Prediction on IBM Attrition Data
I used the general linear model in r to classify whether an employee will stay in the company or leave based on available variables
The data was downloaded from IBM Watson Analytics site
library(knitr)
attrition <- read.csv("~/1 UW Tacoma/580 Social media/IBMAttrition.csv", header=TRUE)any(is.na(attrition))## [1] FALSEsummary(attrition)## 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
## names(attrition)## [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"attrition$NonTravel <- ifelse(attrition$BusinessTravel == "Non-Travel", 1 ,0)
attrition$TravelFrequently <- ifelse(attrition$BusinessTravel == "Travel_Frequently", 1 ,0)
attrition$TravelRarely <- ifelse(attrition$BusinessTravel == "Travel_Rarely", 1 ,0)
attrition$SalesPos <- ifelse(attrition$Department == "Sales", 1, 0)
attrition$RDPos <- ifelse(attrition$Department == "Research & Development", 1, 0)
attrition$HRPos <- ifelse(attrition$Department == "Human Resources", 1, 0)
attrition$EducationLS <- ifelse(attrition$EducationField == "Life Sciences", 1, 0)
attrition$EducationOther <- ifelse(attrition$EducationField == "Other", 1, 0)
attrition$EducationMedical <- ifelse(attrition$EducationField == "Medical", 1, 0)
attrition$EducationMarketing <- ifelse(attrition$EducationField == "Marketing", 1, 0)
attrition$EducationTechnical <- ifelse(attrition$EducationField == "Human Resources", 1, 0)
attrition$EducationHR <- ifelse(attrition$EducationField == "Technical Degree", 1, 0)
attrition$Gender <- ifelse(attrition$Gender == "Female", 1, 0)
attrition$Over18 <- ifelse(attrition$Over18 == "Y", 1, 0)
attrition$OverTime <- ifelse(attrition$OverTime == "Yes", 1, 0)
attrition$MaritalSingle <- ifelse(attrition$MaritalStatus == "Single", 1, 0)
attrition$MaritalMarried <- ifelse(attrition$MaritalStatus == "Married", 1, 0)
attrition$MaritalDivorced <- ifelse(attrition$MaritalStatus == "Divorced", 1, 0)
attrition$SalesExecutive <- ifelse(attrition$JobRole == "Sales Executive", 1, 0)
attrition$ResearchScienist <- ifelse(attrition$JobRole == "Research Scientist", 1, 0)
attrition$LabTech <- ifelse(attrition$JobRole == "Laboratory Technician", 1, 0)
attrition$ManufDir <- ifelse(attrition$JobRole == "Manufacturing Director", 1, 0)
attrition$HealthRep <- ifelse(attrition$JobRole == "Healthcare Representative", 1, 0)
attrition$Manager <- ifelse(attrition$JobRole == "Manager", 1, 0)
attrition$SalesRep <- ifelse(attrition$JobRole == "Sales Representative", 1, 0)
attrition$ResearchDir <- ifelse(attrition$JobRole == "Research Director", 1, 0)
attrition$HR <- ifelse(attrition$JobRole == "Human Resources", 1, 0)
unique(attrition$EducationTechnical)## [1] 0 1df <- attrition[,-c(3,5,8,9, 16, 18, 22, 27)]
summary(df)## Age Attrition DailyRate DistanceFromHome
## Min. :18.00 No :1233 Min. : 102.0 Min. : 1.000
## 1st Qu.:30.00 Yes: 237 1st Qu.: 465.0 1st Qu.: 2.000
## Median :36.00 Median : 802.0 Median : 7.000
## Mean :36.92 Mean : 802.5 Mean : 9.193
## 3rd Qu.:43.00 3rd Qu.:1157.0 3rd Qu.:14.000
## Max. :60.00 Max. :1499.0 Max. :29.000
## Education EmployeeNumber EnvironmentSatisfaction Gender
## Min. :1.000 Min. : 1.0 Min. :1.000 Min. :0.0
## 1st Qu.:2.000 1st Qu.: 491.2 1st Qu.:2.000 1st Qu.:0.0
## Median :3.000 Median :1020.5 Median :3.000 Median :0.0
## Mean :2.913 Mean :1024.9 Mean :2.722 Mean :0.4
## 3rd Qu.:4.000 3rd Qu.:1555.8 3rd Qu.:4.000 3rd Qu.:1.0
## Max. :5.000 Max. :2068.0 Max. :4.000 Max. :1.0
## HourlyRate JobInvolvement JobLevel JobSatisfaction
## Min. : 30.00 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.: 48.00 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:2.000
## Median : 66.00 Median :3.00 Median :2.000 Median :3.000
## Mean : 65.89 Mean :2.73 Mean :2.064 Mean :2.729
## 3rd Qu.: 83.75 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :100.00 Max. :4.00 Max. :5.000 Max. :4.000
## MonthlyIncome MonthlyRate NumCompaniesWorked OverTime
## Min. : 1009 Min. : 2094 Min. :0.000 Min. :0.000
## 1st Qu.: 2911 1st Qu.: 8047 1st Qu.:1.000 1st Qu.:0.000
## Median : 4919 Median :14236 Median :2.000 Median :0.000
## Mean : 6503 Mean :14313 Mean :2.693 Mean :0.283
## 3rd Qu.: 8379 3rd Qu.:20462 3rd Qu.:4.000 3rd Qu.:1.000
## Max. :19999 Max. :26999 Max. :9.000 Max. :1.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## Min. :11.00 Min. :3.000 Min. :1.000
## 1st Qu.:12.00 1st Qu.:3.000 1st Qu.:2.000
## Median :14.00 Median :3.000 Median :3.000
## Mean :15.21 Mean :3.154 Mean :2.712
## 3rd Qu.:18.00 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :25.00 Max. :4.000 Max. :4.000
## StockOptionLevel TotalWorkingYears TrainingTimesLastYear WorkLifeBalance
## Min. :0.0000 Min. : 0.00 Min. :0.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.: 6.00 1st Qu.:2.000 1st Qu.:2.000
## Median :1.0000 Median :10.00 Median :3.000 Median :3.000
## Mean :0.7939 Mean :11.28 Mean :2.799 Mean :2.761
## 3rd Qu.:1.0000 3rd Qu.:15.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :3.0000 Max. :40.00 Max. :6.000 Max. :4.000
## YearsAtCompany YearsInCurrentRole YearsSinceLastPromotion
## Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.000 1st Qu.: 2.000 1st Qu.: 0.000
