 0.00 1 1 0 NaN NaN 0.00
## OverTime* 0.00 1 2 1 0.96 -1.07 0.01
## PercentSalaryHike 2.97 11 25 14 0.82 -0.31 0.10
## PerformanceRating 0.00 3 4 1 1.92 1.68 0.01
## RelationshipSatisfaction 1.48 1 4 3 -0.30 -1.19 0.03
## StandardHours 0.00 80 80 0 NaN NaN 0.00
## StockOptionLevel 1.48 0 3 3 0.97 0.35 0.02
## TotalWorkingYears 5.93 0 40 40 1.11 0.91 0.20
## TrainingTimesLastYear 1.48 0 6 6 0.55 0.48 0.03
## WorkLifeBalance 0.00 1 4 3 -0.55 0.41 0.02
## YearsAtCompany 4.45 0 40 40 1.76 3.91 0.16
## YearsInCurrentRole 4.45 0 18 18 0.92 0.47 0.09
## YearsSinceLastPromotion 1.48 0 15 15 1.98 3.59 0.08
## YearsWithCurrManager 4.45 0 17 17 0.83 0.16 0.09
Reading more details about Data
One-Way Contingency tables
Attrition Count
mytable <-xtabs(~Attrition, data =Employeedata)
mytable
## Attrition
## No Yes
## 1233 237
Type of Travelling Involved
mytable <-xtabs(~BusinessTravel, data =Employeedata)
mytable
## BusinessTravel
## Non-Travel Travel_Frequently Travel_Rarely
## 150 277 1043
Number of Employees in Different Department
mytable <-xtabs(~Department, data =Employeedata)
mytable
## Department
## Human Resources Research & Development Sales
## 63 961 446
Type of Education of Employee
mytable <-xtabs(~Education, data =Employeedata)
mytable
## Education
## 1 2 3 4 5
## 170 282 572 398 48
Different EducationField of Employees
mytable <-xtabs(~EducationField, data =Employeedata)
mytable
## EducationField
## Human Resources Life Sciences Marketing Medical
## 27 606 159 464
## Other Technical Degree
## 82 132
Types of EnvironmentSatisfaction of Employees
mytable <-xtabs(~EnvironmentSatisfaction, data =Employeedata)
mytable
## EnvironmentSatisfaction
## 1 2 3 4
## 284 287 453 446
Number of Males and Females in Company
mytable <-xtabs(~Gender, data =Employeedata)
mytable
## Gender
## Female Male
## 588 882
Kinds of JobInvolvement of Employee in Company
mytable <-xtabs(~JobInvolvement, data =Employeedata)
mytable
## JobInvolvement
## 1 2 3 4
## 83 375 868 144
Types of Job Level
mytable <-xtabs(~JobLevel,data=Employeedata)
mytable
## JobLevel
## 1 2 3 4 5
## 543 534 218 106 69
Types of Job Role
mytable <-xtabs(~JobRole,data=Employeedata)
mytable
## JobRole
## Healthcare Representative Human Resources
## 131 52
## Laboratory Technician Manager
## 259 102
## Manufacturing Director Research Director
## 145 80
## Research Scientist Sales Executive
## 292 326
## Sales Representative
## 83
Types of JobSatisfaction
mytable <-xtabs(~JobSatisfaction,data=Employeedata)
mytable
## JobSatisfaction
## 1 2 3 4
## 289 280 442 459
Employees Maried or Not
mytable <-xtabs(~MaritalStatus,data=Employeedata)
mytable
## MaritalStatus
## Divorced Married Single
## 327 673 470
Over 18 or not
mytable <-xtabs(~Over18,data=Employeedata)
mytable
## Over18
## Y
## 1470
How many Employees do Overtime
mytable <-xtabs(~OverTime,data=Employeedata)
mytable
## OverTime
## No Yes
## 1054 416
Distribution of Perforamce Rating
mytable <-xtabs(~PerformanceRating,data=Employeedata)
mytable
## PerformanceRating
## 3 4
## 1244 226
Distribution of RealtionshipSatisfaction
mytable <-xtabs(~RelationshipSatisfaction, data=Employeedata)
mytable
## RelationshipSatisfaction
## 1 2 3 4
## 276 303 459 432
Standard Hours of Employees
mytable <-xtabs(~StandardHours, data=Employeedata)
mytable
## StandardHours
## 80
## 1470
Type of StockOptionsGiven
mytable <-xtabs(~StockOptionLevel, data=Employeedata)
mytable
## StockOptionLevel
## 0 1 2 3
## 631 596 158 85
Work Life Balance
mytable <-xtabs(~WorkLifeBalance, data = Employeedata)
mytable
## WorkLifeBalance
## 1 2 3 4
## 80 344 893 153
Two Way Contingency Tables
Effect of Travel on Attrition
mytable <-xtabs(~BusinessTravel + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## BusinessTravel No Yes
## Non-Travel 0.9200000 0.0800000
## Travel_Frequently 0.7509025 0.2490975
## Travel_Rarely 0.8504314 0.1495686
Finding: Seems like Employee Facing heavy travel has more attrition rate
Departmentwise Attrition Rate
mytable <-xtabs(~Department + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## Department No Yes
## Human Resources 0.8095238 0.1904762
## Research & Development 0.8616025 0.1383975
## Sales 0.7937220 0.2062780
Finding: Attrition rate does not very much across different departments
Educationwise Attrition Rate
mytable <-xtabs(~Education + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## Education No Yes
## 1 0.8176471 0.1823529
## 2 0.8439716 0.1560284
## 3 0.8269231 0.1730769
## 4 0.8542714 0.1457286
## 5 0.8958333 0.1041667
Finding: Attrition rate does not very much across different Education
EducationField wise Attrition Rate
mytable <-xtabs(~EducationField + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## EducationField No Yes
## Human Resources 0.7407407 0.2592593
## Life Sciences 0.8531353 0.1468647
## Marketing 0.7798742 0.2201258
## Medical 0.8642241 0.1357759
## Other 0.8658537 0.1341463
## Technical Degree 0.7575758 0.2424242
Finding: Attrition rate donot vary much between different EducationField
Effect of EnvironmentSatisfaction on Attrition
mytable <-xtabs(~EnvironmentSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## EnvironmentSatisfaction No Yes
## 1 0.7464789 0.2535211
## 2 0.8501742 0.1498258
## 3 0.8631347 0.1368653
## 4 0.8654709 0.1345291
Finding: Lower Environment Satisfaction rate increases Attrition Rate
Effect of Gender on Attrition Rate
mytable <-xtabs(~Gender + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## Gender No Yes
## Female 0.8520408 0.1479592
## Male 0.8299320 0.1700680
Finding: So Gender doesnot effect Attrition Rate much
Effect of JobInvolvement on Attrition Rate
mytable <-xtabs(~JobInvolvement + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## JobInvolvement No Yes
## 1 0.66265060 0.33734940
## 2 0.81066667 0.18933333
## 3 0.85599078 0.14400922
## 4 0.90972222 0.09027778
Finding: Level of JobInvolvement also plays a good role in attrition rate. High Job Involvemnet reduces attrition rate
Effect of JobLevel on Attrition Rate
mytable <-xtabs(~JobLevel + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## JobLevel No Yes
## 1 0.73664825 0.26335175
## 2 0.90262172 0.09737828
## 3 0.85321101 0.14678899
## 4 0.95283019 0.04716981
## 5 0.92753623 0.07246377
Finding: Level of JobLevel also plays a role in attrition rate as percentage of attrition is maximum for lowest JobLevl Employees but it is not that strict for further levels
Effect of JobRole on Attrition Rate
mytable <-xtabs(~JobRole + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## JobRole No Yes
## Healthcare Representative 0.93129771 0.06870229
## Human Resources 0.76923077 0.23076923
## Laboratory Technician 0.76061776 0.23938224
## Manager 0.95098039 0.04901961
## Manufacturing Director 0.93103448 0.06896552
## Research Director 0.97500000 0.02500000
## Research Scientist 0.83904110 0.16095890
## Sales Executive 0.82515337 0.17484663
## Sales Representative 0.60240964 0.39759036
Finding: Attrition Rate depend on JobRole, some JobRole have 39% Attrition Rate while some JobRole have 4% Attrition Rate
Effect of JobSatisfaction on Attrition Rate
mytable <-xtabs(~JobSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## JobSatisfaction No Yes
## 1 0.7716263 0.2283737
## 2 0.8357143 0.1642857
## 3 0.8348416 0.1651584
## 4 0.8867102 0.1132898
Finding: Higher Job Satisfaction Rate decreases attrition rate
Effect of MaritalStatus on Attrition Rate
mytable <-xtabs(~MaritalStatus + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## MaritalStatus No Yes
## Divorced 0.8990826 0.1009174
## Married 0.8751857 0.1248143
## Single 0.7446809 0.2553191
Finding: Divorced People have lowest Attrition Rate
Effect of OverTime on Attrition Rate
mytable <-xtabs(~OverTime + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## OverTime No Yes
## No 0.8956357 0.1043643
## Yes 0.6947115 0.3052885
Finding: People doing more OverTime have higher Attrition Rate
Finding: PerformanceRating does not effect Attrition Rate much
Effect of RelationshipSatisfaction on Attrition Rate
mytable <-xtabs(~RelationshipSatisfaction + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## RelationshipSatisfaction No Yes
## 1 0.7934783 0.2065217
## 2 0.8514851 0.1485149
## 3 0.8453159 0.1546841
## 4 0.8518519 0.1481481
Finding: Bad RelationshipSatisfaction increases Attrition Rate
Effect of StockOptionLevel on Attrition Rate
mytable <-xtabs(~StockOptionLevel + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## StockOptionLevel No Yes
## 0 0.75594295 0.24405705
## 1 0.90604027 0.09395973
## 2 0.92405063 0.07594937
## 3 0.82352941 0.17647059
Finding: StockoptionLeveldont have any definite effect on Attrition rate
Effect of WorkLifeBalance on Attrition Rate
mytable <-xtabs(~WorkLifeBalance + Attrition, data =Employeedata)
prop.table(mytable,1)
## Attrition
## WorkLifeBalance No Yes
## 1 0.6875000 0.3125000
## 2 0.8313953 0.1686047
## 3 0.8577828 0.1422172
## 4 0.8235294 0.1764706
Finding: Poor Worklifebalance increases Attrition Rate
Boxplots
Boxplot to see distribution of Age
boxplot(Employeedata$ï..Age,main="Boxplot Showing Variation of Age")
mean(Employeedata$ï..Age)
## [1] 36.92381
Boxplot to see Variation of DailyRate
boxplot(Employeedata$DailyRate,main="Boxplot Showing Variation of DailyRate")
mean(Employeedata$DailyRate)
## [1] 802.4857
Boxplot to see Variation of DistanceFromHome
boxplot(Employeedata$DistanceFromHome,ylim=c(-5,20),main="Boxplot Showing Variation of DistanceFromHome")
mean(Employeedata$DistanceFromHome)
## [1] 9.192517
Boxplot to see Variation of HourlyRate
boxplot(Employeedata$HourlyRate,main="Boxplot Showing Variation of HourlyRate")
mean(Employeedata$HourlyRate)
## [1] 65.89116
Boxplot to see Variation of MonthlyIncome
boxplot(Employeedata$MonthlyIncome,ylim=c(0,17000),main="Boxplot Showing Variation of MontlyIncome")
mean(Employeedata$MonthlyIncome)
## [1] 6502.931
Boxplot to see Variation of MonthlyRate
boxplot(Employeedata$MonthlyRate,ylim=c(),main="Boxplot Showing Variation of MonthlyRate")
mean(Employeedata$MonthlyRate)
## [1] 14313.1
Boxplot to see Variation of Number Of Companies Worked
boxplot(Employeedata$NumCompaniesWorked,ylim=c(),main="Boxplot Showing Variation of Number of Companies Worked")
mean(Employeedata$NumCompaniesWorked)
## [1] 2.693197
Boxplot to see Distribution of PercentSalaryHike
boxplot(Employeedata$PercentSalaryHike,ylim=c(),main="Boxplot Showing Variation of Number of Companies Worked")
mean(Employeedata$PercentSalaryHike)
## [1] 15.20952
Boxplot to see Distribution of Total Working Years
boxplot(Employeedata$TotalWorkingYears,ylim=c(),main="Boxplot Showing Variation of Total Working Years of Employee")
mean(Employeedata$TotalWorkingYears)
## [1] 11.27959
Boxplot to see Distribution of Training Time Last Year
boxplot(Employeedata$TrainingTimesLastYear,ylim=c(),main="Boxplot Showing Variation of Training Time Last Year of Employee")
mean(Employeedata$TrainingTimesLastYear)
## [1] 2.79932
Boxplot to see Variation Of Years At Company
boxplot(Employeedata$YearsAtCompany,ylim=c(0,20),main="Boxplot Showing Variation of Years At Company for Employee")
mean(Employeedata$YearsAtCompany)
## [1] 7.008163
Boxplot to see Variation Of Years in Current Role