## Median : 5.000 Median : 3.000 Median : 1.000
## Mean : 7.008 Mean : 4.229 Mean : 2.188
## 3rd Qu.: 9.000 3rd Qu.: 7.000 3rd Qu.: 3.000
## Max. :40.000 Max. :18.000 Max. :15.000
## YearsWithCurrManager NonTravel TravelFrequently TravelRarely
## Min. : 0.000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 2.000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 3.000 Median :0.000 Median :0.0000 Median :1.0000
## Mean : 4.123 Mean :0.102 Mean :0.1884 Mean :0.7095
## 3rd Qu.: 7.000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :17.000 Max. :1.000 Max. :1.0000 Max. :1.0000
## SalesPos RDPos HRPos EducationLS
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :0.00000 Median :0.0000
## Mean :0.3034 Mean :0.6537 Mean :0.04286 Mean :0.4122
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## EducationOther EducationMedical EducationMarketing EducationTechnical
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.05578 Mean :0.3156 Mean :0.1082 Mean :0.01837
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## EducationHR MaritalSingle MaritalMarried MaritalDivorced
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.0898 Mean :0.3197 Mean :0.4578 Mean :0.2224
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## SalesExecutive ResearchScienist LabTech ManufDir
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.2218 Mean :0.1986 Mean :0.1762 Mean :0.09864
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## HealthRep Manager SalesRep ResearchDir
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.08912 Mean :0.06939 Mean :0.05646 Mean :0.05442
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## HR
## Min. :0.00000
## 1st Qu.:0.00000
## Median :0.00000
## Mean :0.03537
## 3rd Qu.:0.00000
## Max. :1.00000#library(fastmatch)
#names(attrition)
#fmatch("Over18",names(attrition))
#unique(attrition$Over18)
#head(df)
names(df)## [1] "Age" "Attrition"
## [3] "DailyRate" "DistanceFromHome"
## [5] "Education" "EmployeeNumber"
## [7] "EnvironmentSatisfaction" "Gender"
## [9] "HourlyRate" "JobInvolvement"
## [11] "JobLevel" "JobSatisfaction"
## [13] "MonthlyIncome" "MonthlyRate"
## [15] "NumCompaniesWorked" "OverTime"
## [17] "PercentSalaryHike" "PerformanceRating"
## [19] "RelationshipSatisfaction" "StockOptionLevel"
## [21] "TotalWorkingYears" "TrainingTimesLastYear"
## [23] "WorkLifeBalance" "YearsAtCompany"
## [25] "YearsInCurrentRole" "YearsSinceLastPromotion"
## [27] "YearsWithCurrManager" "NonTravel"
## [29] "TravelFrequently" "TravelRarely"
## [31] "SalesPos" "RDPos"
## [33] "HRPos" "EducationLS"
## [35] "EducationOther" "EducationMedical"
## [37] "EducationMarketing" "EducationTechnical"
## [39] "EducationHR" "MaritalSingle"
## [41] "MaritalMarried" "MaritalDivorced"
## [43] "SalesExecutive" "ResearchScienist"
## [45] "LabTech" "ManufDir"
## [47] "HealthRep" "Manager"
## [49] "SalesRep" "ResearchDir"
## [51] "HR"factor(df$Attrition)## [1] Yes No Yes No No No No No No No No No No No Yes No No
## [18] No No No No Yes No No Yes No Yes No No No No No No Yes
## [35] Yes No Yes No No No No No Yes No No Yes No No No No Yes
## [52] Yes No No No No No No No No No No No No No No No No
## [69] No Yes No No No No No No No No No No No No No No No
## [86] No No No No Yes No No No No No No No No No No Yes No
## [103] Yes No No No No Yes No No No Yes No No No No No No No
## [120] No No No Yes No Yes No Yes Yes No No No No Yes No No No
## [137] Yes No No No Yes No No No No No No No No No No No No
## [154] No No No No No No No No No No No No No No No No No
## [171] No Yes No No No No No Yes No No No No Yes No No No No
## [188] No No No No No Yes No No No No No No No No No No No
## [205] Yes Yes No No No No Yes No No No Yes No Yes Yes No No No
## [222] No No No No No No No No Yes No No No No Yes No Yes No
## [239] No Yes No No No