boxplot(Employeedata$YearsInCurrentRole,ylim=c(),main="Boxplot Showing Variation of Years In Current Role for Employee")
mean(Employeedata$YearsInCurrentRole)
## [1] 4.229252
Boxplot to see Variation Of Years With Current Manager
boxplot(Employeedata$YearsWithCurrManager,ylim=c(),main="Boxplot Showing Variation of Years With Current Manager")
mean(Employeedata$YearsWithCurrManager)
## [1] 4.123129
Histograms
Histogram showing Attrition Rate
plot(Employeedata$Attrition,xlim=c(),ylim=c(0,1500),ylab="Number Of Employee",main="Attrition Rate Counts")
Histogram showing BusinessTravel Variance
plot(Employeedata$BusinessTravel,xlim=c(),ylim=c(),ylab="Count Of Employee",main="Variation Of Travel")
Variance of Department
plot(Employeedata$Department,xlim=c(),ylim=c(),ylab="Count Of Employee",main="Variation Across Department")
Histogram showing Education Background
hist(Employeedata$Education,col='grey')
Variation of Different Education Field
plot(Employeedata$EducationField, ylab="Number OF Employees",main="Employees in Departments")
Histogram showing Environment Satisfaction
hist(Employeedata$EnvironmentSatisfaction,col='grey')
Histogram Showing Hourly Rate
hist(Employeedata$HourlyRate,col='grey')
JobInvolvement Variance
hist(Employeedata$JobInvolvement,col='grey')
JobLevel Variance
hist(Employeedata$JobLevel,col='grey')
JobSatisfaction Variance
hist(Employeedata$JobSatisfaction,col='grey',main="Employee Satisfaction Variance")
Monthly Income Variance
hist(Employeedata$MonthlyIncome,breaks=20,col='grey',xlim=c(0,20000))
Monthly Rate Variance
hist(Employeedata$MonthlyRate,col='grey',xlim=c(0,30000))
Number of Companies Worked
hist(Employeedata$NumCompaniesWorked,col='grey')
Percentage Hike Given Variation
hist(Employeedata$PercentSalaryHike,col='grey')
Distribution Of Relationship Satisfaction
hist(Employeedata$RelationshipSatisfaction,col='grey')
Distribution Of StockOptionLevel
hist(Employeedata$StockOptionLevel,col='grey')
Distribution Of TotalWorking years
hist(Employeedata$TotalWorkingYears,col='grey', main="Employee Working Years",breaks=25)
TrainingTime Last Year
hist(Employeedata$TrainingTimesLastYear,col='grey', main="Employee's Training Time Last Year")
Variance Of WorkLifeBalance among Employees
hist(Employeedata$WorkLifeBalance,col='grey', main="Employee's Work Life Balance Ratings")
Variation of YearsAtCompany
hist(Employeedata$YearsAtCompany,col='grey', main="Employee's Years At Company")
Variation of YearsInCurrentRole
hist(Employeedata$YearsInCurrentRole,col='grey',xlim=c(0,20),main="Employee's Years In Current Role")
Variation of Employee’s Years With Current Manager
hist(Employeedata$YearsWithCurrManager,col='grey',main="Employee's Years With current Managers")
Converting Character Variable into Numeric Vraiables
Employeedata$Attrition1[Employeedata$Attrition== "Yes"] <-1
Employeedata$Attrition1[Employeedata$Attrition== "No"] <-0
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Non-Travel"] <-1
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Travel_Rarely"] <-2
Employeedata$BusinessTravel1[Employeedata$BusinessTravel== "Travel_Frequently"] <-3
Employeedata$Department1[Employeedata$Department== "Human Resources"] <-1
Employeedata$Department1[Employeedata$Department== "Research & Development"] <-2
Employeedata$Department1[Employeedata$Department== "Sales"] <-3
Employeedata$EducationField1[Employeedata$EducationField== "Human Resources"] <-1
Employeedata$EducationField1[Employeedata$EducationField== "Life Sciences"] <-2
Employeedata$EducationField1[Employeedata$EducationField== "Marketing"] <-3
Employeedata$EducationField1[Employeedata$EducationField== "Medical"] <-4
Employeedata$EducationField1[Employeedata$EducationField== "Other"] <-5
Employeedata$EducationField1[Employeedata$EducationField== "Technical Degree"] <-6
Employeedata$Gender1[Employeedata$Gender== "Female"] <-1
Employeedata$Gender1[Employeedata$Gender== "Male"] <-2
Employeedata$JobRole1[Employeedata$JobRole== "Human Resources"] <-1
Employeedata$JobRole1[Employeedata$JobRole== "Healthcare Representative"] <-2
Employeedata$JobRole1[Employeedata$JobRole== "Laboratory Technician"] <-3
Employeedata$JobRole1[Employeedata$JobRole== "Manager"] <-4
Employeedata$JobRole1[Employeedata$JobRole== "Manufacturing Director"] <-5
Employeedata$JobRole1[Employeedata$JobRole== "Research Director"] <-6
Employeedata$JobRole1[Employeedata$JobRole== "Research Scientist"] <-7
Employeedata$JobRole1[Employeedata$JobRole== "Sales Executive"] <-8
Employeedata$JobRole1[Employeedata$JobRole== "Sales Representative"] <-9
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Divorced"] <-1
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Married"] <-2
Employeedata$MaritalStatus1[Employeedata$MaritalStatus== "Single"] <-3
Employeedata$OverTime1[Employeedata$OverTime== "Yes"] <-1
Employeedata$OverTime1[Employeedata$OverTime== "No"] <-0
dim(Employeedata)
## [1] 1470 43
Creating a Data Frame “Num” containing only numeric relevant variable
Num <- subset(Employeedata, select = c(1,4,6,7,11,13,14,15,17,19,20,21,24,25,26,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43))
View(Num)
Creating Covariance Correlation Matrix
cor(Num,use="complete")