No No No No No No No Yes No No No No
## [256] No No No No Yes No No No No Yes No No No No No No Yes
## [273] No No No No No No No No No No No No No No Yes No Yes
## [290] No No No No Yes No No Yes No No No No No No No No No
## [307] No No No No No No No No No No No Yes No No No No No
## [324] Yes No No No Yes No No No No No No No No Yes No No No
## [341] No No No No No No No No No No No No No No No No No
## [358] Yes No No No No No Yes No No Yes No Yes No Yes No No No
## [375] No No No No Yes No No No Yes No No Yes No No No No No
## [392] No No No No No No No No No No No No No No Yes No No
## [409] No No No No No No Yes Yes No No No No No Yes Yes No No
## [426] No No No No No No No No No No Yes Yes No No Yes Yes No
## [443] No Yes No No No No No No No No No Yes No No No Yes No
## [460] No No No No Yes No No No No No Yes No No No No No No
## [477] No No No Yes Yes No Yes No No No No No No No No No No
## [494] No No Yes No No No No No No No No Yes No No No No No
## [511] No No No Yes Yes No No No No No No No No No No Yes No
## [528] No Yes No No No No No No No No No No No Yes No No No
## [545] No No No Yes No No No No No No No No No No No No No
## [562] No Yes No No No Yes No Yes No No No No Yes No No No No
## [579] No No No No No No No Yes No No No Yes No Yes No No No
## [596] Yes No No Yes No No No No No No No No Yes Yes No No No
## [613] No No Yes No No No No No No No No No No No No No No
## [630] No No No No No No No Yes No No No No No No No No Yes
## [647] No No No No No No No No No No Yes No No No Yes No Yes
## [664] Yes No No Yes Yes No Yes No No No No No No No No No No
## [681] No No No Yes No No No No Yes Yes No No No Yes No Yes No
## [698] No No No Yes No No No No No Yes No No Yes No Yes No No
## [715] No No No No No No Yes No No No No Yes No No No No No
## [732] Yes Yes No No No No No No No No No No No Yes No No No
## [749] Yes Yes No No Yes No No No No No No No No Yes Yes No No
## [766] No No No No No No No No No No No Yes Yes No Yes Yes No
## [783] No No No No No No No Yes No Yes Yes No No No Yes Yes Yes
## [800] No Yes Yes No No No No No No No No No No No Yes No No
## [817] No No No No No No No No No No No No Yes Yes No Yes No
## [834] No No No Yes No Yes No No No Yes No No No No No No Yes
## [851] No No No No No No No Yes No No Yes No No No Yes No No
## [868] No No No No Yes No No No No No No No No No No No No
## [885] No No No No No No No No Yes No No No No No No No No
## [902] No No No No No No No No No No Yes No Yes No Yes No No
## [919] No No No No No No No No No No Yes No No No Yes No No
## [936] No No No No Yes Yes No No No No No Yes Yes No No No No
## [953] Yes Yes No No No No No No No No No No No No Yes No No
## [970] No No No No No No Yes No No No No Yes Yes No No No Yes
## [987] No No No No No No No No No No No Yes No No No No No
## [1004] No No No Yes Yes No No No No Yes No No No Yes No No No
## [1021] No Yes No No No No No No No No No Yes Yes Yes No No Yes
## [1038] No No Yes No No No No No No No No No No No No No No
## [1055] No No Yes Yes Yes No Yes No No No No No No No Yes No No
## [1072] No No No No No No Yes No No No No No Yes No Yes No No
## [1089] No No No No No No No No No No No No No No No No No
## [1106] No Yes No No No Yes Yes Yes No No No No No No No No No
## [1123] No No No No No No No No No No No No No No Yes No No
## [1140] No No No No No No No No No No No No No No Yes No No
## [1157] No No No No No No Yes No No No No Yes No No No Yes No
## [1174] No No No No No No No No No No No No No Yes No No No
## [1191] No No No No No No No No No No No Yes No No Yes Yes No
## [1208] No No No No No No Yes No No No No No No No No Yes Yes
## [1225] No No No No No No No No No No No No Yes Yes No No No
## [1242] No No No No No Yes No No Yes No No No No No Yes No Yes
## [1259] No No No No Yes No No No No No No No No Yes No Yes No
## [1276] No No No No Yes No Yes No No No No No No No No Yes Yes
## [1293] No No No No No Yes Yes No No No No No No No No No No
## [1310] No No No Yes Yes No No No No No No No No No No No No
## [1327] Yes No No No No No Yes Yes No No No No Yes Yes No No No
## [1344] No No No No No No No No No No Yes Yes No No No No No
## [1361] No No No No No Yes No No No Yes No No No No No Yes No
## [1378] No No Yes No No No No No No No No No No Yes No No No
## [1395] No Yes Yes No No No No No No No No No No No No No No
## [1412] No No No No No No No No No No No No No No No No No
## [1429] No No No No No No No No No No Yes No No No Yes No Yes
## [1446] No No No No No No No Yes No No No No No No No No Yes
## [1463] No No No No No No No No
## Levels: No Yes#lapply(df, as.numeric)
library(corrplot)## corrplot 0.84 loadedcor(df[,-c(2)])## Age DailyRate DistanceFromHome