## ï..Age DailyRate DistanceFromHome
## ï..Age 1.000000000 0.0106609426 -0.001686120
## DailyRate 0.010660943 1.0000000000 -0.004985337
## DistanceFromHome -0.001686120 -0.0049853374 1.000000000
## Education 0.208033731 -0.0168064332 0.021041826
## EnvironmentSatisfaction 0.010146428 0.0183548543 -0.016075327
## HourlyRate 0.024286543 0.0233814215 0.031130586
## JobInvolvement 0.029819959 0.0461348740 0.008783280
## JobLevel 0.509604228 0.0029663349 0.005302731
## JobSatisfaction -0.004891877 0.0305710078 -0.003668839
## MonthlyIncome 0.497854567 0.0077070589 -0.017014445
## MonthlyRate 0.028051167 -0.0321816015 0.027472864
## NumCompaniesWorked 0.299634758 0.0381534343 -0.029250804
## PercentSalaryHike 0.003633585 0.0227036775 0.040235377
## PerformanceRating 0.001903896 0.0004732963 0.027109618
## RelationshipSatisfaction 0.053534720 0.0078460310 0.006557475
## StockOptionLevel 0.037509712 0.0421427964 0.044871999
## TotalWorkingYears 0.680380536 0.0145147387 0.004628426
## TrainingTimesLastYear -0.019620819 0.0024525427 -0.036942234
## WorkLifeBalance -0.021490028 -0.0378480510 -0.026556004
## YearsAtCompany 0.311308770 -0.0340547676 0.009507720
## YearsInCurrentRole 0.212901056 0.0099320150 0.018844999
## YearsSinceLastPromotion 0.216513368 -0.0332289848 0.010028836
## YearsWithCurrManager 0.202088602 -0.0263631782 0.014406048
## Attrition1 -0.159205007 -0.0566519919 0.077923583
## BusinessTravel1 -0.011807332 -0.0155388905 -0.009696041
## Department1 -0.031882283 0.0071087137 0.017224804
## EducationField1 -0.040872848 0.0377092287 0.002013453
## Gender1 -0.036310550 -0.0117161379 -0.001850528
## JobRole1 -0.112807055 -0.0033580191 0.003574933
## MaritalStatus1 -0.095028910 -0.0695856414 -0.014437031
## OverTime1 0.028062357 0.0091349699 0.025513635
## Education EnvironmentSatisfaction
## ï..Age 0.208033731 0.0101464279
## DailyRate -0.016806433 0.0183548543
## DistanceFromHome 0.021041826 -0.0160753270
## Education 1.000000000 -0.0271283133
## EnvironmentSatisfaction -0.027128313 1.0000000000
## HourlyRate 0.016774829 -0.0498569562
## JobInvolvement 0.042437634 -0.0082775982
## JobLevel 0.101588886 0.0012116994
## JobSatisfaction -0.011296117 -0.0067843526
## MonthlyIncome 0.094960677 -0.0062590878
## MonthlyRate -0.026084197 0.0375996229
## NumCompaniesWorked 0.126316560 0.0125943232
## PercentSalaryHike -0.011110941 -0.0317011952
## PerformanceRating -0.024538791 -0.0295479523
## RelationshipSatisfaction -0.009118377 0.0076653835
## StockOptionLevel 0.018422220 0.0034321578
## TotalWorkingYears 0.148279697 -0.0026930704
## TrainingTimesLastYear -0.025100241 -0.0193593083
## WorkLifeBalance 0.009819189 0.0276272955
## YearsAtCompany 0.069113696 0.0014575492
## YearsInCurrentRole 0.060235554 0.0180074601
## YearsSinceLastPromotion 0.054254334 0.0161936056
## YearsWithCurrManager 0.069065378 -0.0049987226
## Attrition1 -0.031372820 -0.1033689783
## BusinessTravel1 -0.008669840 -0.0113099347
## Department1 0.007996422 -0.0193952706
## EducationField1 -0.039592150 0.0431634907
## Gender1 -0.016546827 0.0005083139
## JobRole1 0.007721031 -0.0145621843
## MaritalStatus1 0.004052654 -0.0035934733
## OverTime1 -0.020321767 0.0701317268
## HourlyRate JobInvolvement JobLevel
## ï..Age 0.0242865426 0.029819959 0.5096042284
## DailyRate 0.0233814215 0.046134874 0.0029663349
## DistanceFromHome 0.0311305856 0.008783280 0.0053027306
## Education 0.0167748289 0.042437634 0.1015888862
## EnvironmentSatisfaction -0.0498569562 -0.008277598 0.0012116994
## HourlyRate 1.0000000000 0.042860641 -0.0278534864
## JobInvolvement 0.0428606410 1.000000000 -0.0126298827
## JobLevel -0.0278534864 -0.012629883 1.0000000000
## JobSatisfaction -0.0713346244 -0.021475910 -0.0019437080
## MonthlyIncome -0.0157943044 -0.015271491 0.9502999135
## MonthlyRate -0.0152967496 -0.016322079 0.0395629510
## NumCompaniesWorked 0.0221568834 0.015012413 0.1425011238
## PercentSalaryHike -0.0090619863 -0.017204572 -0.0347304923
## PerformanceRating -0.0021716974 -0.029071333 -0.0212220821
## RelationshipSatisfaction 0.0013304528 0.034296821 0.0216415105
## StockOptionLevel 0.0502633991 0.021522640 0.0139839105
## TotalWorkingYears -0.0023336818 -0.005533182 0.7822078045
## TrainingTimesLastYear -0.0085476852 -0.015337826 -0.0181905502
## WorkLifeBalance -0.0046072338 -0.014616593 0.0378177456
## YearsAtCompany -0.0195816162 -0.021355427 0.5347386874
## YearsInCurrentRole -0.0241062202 0.008716963 0.3894467329
## YearsSinceLastPromotion -0.0267155861 -0.024184292 0.3538853470
## YearsWithCurrManager -0.0201232002 0.025975808 0.3752806078
## Attrition1 -0.0068455496 -0.130015957 -0.1691047509
## BusinessTravel1 -0.0041639831 0.029299959 -0.0116958303
## Department1 -0.0041437079 -0.024586062 0.1019631058
## EducationField1 -0.0219412191 -0.002655278 -0.0449326718
## Gender1 -0.0004782971 0.017959755 -0.0394031027
## JobRole1 -0.0165515330 0.007401840 -0.0668959855
## MaritalStatus1 -0.0178605059 -0.038497019 -0.0767694781
## OverTime1 -0.0077819744 -0.003506711 0.0005440478
## JobSatisfaction MonthlyIncome MonthlyRate
## ï..Age -0.0048918771 0.497854567 0.0280511671
## DailyRate 0.0305710078 0.007707059 -0.0321816015
## DistanceFromHome -0.0036688392 -0.017014445 0.0274728635
## Education -0.0112961167 0.094960677 -0.0260841972
## EnvironmentSatisfaction -0.0067843526 -0.006259088 0.0375996229
## HourlyRate -0.0713346244 -0.015794304 -0.0152967496
## JobInvolvement -0.0214759103 -0.015271491 -0.0163220791
## JobLevel -0.0019437080 0.950299913 0.0395629510
## JobSatisfaction 1.0000000000 -0.007156742 0.0006439169
## MonthlyIncome -0.0071567424 1.000000000 0.0348136261
## MonthlyRate 0.0006439169 0.034813626 1.0000000000
## NumCompaniesWorked -0.0556994260 0.149515216 0.0175213534
## PercentSalaryHike 0.0200020394 -0.027268586 -0.0064293459
## PerformanceRating 0.0022971971 -0.017120138 -0.0098114285
## RelationshipSatisfaction -0.0124535932 0.025873436 -0.0040853293
## StockOptionLevel 0.0106902261 0.005407677 -0.0343228302
## TotalWorkingYears -0.0201850727 0.772893246 0.0264424712
## TrainingTimesLastYear -0.0057793350 -0.021736277 0.0014668806
## WorkLifeBalance -0.0194587102 0.030683082 0.0079631575
## YearsAtCompany -0.0038026279 0.514284826 -0.0236551067
## YearsInCurrentRole -0.0023047852 0.363817667 -0.0128148744
## YearsSinceLastPromotion -0.0182135678 0.344977638 0.0015667995
## YearsWithCurrManager -0.0276562139 0.344078883 -0.0367459053
## Attrition1 -0.1034811261 -0.159839582 0.0151702125
## BusinessTravel1 0.0086659585 -0.013449947 -0.0084404916
## Department1 0.0210008789 0.053129698 0.0236420721
## EducationField1 -0.0344008137 -0.041070150 -0.0271816823
## Gender1 0.0332516974 -0.031858492 -0.0414822051
## JobRole1 0.0224686645 -0.082090118 0.0080868505
## MaritalStatus1 0.0243599530 -0.075449582 0.0239374266
## OverTime1 0.0245394811 0.006089285 0.0214311446
## NumCompaniesWorked PercentSalaryHike
## ï..Age 0.299634758 0.003633585
## DailyRate 0.038153434 0.022703677
## DistanceFromHome -0.029250804 0.040235377
## Education 0.126316560 -0.011110941
## EnvironmentSatisfaction 0.012594323 -0.031701195
## HourlyRate 0.022156883 -0.009061986
## JobInvolvement 0.015012413 -0.017204572
## JobLevel 0.142501124 -0.034730492
## JobSatisfaction -0.055699426 0.020002039
## MonthlyIncome 0.149515216 -0.027268586
## MonthlyRate 0.017521353 -0.006429346
## NumCompaniesWorked 1.000000000 -0.010238309
## PercentSalaryHike -0.010238309 1.000000000
## PerformanceRating -0.014094873 0.773549996
## RelationshipSatisfaction 0.052733049 -0.040490081
## StockOptionLevel 0.030075475 0.007527748
## TotalWorkingYears 0.237638590 -0.020608488
## TrainingTimesLastYear -0.066054072 -0.005221012
## WorkLifeBalance -0.008365685 -0.003279636
## YearsAtCompany -0.118421340 -0.035991262
## YearsInCurrentRole -0.090753934 -0.001520027
## YearsSinceLastPromotion -0.036813892 -0.022154313
## YearsWithCurrManager -0.110319155 -0.011985248
## Attrition1 0.043493739 -0.013478202
## BusinessTravel1 -0.030742984 -0.025726943
## Department1 -0.035881612 -0.007840161
## EducationField1 -0.008663043 -0.011213502
## Gender1 -0.039147450 0.002732648
## JobRole1 -0.056838388 0.003231282
## MaritalStatus1 -0.035505389 0.012492329
## OverTime1 -0.020785821 -0.005432827
## PerformanceRating RelationshipSatisfaction
## ï..Age 0.0019038955 0.0535347197
## DailyRate 0.0004732963 0.0078460310
## DistanceFromHome 0.0271096185 0.0065574746
## Education -0.0245387912 -0.0091183767
## EnvironmentSatisfaction -0.0295479523 0.0076653835
## HourlyRate -0.0021716974 0.0013304528
## JobInvolvement -0.0290713334 0.0342968206
## JobLevel -0.0212220821 0.0216415105
## JobSatisfaction 0.0022971971 -0.0124535932
## MonthlyIncome -0.0171201382 0.0258734361
## MonthlyRate -0.0098114285 -0.0040853293
## NumCompaniesWorked -0.0140948728 0.0527330486
## PercentSalaryHike 0.7735499964 -0.0404900811
## PerformanceRating 1.0000000000 -0.0313514554
## RelationshipSatisfaction -0.0313514554 1.0000000000
## StockOptionLevel 0.0035064716 -0.0459524907
## TotalWorkingYears 0.0067436679 0.0240542918
## TrainingTimesLastYear -0.0155788817 0.0024965264
## WorkLifeBalance 0.0025723613 0.0196044057
## YearsAtCompany 0.0034351261 0.0193667869
## YearsInCurrentRole 0.0349862604 -0.0151229149
## YearsSinceLastPromotion 0.0178960661 0.0334925021
## YearsWithCurrManager 0.0228271689 -0.0008674968
## Attrition1 0.0028887517 -0.0458722789
## BusinessTravel1 0.0016833372 0.0089263034
## Department1 -0.0246035428 -0.0224144254
## EducationField1 -0.0056142134 -0.0043777110
## Gender1 -0.0138590184 0.0228683700
## JobRole1 -0.0244593806 -0.0250233999
## MaritalStatus1 0.0052066260 0.0225490707
## OverTime1 0.0043691201 0.0484928029
## StockOptionLevel TotalWorkingYears
## ï..Age 0.0375097124 0.680380536
## DailyRate 0.0421427964 0.014514739
## DistanceFromHome 0.0448719989 0.004628426
## Education 0.0184222202 0.148279697
## EnvironmentSatisfaction 0.0034321578 -0.002693070
## HourlyRate 0.0502633991 -0.002333682
## JobInvolvement 0.0215226404 -0.005533182
## JobLevel 0.0139839105 0.782207805
## JobSatisfaction 0.0106902261 -0.020185073
## MonthlyIncome 0.0054076767 0.772893246
## MonthlyRate -0.0343228302 0.026442471
## NumCompaniesWorked 0.0300754751 0.237638590
## PercentSalaryHike 0.0075277478 -0.020608488
## PerformanceRating 0.0035064716 0.006743668
## RelationshipSatisfaction -0.0459524907 0.024054292
## StockOptionLevel 1.0000000000 0.010135969
## TotalWorkingYears 0.0101359693 1.000000000
## TrainingTimesLastYear 0.0112740696 -0.035661571
## WorkLifeBalance 0.0041287300 0.001007646
## YearsAtCompany 0.0150580080 0.628133155
## YearsInCurrentRole 0.0508178728 0.460364638
## YearsSinceLastPromotion 0.0143521849 0.404857759
## YearsWithCurrManager 0.0246982266 0.459188397
## Attrition1 -0.1371449189 -0.171063246
## BusinessTravel1 -0.0282569533 0.007972110
## Department1 -0.0121929144 -0.015761512
## EducationField1 -0.0161847132 -0.027847626
## Gender1 0.0127157138 -0.046880939
## JobRole1 -0.0174347521 -0.131447230
## MaritalStatus1 -0.6625772917 -0.077886352
## OverTime1 -0.0004486707 0.012754266
## TrainingTimesLastYear WorkLifeBalance
## ï..Age -0.019620819 -0.021490028
## DailyRate 0.002452543 -0.037848051
## DistanceFromHome -0.036942234 -0.026556004
## Education -0.025100241 0.009819189
## EnvironmentSatisfaction -0.019359308 0.027627295
## HourlyRate -0.008547685 -0.004607234
## JobInvolvement -0.015337826 -0.014616593
## JobLevel -0.018190550 0.037817746
## JobSatisfaction -0.005779335 -0.019458710
## MonthlyIncome -0.021736277 0.030683082
## MonthlyRate 0.001466881 0.007963158
## NumCompaniesWorked -0.066054072 -0.008365685
## PercentSalaryHike -0.005221012 -0.003279636
## PerformanceRating -0.015578882 0.002572361
## RelationshipSatisfaction 