## Age 1.000000000 1.066094e-02 -0.001686120
## DailyRate 0.010660943 1.000000e+00 -0.004985337
## DistanceFromHome -0.001686120 -4.985337e-03 1.000000000
## Education 0.208033731 -1.680643e-02 0.021041826
## EmployeeNumber -0.010145467 -5.099043e-02 0.032916407
## EnvironmentSatisfaction 0.010146428 1.835485e-02 -0.016075327
## Gender 0.036310550 1.171614e-02 0.001850528
## HourlyRate 0.024286543 2.338142e-02 0.031130586
## JobInvolvement 0.029819959 4.613487e-02 0.008783280
## JobLevel 0.509604228 2.966335e-03 0.005302731
## JobSatisfaction -0.004891877 3.057101e-02 -0.003668839
## MonthlyIncome 0.497854567 7.707059e-03 -0.017014445
## MonthlyRate 0.028051167 -3.218160e-02 0.027472864
## NumCompaniesWorked 0.299634758 3.815343e-02 -0.029250804
## OverTime 0.028062357 9.134970e-03 0.025513635
## PercentSalaryHike 0.003633585 2.270368e-02 0.040235377
## PerformanceRating 0.001903896 4.732963e-04 0.027109618
## RelationshipSatisfaction 0.053534720 7.846031e-03 0.006557475
## StockOptionLevel 0.037509712 4.214280e-02 0.044871999
## TotalWorkingYears 0.680380536 1.451474e-02 0.004628426
## TrainingTimesLastYear -0.019620819 2.452543e-03 -0.036942234
## WorkLifeBalance -0.021490028 -3.784805e-02 -0.026556004
## YearsAtCompany 0.311308770 -3.405477e-02 0.009507720
## YearsInCurrentRole 0.212901056 9.932015e-03 0.018844999
## YearsSinceLastPromotion 0.216513368 -3.322898e-02 0.010028836
## YearsWithCurrManager 0.202088602 -2.636318e-02 0.014406048
## NonTravel -0.011214541 1.209624e-02 0.023605129
## TravelFrequently -0.024743127 -1.177561e-02 0.005081409
## TravelRarely 0.028791215 2.078073e-03 -0.020116325
## SalesPos -0.027548733 -3.615683e-03 0.014084536
## RDPos 0.017882960 1.487063e-02 -0.008117375
## HRPos 0.020522881 -2.672563e-02 -0.012901397
## EducationLS 0.016823698 4.027930e-03 -0.024499294
## EducationOther -0.041465681 -3.893392e-03 -0.007968536
## EducationMedical -0.006354488 3.420163e-02 0.013486377
## EducationMarketing 0.038161965 -6.444912e-02 0.039294239
## EducationTechnical 0.001695986 -4.314402e-02 -0.002624327
## EducationHR -0.027604388 3.086887e-02 -0.014801846
## MaritalSingle -0.119185174 -7.583515e-02 -0.027445259
## MaritalMarried 0.083919210 4.003470e-02 0.030232069
## MaritalDivorced 0.033120351 3.708018e-02 -0.005440089
## SalesExecutive -0.002001419 -5.128889e-04 0.030760987
## ResearchScienist -0.146517680 -2.624231e-03 -0.010985651
## LabTech -0.143175521 -6.727581e-03 0.012368886
## ManufDir 0.049726342 -5.302012e-03 0.011847606
## HealthRep 0.098824685 4.014080e-02 0.022915977
## Manager 0.294247754 -1.322388e-02 -0.039189607
## SalesRep -0.175785309 5.375384e-03 -0.015994183
## ResearchDir 0.185891088 -2.124095e-05 -0.022350600
## HR -0.029856364 -2.115577e-02 -0.024089075
## Education EmployeeNumber
## Age 0.2080337310 -0.0101454671
## DailyRate -0.0168064332 -0.0509904337
## DistanceFromHome 0.0210418256 0.0329164072
## Education 1.0000000000 0.0420700930
## EmployeeNumber 0.0420700930 1.0000000000
## EnvironmentSatisfaction -0.0271283133 0.0176208025
## Gender 0.0165468274 -0.0225560514
## HourlyRate 0.0167748289 0.0351792124
## JobInvolvement 0.0424376343 -0.0068879230
## JobLevel 0.1015888862 -0.0185191940
## JobSatisfaction -0.0112961167 -0.0462467349
## MonthlyIncome 0.0949606770 -0.0148285159
## MonthlyRate -0.0260841972 0.0126482292
## NumCompaniesWorked 0.1263165602 -0.0012510320
## OverTime -0.0203217674 -0.0240372868
## PercentSalaryHike -0.0111109409 -0.0129439955
## PerformanceRating -0.0245387912 -0.0203588251
## RelationshipSatisfaction -0.0091183767 -0.0698614115
## StockOptionLevel 0.0184222202 0.0622266925
## TotalWorkingYears 0.1482796965 -0.0143651985
## TrainingTimesLastYear -0.0251002411 0.0236031696
## WorkLifeBalance 0.0098191893 0.0103086414
## YearsAtCompany 0.0691136960 -0.0112404637
## YearsInCurrentRole 0.0602355541 -0.0084163120
## YearsSinceLastPromotion 0.0542543336 -0.0090190642
## YearsWithCurrManager 0.0690653783 -0.0091966453
## NonTravel 0.0045244997 0.0222717131
## TravelFrequently -0.0082920477 -0.0079797857
## TravelRarely 0.0041259521 -0.0079763517
## SalesPos 0.0142151465 0.0154411997
## RDPos -0.0186035998 -0.0419226342
## HRPos 0.0114354254 0.0634314151
## EducationLS 0.0131844280 -0.0006093985
## EducationOther 