0.002496526 0.019604406
## StockOptionLevel 0.011274070 0.004128730
## TotalWorkingYears -0.035661571 0.001007646
## TrainingTimesLastYear 1.000000000 0.028072207
## WorkLifeBalance 0.028072207 1.000000000
## YearsAtCompany 0.003568666 0.012089185
## YearsInCurrentRole -0.005737504 0.049856498
## YearsSinceLastPromotion -0.002066536 0.008941249
## YearsWithCurrManager -0.004095526 0.002759440
## Attrition1 -0.059477799 -0.063939047
## BusinessTravel1 0.016357219 0.004208799
## Department1 0.036875066 0.026382525
## EducationField1 0.049195353 0.041191131
## Gender1 -0.038786735 -0.002752679
## JobRole1 0.002694826 0.022249350
## MaritalStatus1 0.010628607 0.014708294
## OverTime1 -0.079113372 -0.027091878
## YearsAtCompany YearsInCurrentRole
## ï..Age 0.311308770 0.212901056
## DailyRate -0.034054768 0.009932015
## DistanceFromHome 0.009507720 0.018844999
## Education 0.069113696 0.060235554
## EnvironmentSatisfaction 0.001457549 0.018007460
## HourlyRate -0.019581616 -0.024106220
## JobInvolvement -0.021355427 0.008716963
## JobLevel 0.534738687 0.389446733
## JobSatisfaction -0.003802628 -0.002304785
## MonthlyIncome 0.514284826 0.363817667
## MonthlyRate -0.023655107 -0.012814874
## NumCompaniesWorked -0.118421340 -0.090753934
## PercentSalaryHike -0.035991262 -0.001520027
## PerformanceRating 0.003435126 0.034986260
## RelationshipSatisfaction 0.019366787 -0.015122915
## StockOptionLevel 0.015058008 0.050817873
## TotalWorkingYears 0.628133155 0.460364638
## TrainingTimesLastYear 0.003568666 -0.005737504
## WorkLifeBalance 0.012089185 0.049856498
## YearsAtCompany 1.000000000 0.758753737
## YearsInCurrentRole 0.758753737 1.000000000
## YearsSinceLastPromotion 0.618408865 0.548056248
## YearsWithCurrManager 0.769212425 0.714364762
## Attrition1 -0.134392214 -0.160545004
## BusinessTravel1 0.005212128 -0.005336424
## Department1 0.022920442 0.056315447
## EducationField1 -0.018692101 -0.010506203
## Gender1 -0.029747086 -0.041482729
## JobRole1 -0.074311010 -0.018338077
## MaritalStatus1 -0.059986004 -0.065821900
## OverTime1 -0.011687120 -0.029758009
## YearsSinceLastPromotion YearsWithCurrManager
## ï..Age 0.216513368 0.2020886024
## DailyRate -0.033228985 -0.0263631782
## DistanceFromHome 0.010028836 0.0144060484
## Education 0.054254334 0.0690653783
## EnvironmentSatisfaction 0.016193606 -0.0049987226
## HourlyRate -0.026715586 -0.0201232002
## JobInvolvement -0.024184292 0.0259758079
## JobLevel 0.353885347 0.3752806078
## JobSatisfaction -0.018213568 -0.0276562139
## MonthlyIncome 0.344977638 0.3440788833
## MonthlyRate 0.001566800 -0.0367459053
## NumCompaniesWorked -0.036813892 -0.1103191554
## PercentSalaryHike -0.022154313 -0.0119852485
## PerformanceRating 0.017896066 0.0228271689
## RelationshipSatisfaction 0.033492502 -0.0008674968
## StockOptionLevel 0.014352185 0.0246982266
## TotalWorkingYears 0.404857759 0.4591883971
## TrainingTimesLastYear -0.002066536 -0.0040955260
## WorkLifeBalance 0.008941249 0.0027594402
## YearsAtCompany 0.618408865 0.7692124251
## YearsInCurrentRole 0.548056248 0.7143647616
## YearsSinceLastPromotion 1.000000000 0.5102236358
## YearsWithCurrManager 0.510223636 1.0000000000
## Attrition1 -0.033018775 -0.1561993159
## BusinessTravel1 0.005222020 -0.0002285118
## Department1 0.040060967 0.0342824726
## EducationField1 0.002325656 -0.0041296947
## Gender1 -0.026984577 -0.0305989093
## JobRole1 -0.034749921 -0.0339826983
## MaritalStatus1 -0.030915079 -0.0385700737
## OverTime1 -0.012238823 -0.0415859987
## Attrition1 BusinessTravel1 Department1
## ï..Age -0.159205007 -0.0118073324 -0.031882283
## DailyRate -0.056651992 -0.0155388905 0.007108714
## DistanceFromHome 0.077923583 -0.0096960412 0.017224804
## Education -0.031372820 -0.0086698401 0.007996422
## EnvironmentSatisfaction -0.103368978 -0.0113099347 -0.019395271
## HourlyRate -0.006845550 -0.0041639831 -0.004143708
## JobInvolvement -0.130015957 0.0292999586 -0.024586062
## JobLevel -0.169104751 -0.0116958303 0.101963106
## JobSatisfaction -0.103481126 0.0086659585 0.021000879
## MonthlyIncome -0.159839582 -0.0134499467 0.053129698
## MonthlyRate 0.015170213 -0.0084404916 0.023642072
## NumCompaniesWorked 0.043493739 -0.0307429842 -0.035881612
## PercentSalaryHike -0.013478202 -0.0257269434 -0.007840161
## PerformanceRating 0.002888752 0.0016833372 -0.024603543
## RelationshipSatisfaction -0.045872279 0.0089263034 -0.022414425
## StockOptionLevel -0.137144919 -0.0282569533 -0.012192914
## TotalWorkingYears -0.171063246 0.0079721100 -0.015761512
## TrainingTimesLastYear -0.059477799 0.0163572195 0.036875066
## WorkLifeBalance -0.063939047 0.0042087993 0.026382525
## YearsAtCompany -0.134392214 0.0052121277 0.022920442
## YearsInCurrentRole -0.160545004 -0.0053364236 0.056315447
## YearsSinceLastPromotion -0.033018775 0.0052220205 0.040060967
## YearsWithCurrManager -0.156199316 -0.0002285118 0.034282473
## Attrition1 1.000000000 0.1270064832 0.063990596
## BusinessTravel1 0.127006483 1.0000000000 -0.002639604
## Department1 0.063990596 -0.0026396043 1.000000000
## EducationField1 0.026845546 -0.0234890783 0.013719502
## Gender1 0.029453253 -0.0448955126 -0.041583290
## JobRole1 0.057389161 0.0112895901 0.704252821
## MaritalStatus1 0.162070235 0.0309149876 0.056073435
## OverTime1 0.246117994 0.0427515162 0.007480968
## EducationField1 Gender1 JobRole1
## ï..Age -0.040872848 -0.0363105501 -0.112807055
## DailyRate 0.037709229 -0.0117161379 -0.003358019
## DistanceFromHome 0.002013453 -0.0018505280 0.003574933
## Education -0.039592150 -0.0165468274 0.007721031
## EnvironmentSatisfaction 0.043163491 0.0005083139 -0.014562184
## HourlyRate -0.021941219 -0.0004782971 -0.016551533
## JobInvolvement -0.002655278 0.0179597554 0.007401840
## JobLevel -0.044932672 -0.0394031027 -0.066895985
## JobSatisfaction -0.034400814 0.0332516974 0.022468664
## MonthlyIncome -0.041070150 -0.0318584918 -0.082090118
## MonthlyRate -0.027181682 -0.0414822051 0.008086851
## NumCompaniesWorked -0.008663043 -0.0391474496 -0.056838388
## PercentSalaryHike -0.011213502 0.0027326475 0.003231282
## PerformanceRating -0.005614213 -0.0138590184 -0.024459381
## RelationshipSatisfaction -0.004377711 0.0228683700 -0.025023400
## StockOptionLevel -0.016184713 0.0127157138 -0.017434752
## TotalWorkingYears -0.027847626 -0.0468809395 -0.131447230
## TrainingTimesLastYear 0.049195353 -0.0387867355 0.002694826
## WorkLifeBalance 0.041191131 -0.0027526788 0.022249350
## YearsAtCompany -0.018692101 -0.0297470859 -0.074311010
## YearsInCurrentRole -0.010506203 -0.0414827285 -0.018338077
## YearsSinceLastPromotion 0.002325656 -0.0269845772 -0.034749921
## YearsWithCurrManager -0.004129695 -0.0305989093 -0.033982698
## Attrition1 0.026845546 0.0294532532 0.057389161
## BusinessTravel1 -0.023489078 -0.0448955126 0.011289590
## Department1 0.013719502 -0.0415832902 0.704252821
## EducationField1 1.000000000 -0.0025040188 0.027910348
## Gender1 -0.002504019 1.0000000000 -0.043196964
## JobRole1 0.027910348 -0.0431969638 1.000000000
## MaritalStatus1 0.014419541 -0.0471825924 0.069833317
## OverTime1 0.002258600 -0.0419243480 0.043227665
## MaritalStatus1 OverTime1
## ï..Age -0.095028910 0.0280623571
## DailyRate -0.069585641 0.0091349699
## DistanceFromHome -0.014437031 0.0255136349
## Education 0.004052654 -0.0203217674
## EnvironmentSatisfaction -0.003593473 0.0701317268
## HourlyRate -0.017860506 -0.0077819744
## JobInvolvement -0.038497019 -0.0035067106
## JobLevel -0.076769478 0.0005440478
## JobSatisfaction 0.024359953 0.0245394811
## MonthlyIncome -0.075449582 0.0060892854
## MonthlyRate 0.023937427 0.0214311446
## NumCompaniesWorked -0.035505389 -0.0207858214
## PercentSalaryHike 0.012492329 -0.0054328267
## PerformanceRating 0.005206626 0.0043691201
## RelationshipSatisfaction 0.022549071 0.0484928029
## StockOptionLevel -0.662577292 -0.0004486707
## TotalWorkingYears -0.077886352 0.0127542663
## TrainingTimesLastYear 0.010628607 -0.0791133716
## WorkLifeBalance 0.014708294 -0.0270918785
## YearsAtCompany -0.059986004 -0.0116871205
## YearsInCurrentRole -0.065821900 -0.0297580089
## YearsSinceLastPromotion -0.030915079 -0.0122388226
## YearsWithCurrManager -0.038570074 -0.0415859987
## Attrition1 0.162070235 0.2461179942
## BusinessTravel1 0.030914988 0.0427515162
## Department1 0.056073435 0.0074809676
## EducationField1 0.014419541 0.0022585999
## Gender1 -0.047182592 -0.0419243480
## JobRole1 0.069833317 0.0432276651
## MaritalStatus1 1.000000000 -0.0175213816
## OverTime1 -0.017521382 1.0000000000
Corrgram of variables
library("corrgram", lib.loc="~/R/win-library/3.4")
corrgram(Num,lower.panel=panel.shade,upper.panel=panel.pie,order=TRUE)
Correlation Tests
cor.test (Num[,"Attrition1"], Num[,"BusinessTravel1"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "BusinessTravel1"]
## t = 4.9059, df = 1468, p-value = 1.033e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07637497 0.17698469
## sample estimates:
## cor
## 0.1270065
Finding: Attrition Rate depend on Business Travel
cor.test (Num[,"Attrition1"], Num[,"DailyRate"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "DailyRate"]
## t = -2.1741, df = 1468, p-value = 0.02986
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.107468170 -0.005540584
## sample estimates:
## cor
## -0.05665199
Finding: Attrition Rate depend a bit on Daily Rate
cor.test (Num[,"Attrition1"], Num[,"DistanceFromHome"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "DistanceFromHome"]
## t = 2.9947, df = 1468, p-value = 0.002793
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02690331 0.12853894
## sample estimates:
## cor
## 0.07792358
Finding: Attrition Rate depends on DistanceFromHome too
cor.test (Num[,"Attrition1"], Num[,"Education"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "Education"]
## t = -1.2026, df = 1468, p-value = 0.2293
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08236816 0.01978637
## sample estimates:
## cor
## -0.03137282
Finding: Attrition Rate doesnot depend upon Education
cor.test (Num[,"Attrition1"], Num[,"EnvironmentSatisfaction"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "EnvironmentSatisfaction"]
## t = -3.9819, df = 1468, p-value = 7.172e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15368421 -0.05251908
## sample estimates:
## cor
## -0.103369
Finding: Attrition Rate Depend upon Environment Satisfaction
cor.test (Num[,"Attrition1"], Num[,"Gender1"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "Gender1"]
## t = 1.129, df = 1468, p-value = 0.2591
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.02170689 0.08045955
## sample estimates:
## cor
## 0.02945325
Finding: Attrition Rate does not Depend upon Gender of Employee
cor.test (Num[,"Attrition1"], Num[,"HourlyRate"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "HourlyRate"]
## t = -0.26229, df = 1468, p-value = 0.7931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.05795272 0.04429741
## sample estimates:
## cor
## -0.00684555
Finding: Attrition Rate does not Depend upon HourlyRate
cor.test (Num[,"Attrition1"], Num[,"JobInvolvement"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobInvolvement"]