0.0380431146 0.0104319915
## EducationMedical -0.0723347598 -0.0086885685
## EducationMarketing 0.0724054345 -0.0144870705
## EducationTechnical 0.0264788685 0.0353449178
## EducationHR -0.0267418736 0.0059377909
## MaritalSingle 0.0041675427 -0.0351894389
## MaritalMarried -0.0018652925 0.0539332186
## MaritalDivorced -0.0024388573 -0.0251487953
## SalesExecutive 0.0533980715 0.0232634148
## ResearchScienist 0.0007092034 -0.0176864222
## LabTech -0.0635660276 -0.0197217257
## ManufDir -0.0052904671 -0.0143503179
## HealthRep 0.0242699705 0.0259454946
## Manager 0.0284530475 -0.0350579333
## SalesRep -0.0914652080 0.0062547278
## ResearchDir 0.0496940719 -0.0139831089
## HR -0.0052951467 0.0672866378
## EnvironmentSatisfaction Gender
## Age 0.0101464279 0.0363105501
## DailyRate 0.0183548543 0.0117161379
## DistanceFromHome -0.0160753270 0.0018505280
## Education -0.0271283133 0.0165468274
## EmployeeNumber 0.0176208025 -0.0225560514
## EnvironmentSatisfaction 1.0000000000 -0.0005083139
## Gender -0.0005083139 1.0000000000
## HourlyRate -0.0498569562 0.0004782971
## JobInvolvement -0.0082775982 -0.0179597554
## JobLevel 0.0012116994 0.0394031027
## JobSatisfaction -0.0067843526 -0.0332516974
## MonthlyIncome -0.0062590878 0.0318584918
## MonthlyRate 0.0375996229 0.0414822051
## NumCompaniesWorked 0.0125943232 0.0391474496
## OverTime 0.0701317268 0.0419243480
## PercentSalaryHike -0.0317011952 -0.0027326475
## PerformanceRating -0.0295479523 0.0138590184
## RelationshipSatisfaction 0.0076653835 -0.0228683700
## StockOptionLevel 0.0034321578 -0.0127157138
## TotalWorkingYears -0.0026930704 0.0468809395
## TrainingTimesLastYear -0.0193593083 0.0387867355
## WorkLifeBalance 0.0276272955 0.0027526788
## YearsAtCompany 0.0014575492 0.0297470859
## YearsInCurrentRole 0.0180074601 0.0414827285
## YearsSinceLastPromotion 0.0161936056 0.0269845772
## YearsWithCurrManager -0.0049987226 0.0305989093
## NonTravel 0.0035676742 -0.0504608392
## TravelFrequently -0.0126242125 0.0220153687
## TravelRarely 0.0084956547 0.0146818075
## SalesPos -0.0256060449 0.0320171622
## RDPos 0.0279761266 -0.0157604115
## HRPos -0.0075967123 -0.0356517128
## EducationLS -0.0245256600 -0.0067703705
## EducationOther 0.0646015960 -0.0229919770
## EducationMedical -0.0212986789 0.0131458622
## EducationMarketing 0.0004786134 0.0241428330
## EducationTechnical -0.0068978118 -0.0289559197
## EducationHR 0.0277134086 -0.0038857007
## MaritalSingle 0.0090354910 0.0327520234
## MaritalMarried -0.0221801729 0.0078039723
## MaritalDivorced 0.0164386013 -0.0460761808
## SalesExecutive -0.0244213742 0.0053480142
## ResearchScienist 0.0019403931 -0.0097451458
## LabTech -0.0015329215 -0.0677930807
## ManufDir 0.0591775598 0.0651974297
## HealthRep 0.0140901038 -0.0068233316
## Manager 0.0107296473 0.0338799635
## SalesRep 0.0029486023 0.0288774167
## ResearchDir -0.0486892181 0.0061212763
## HR -0.0220140283 -0.0360824343
## HourlyRate JobInvolvement JobLevel
## 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
## EmployeeNumber 0.0351792124 -0.006887923 -0.0185191940
## EnvironmentSatisfaction -0.0498569562 -0.008277598 0.0012116994
## Gender 0.0004782971 -0.017959755 0.0394031027
## 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
## OverTime -0.0077819744 -0.003506711 0.0005440478
## 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
## NonTravel -0.0169937381 -0.045778886 -0.0072947048
## TravelFrequently -0.0188187015 0.004423606 -0.0215572683
## TravelRarely 0.0275413242 0.026713533 0.0234332898
## SalesPos -0.0120472289 -0.026106587 0.1143068393
## RDPos 0.0186864963 0.023187111 -0.1078296845
## HRPos -0.0165510394 0.004789372 -0.0061573907
## EducationLS 0.0387590560 0.003227533 -0.0084314053
## EducationOther -0.0421629742 -0.011894660 -0.0167244863
## EducationMedical -0.0204179667 0.017102530 -0.0141143416
## EducationMarketing 0.0044518153 -0.018657341 0.0926984732
## EducationTechnical -0.0336696230 0.002078550 0.0104087311
## EducationHR 0.0112833354 -0.004519417 -0.0547066030
## MaritalSingle -0.0334359575 -0.045253287 -0.0870716339
## MaritalMarried 0.0364323218 0.028324424 0.0505470748
## MaritalDivorced -0.0061498622 0.016814706 0.0370871845
## SalesExecutive -0.0118861963 -0.011413130 0.1274899816
## ResearchScienist 0.0200336786 0.047604471 -0.3877883387
## LabTech 0.0180284282 -0.022723562 -0.3446081624
## ManufDir -0.0143939045 -0.021938571 0.1148958643
## HealthRep 0.0145989897 0.001271884 0.1157044190