## t = -5.0241, df = 1468, p-value = 5.677e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.17994723 -0.07941641
## sample estimates:
## cor
## -0.130016
Finding: Attrition Rate depends on Job Involvement Also
cor.test (Num[,"Attrition1"], Num[,"JobLevel"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobLevel"]
## t = -6.5738, df = 1468, p-value = 6.795e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2183444 -0.1190062
## sample estimates:
## cor
## -0.1691048
Finding: Attrition Rate depend upon JobLevel also
cor.test (Num[,"Attrition1"], Num[,"JobSatisfaction"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "JobSatisfaction"]
## t = -3.9862, df = 1468, p-value = 7.043e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15379490 -0.05263213
## sample estimates:
## cor
## -0.1034811
Finding: Attrition Rate depend upon JobSatisfaction also
cor.test (Num[,"Attrition1"], Num[,"MaritalStatus1"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MaritalStatus1"]
## t = 6.2928, df = 1468, p-value = 4.106e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1118698 0.2114456
## sample estimates:
## cor
## 0.1620702
Finding: Attrition Rate depend upon MaritalStatus
cor.test (Num[,"Attrition1"], Num[,"MonthlyIncome"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MonthlyIncome"]
## t = -6.2039, df = 1468, p-value = 7.147e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2092570 -0.1096079
## sample estimates:
## cor
## -0.1598396
Finding: Attrition Rate depend upon Monthly Income
cor.test (Num[,"Attrition1"], Num[,"MonthlyRate"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "MonthlyRate"]
## t = 0.58131, df = 1468, p-value = 0.5611
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03598515 0.06624629
## sample estimates:
## cor
## 0.01517021
Finding: Attrition Rate doesnot depend upon Monthly Rate
cor.test (Num[,"Attrition1"], Num[,"NumCompaniesWorked"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "NumCompaniesWorked"]
## t = 1.668, df = 1468, p-value = 0.09553
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.00765073 0.09441125
## sample estimates:
## cor
## 0.04349374
Finding: Attrition Rate doesnot depend much on Number of Companies Worked
cor.test (Num[,"Attrition1"], Num[,"OverTime1"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "OverTime1"]
## t = 9.7292, df = 1468, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1974754 0.2935516
## sample estimates:
## cor
## 0.246118
Finding: Attrition Rate strongly depend on OverTime
cor.test (Num[,"Attrition1"], Num[,"PercentSalaryHike"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "PercentSalaryHike"]
## t = -0.51646, df = 1468, p-value = 0.6056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06456117 0.03767522
## sample estimates:
## cor
## -0.0134782
Finding: Attrition Rate doesnot depend upon PercentSalaryHike too according to coorelation test
cor.test (Num[,"Attrition1"], Num[,"PerformanceRating"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "PerformanceRating"]
## t = 0.11068, df = 1468, p-value = 0.9119
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04824583 0.05400823
## sample estimates:
## cor
## 0.002888752
Finding: Attrition Rate doesnot depend significantly on RelationshipSatisfaction
cor.test (Num[,"Attrition1"], Num[,"StockOptionLevel"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "StockOptionLevel"]
## t = -5.3048, df = 1468, p-value = 1.301e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.18696143 -0.08662486
## sample estimates:
## cor
## -0.1371449
Finding: Attrition Rate depend upon StockOptionLevel
cor.test (Num[,"Attrition1"], Num[,"TotalWorkingYears"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "TotalWorkingYears"]
## t = -6.6523, df = 1468, p-value = 4.062e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2202643 -0.1209940
## sample estimates:
## cor
## -0.1710632
Attrition Rate depend on Total Working Years
cor.test (Num[,"Attrition1"], Num[,"WorkLifeBalance"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "WorkLifeBalance"]
## t = -2.4548, df = 1468, p-value = 0.01421
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.11469157 -0.01285361
## sample estimates:
## cor
## -0.06393905
Finding: AttritionRate Depend on WorkLifeBalance
cor.test (Num[,"Attrition1"], Num[,"YearsAtCompany"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsAtCompany"]
## t = -5.1963, df = 1468, p-value = 2.319e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.18425364 -0.08384084
## sample estimates:
## cor
## -0.1343922
Finding: Attrition Rate depends on YearsAtCompany
cor.test (Num[,"Attrition1"], Num[,"YearsInCurrentRole"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsInCurrentRole"]
## t = -6.232, df = 1468, p-value = 6.003e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2099491 -0.1103231
## sample estimates:
## cor
## -0.160545
Finding: AttritionRate deoend on YearsInCurrentRole too
cor.test (Num[,"Attrition1"], Num[,"YearsSinceLastPromotion"])
##
## Pearson's product-moment correlation
##
## data: Num[, "Attrition1"] and Num[, "YearsSinceLastPromotion"]
## t = -1.2658, df = 1468, p-value = 0.2058
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08400442 0.01813930
## sample estimates:
## cor
## -0.03301878
Finding: Attrition Rate depend on Years With Current Manager
Conclusion of Analysis:
Our Analysis based on Correlation test shows that Attrition Rate majorly Depends on Business Travel, DailyRate, DistanceFromHome, EnvironmentSatisfaction, Job Involvement, JobLevel, JobSatisfaction, MaritalStatus, Monthly Income, MonthlyRate, OverTime, StockOptionLevel, TotalWorkingYears, WorkLifeBalance, YearsAtCompany, YearsInCurrentRole and YearsWithCurrManager