## Manager 0.0126593700 0.017112423 0.5527441162
## SalesRep -0.0187033985 -0.027281700 -0.2165593644
## ResearchDir -0.0251284552 0.015199933 0.4143185480
## HR -0.0161893511 -0.004951807 -0.1009223108
## JobSatisfaction MonthlyIncome MonthlyRate
## 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
## EmployeeNumber -0.0462467349 -0.014828516 0.0126482292
## EnvironmentSatisfaction -0.0067843526 -0.006259088 0.0375996229
## Gender -0.0332516974 0.031858492 0.0414822051
## 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
## OverTime 0.0245394811 0.006089285 0.0214311446
## 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
## NonTravel 0.0198020944 -0.017260895 0.0152787084
## TravelFrequently 0.0271169181 -0.031657922 0.0003443148
## TravelRarely -0.0365619236 0.038779139 -0.0104839872
## SalesPos 0.0134986414 0.063977520 0.0163882677
## RDPos -0.0027976358 -0.064720452 -0.0054529810
## HRPos -0.0240681448 0.006815078 -0.0243895106
## EducationLS 0.0520041781 -0.007054333 0.0255446098
## EducationOther 0.0033799932 -0.022278709 -0.0356055188
## EducationMedical -0.0226454604 0.001025323 -0.0017225692
## EducationMarketing -0.0235282394 0.062575601 -0.0115586312
## EducationTechnical -0.0214668101 0.021455961 0.0095667520
## EducationHR -0.0197949685 -0.049695118 -0.0045352404
## MaritalSingle 0.0245713621 -0.089361354 0.0372598616
## MaritalMarried -0.0103149392 0.056767393 -0.0346887872
## MaritalDivorced -0.0151969688 0.032203178 -0.0002268508
## SalesExecutive 0.0126037402 0.047791633 0.0118542759
## ResearchScienist 0.0205030615 -0.345180027 -0.0270083536
## LabTech -0.0157102331 -0.320905799 -0.0160564964
## ManufDir -0.0137466166 0.055683925 0.0077112734
## HealthRep 0.0163668290 0.068176850 0.0038286158
## Manager -0.0056196166 0.619573112 0.0317168183
## SalesRep 0.0014130556 -0.201513535 -0.0011998071
## ResearchDir -0.0062173082 0.485818435 0.0258754910
## HR -0.0296814700 -0.092249731 -0.0274703582
## NumCompaniesWorked OverTime
## Age 0.299634758 0.0280623571
## DailyRate 0.038153434 0.0091349699
## DistanceFromHome -0.029250804 0.0255136349
## Education 0.126316560 -0.0203217674
## EmployeeNumber -0.001251032 -0.0240372868
## EnvironmentSatisfaction 0.012594323 0.0701317268
## Gender 0.039147450 0.0419243480
## HourlyRate 0.022156883 -0.0077819744
## JobInvolvement 0.015012413 -0.0035067106
## JobLevel 0.142501124 0.0005440478
## JobSatisfaction -0.055699426 0.0245394811
## MonthlyIncome 0.149515216 0.0060892854
## MonthlyRate 0.017521353 0.0214311446
## NumCompaniesWorked 1.000000000 -0.0207858214
## OverTime -0.020785821 1.0000000000
## PercentSalaryHike -0.010238309 -0.0054328267
## PerformanceRating -0.014094873 0.0043691201
## RelationshipSatisfaction 0.052733049 0.0484928029
## StockOptionLevel 0.030075475 -0.0004486707
## TotalWorkingYears 0.237638590 0.0127542663
## TrainingTimesLastYear -0.066054072 -0.0791133716
## WorkLifeBalance -0.008365685 -0.0270918785
## YearsAtCompany -0.118421340 -0.0116871205
## YearsInCurrentRole -0.090753934 -0.0297580089
## YearsSinceLastPromotion -0.036813892 -0.0122388226
## YearsWithCurrManager -0.110319155 -0.0415859987
## NonTravel 0.002718234 -0.0371633241
## TravelFrequently -0.039718031 0.0293917456
## TravelRarely 0.032400606 -0.0005385846
## SalesPos -0.032096895 0.0058638041
## RDPos 0.022237374 -0.0030359720
## HRPos 0.020617601 -0.0061782147
## EducationLS -0.006130619 -0.0137872743
## EducationOther -0.012869705 0.0249695020
## EducationMedical 0.024826499 0.0022457880
## EducationMarketing -0.018610834 0.0146070389
## EducationTechnical 0.031007480 0.0040397260
## EducationHR -0.013818722 -0.0177231566
## MaritalSingle -0.019160847 -0.0064983952
## MaritalMarried -0.016142078 -0.0135022146
## MaritalDivorced 0.040823996 0.0234621714
## SalesExecutive 0.005913262 0.0063405837
## ResearchScienist -0.043981224 0.0543778139
## LabTech -0.021120992 -0.0447737841
## ManufDir 0.009580497 -0.0103017801
## HealthRep 0.026955086 -0.0003822196
## Manager 0.042124574 -0.0110855526
## SalesRep -0.104494473 0.0033471378
## ResearchDir 0.097925218 0.0024002501
## HR 0.020578071 -0.0140261450
## PercentSalaryHike PerformanceRating
## Age 0.003633585 0.0019038955
## DailyRate 0.022703677 0.0004732963
## DistanceFromHome 0.040235377 0.0271096185
## Education -0.011110941 -0.0245387912
## EmployeeNumber -0.012943996 -0.0203588251
## EnvironmentSatisfaction -0.031701195 -0.0295479523
## Gender -0.002732648 0.0138590184
## HourlyRate -0.009061986 -0.0021716974
## JobInvolvement -0.017204572 -0.0290713334
## JobLevel -0.034730492 -0.0212220821
## JobSatisfaction 0.020002039 0.0022971971
## MonthlyIncome -0.027268586 -0.0171201382
## MonthlyRate -0.006429346 -0.0098114285
## NumCompaniesWorked -0.010238309 -0.0140948728
## OverTime -0.005432827 0.0043691201
## PercentSalaryHike 1.000000000 0.7735499964
## PerformanceRating 0.773549996 1.0000000000
## RelationshipSatisfaction -0.040490081 -0.0313514554
## StockOptionLevel 0.007527748 0.0035064716
## TotalWorkingYears -0.020608488 0.0067436679
## TrainingTimesLastYear -0.005221012 -0.0155788817
## WorkLifeBalance -0.003279636 0.0025723613
## YearsAtCompany -0.035991262 0.0034351261
## YearsInCurrentRole -0.001520027 0.0349862604
## YearsSinceLastPromotion -0.022154313 0.0178960661
## YearsWithCurrManager -0.011985248 0.0228271689
## NonTravel 0.036591439 0.0183099324
## TravelFrequently -0.006674552 0.0164628924
## TravelRarely -0.018648651 -0.0263896341
## SalesPos -0.020403156 -0.0310496878
## RDPos 0.030735414 0.0327204295
## HRPos -0.025888439 -0.0063852631
## EducationLS 0.010209488 0.0108530831
## EducationOther 0.019297312 0.0114489032
## EducationMedical 0.029116142 0.0148677058
## EducationMarketing -0.027726122 -0.0209184441
## EducationTechnical -0.016141770 -0.0161666822
## EducationHR -0.042701294 -0.0217292808
## MaritalSingle -0.001385890 -0.0010453705
## MaritalMarried 0.020895441 0.0095846281
## MaritalDivorced -0.023477646 -0.0103096766
## SalesExecutive -0.046682746 -0.0414012061
## ResearchScienist 0.032537481 0.0194162175
## LabTech -0.020627667 0.0107963550
## ManufDir 0.034682124 0.0297748611
## HealthRep 0.020591134 -0.0009276345
## Manager -0.005393648 0.0320500989
## SalesRep 0.031102210 -0.0062144131
## ResearchDir -0.017017230 -0.0357437633
## HR -0.021032050 -0.0101541407
## RelationshipSatisfaction StockOptionLevel
## Age 0.0535347197 0.0375097124
## DailyRate 0.0078460310 0.0421427964
## DistanceFromHome 0.0065574746 0.0448719989
## Education -0.0091183767 0.0184222202
## EmployeeNumber -0.0698614115 0.0622266925
## EnvironmentSatisfaction 0.0076653835 0.0034321578
## Gender -0.0228683700 -0.0127157138
## HourlyRate 0.0013304528 0.0502633991
## JobInvolvement 0.0342968206 0.0215226404
## JobLevel 0.0216415105 0.0139839105
## JobSatisfaction -0.0124535932 0.0106902261
## MonthlyIncome 0.0258734361 0.0054076767
## MonthlyRate -0.0040853293 -0.0343228302
## NumCompaniesWorked 0.0527330486 0.0300754751
## OverTime 0.0484928029 -0.0004486707
## PercentSalaryHike -0.0404900811 0.0075277478
## PerformanceRating -0.0313514554 0.0035064716
## RelationshipSatisfaction 1.0000000000 -0.0459524907
## StockOptionLevel -0.0459524907 1.0000000000
## TotalWorkingYears 0.0240542918 0.0101359693
## TrainingTimesLastYear 0.0024965264 0.0112740696
## WorkLifeBalance 0.0196044057 0.0041287300
## YearsAtCompany 0.0193667869 0.0150580080
## YearsInCurrentRole -0.0151229149 0.0508178728
## YearsSinceLastPromotion 0.0334925021 0.0143521849
## YearsWithCurrManager -0.0008674968 0.0246982266
## NonTravel 0.0211319138 0.0288067460
## TravelFrequently 0.0285004406 -0.0161421139
## TravelRarely -0.0386403722 -0.0053027044
## SalesPos -0.0104886343 -0.0157554082
## RDPos -0.0045868536 0.0169265047
## HRPos 0.0345828083 -0.0039995588
## EducationLS -0.0199727266 -0.0179928296
## EducationOther -0.0203052201 -0.0420997534
## EducationMedical 0.0304941886 0.0337497570
## EducationMarketing -0.0065797949 0.0225600757
## EducationTechnical 0.0411047858 0.0212056151
## EducationHR -0.0110435560 -0.0245603012
## MaritalSingle 0.0408170260 -0.6389568735
## MaritalMarried -0.0433822512 0.2255742330
## MaritalDivorced 0.0061985836 0.4462847451
## SalesExecutive -0.0048356740 0.0157559614
## ResearchScienist -0.0031164012 -0.0116345323
## LabTech -0.0106909518 0.0133861789
## ManufDir 0.0036400458 0.0077345939
## HealthRep -0.0050899098 0.0140213681
## Manager 0.0256376488 -0.0156373653
## SalesRep -0.0248588343 -0.0480674950
## ResearchDir -0.0054923907 0.0158067762
## HR 0.0441685137 -0.0098644990
## TotalWorkingYears TrainingTimesLastYear
## Age 0.680380536 -0.019620819
## DailyRate 0.014514739 0.002452543
## DistanceFromHome 0.004628426 -0.036942234
## Education 0.148279697 -0.025100241
## EmployeeNumber -0.014365198 0.023603170
## EnvironmentSatisfaction -0.002693070 -0.019359308
## Gender 0.046880939 0.038786735
## HourlyRate -0.002333682 -0.008547685
## JobInvolvement -0.005533182 -0.015337826
## JobLevel 0.782207805 -0.018190550
## JobSatisfaction -0.020185073 -0.005779335
## MonthlyIncome 0.772893246 -0.021736277
## MonthlyRate 0.026442471 0.001466881
## NumCompaniesWorked 0.237638590 -0.066054072
## OverTime 0.012754266 -0.079113372
## PercentSalaryHike -0.020608488 -0.005221012
## PerformanceRating 0.006743668 -0.015578882
## RelationshipSatisfaction 0.024054292 0.002496526
## StockOptionLevel 0.010135969 0.011274070
## TotalWorkingYears 1.000000000 -0.035661571
## TrainingTimesLastYear -0.035661571 1.000000000
## WorkLifeBalance 0.001007646 0.028072207
## YearsAtCompany 0.628133155 0.003568666
## YearsInCurrentRole 0.460364638 -0.005737504
## YearsSinceLastPromotion 0.404857759 -0.002066536
## YearsWithCurrManager 0.459188397 -0.004095526
## NonTravel -0.029742053 -0.020746439
## TravelFrequently -0.012176933 0.006193101
## TravelRarely 0.030320318 0.008498390
## SalesPos -0.014781440 0.024688428
## RDPos 0.011086883 -0.006818580
## HRPos 0.007507576 -0.040021815
## EducationLS -0.003630414 -0.039018068
## EducationOther -0.028934530 -0.008151216
## EducationMedical 0.024890058 0.070541828
## EducationMarketing 0.025778567 -0.029046413
## EducationTechnical 0.005504510 -0.037664148
## EducationHR -0.041576559 0.008289250
## MaritalSingle -0.089529354 0.024128838
## MaritalMarried 0.053512130 -0.029602421
## MaritalDivorced 0.036291221 0.008404760
## SalesExecutive -0.012241358 0.013241077
## ResearchScienist -0.228118930 -0.052125572
## LabTech -0.215425543 0.053998428
## ManufDir 0.064077109 -0.013986647
## HealthRep 0.112159383 -0.012432472
## Manager 0.465836777 0.003052087
## SalesRep -0.207726137 0.040376633
## ResearchDir 0.312147716 -0.004526890
## HR -0.076482406 -0.035901571
## WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Age -0.021490028 0.311308770 0.212901056
## DailyRate -0.037848051 -0.034054768 0.009932015
## DistanceFromHome -0.026556004 0.009507720 0.018844999
## Education 0.009819189 0.069113696 0.060235554
## EmployeeNumber 0.010308641 -0.011240464 -0.008416312
## EnvironmentSatisfaction 0.027627295 0.001457549 0.018007460
## Gender 0.002752679 0.029747086 0.041482729
## HourlyRate -0.004607234 -0.019581616 -0.024106220
## JobInvolvement -0.014616593 -0.021355427 0.008716963
## JobLevel 0.037817746 0.534738687 0.389446733
## JobSatisfaction -0.019458710 -0.003802628 -0.002304785
## MonthlyIncome 0.030683082 0.514284826 0.363817667
## MonthlyRate 0.007963158 -0.023655107 -0.012814874
## NumCompaniesWorked -0.008365685 -0.118421340 -0.090753934
## OverTime -0.027091878 -0.011687120 -0.029758009
## PercentSalaryHike -0.003279636 -0.035991262 -0.001520027
## PerformanceRating 0.002572361 0.003435126 0.034986260
## RelationshipSatisfaction 0.019604406 0.019366787 -0.015122915
## StockOptionLevel 0.004128730 0.015058008 0.050817873
## TotalWorkingYears 0.001007646 0.628133155 0.460364638
## TrainingTimesLastYear 0.028072207 0.003568666 -0.005737504
## WorkLifeBalance 1.000000000 0.012089185 0.049856498
## YearsAtCompany 0.012089185 1.000000000 0.758753737
## YearsInCurrentRole 0.049856498 0.758753737 1.000000000
## YearsSinceLastPromotion 0.008941249 0.618408865 0.548056248
## YearsWithCurrManager 0.002759440 0.769212425 0.714364762
## NonTravel 0.005779786 0.007623469 0.011548596
## TravelFrequently 0.010199440 0.012991460 0.001679706
## TravelRarely -0.012639572 -0.016273928 -0.009147161
## SalesPos 0.051320467 0.029804897 0.046882683
## RDPos -0.069921567 -0.032181306 -0.028151286
## HRPos 0.047762871 0.007944309 -0.040286920
## EducationLS -0.039728025 -0.002018900 0.018343208
## EducationOther 0.031812267 -0.030331043 -0.017021439
## EducationMedical 0.001641074 0.010805171 -0.026418128
## EducationMarketing 0.018500492 0.031364859 0.025126006
## EducationTechnical -0.003967424 -0.005145616 -0.025443438
## EducationHR 0.021962121 -0.021398984 0.009682988
## MaritalSingle 0.014921371 -0.070935377 -0.086485602
## MaritalMarried -0.006387995 0.044924691 0.065487762
## MaritalDivorced -0.009079977 0.025727695 0.018531783
## SalesExecutive 0.032091525 0.042602316 0.092349152
## ResearchScienist -0.058612638 -0.154062102 -0.131314313
## LabTech -0.028208598 -0.150181068 -0.131321689
## ManufDir 0.002010769 0.031967710 0.067876997
## HealthRep -0.026101459 0.069757717 0.054695026
## Manager 0.005136642 0.330965169 0.167498557
## SalesRep 0.045147625 -0.163464375 -0.149750880
## ResearchDir 0.034402697 0.153918347 0.136332176
## HR 0.043886733 -0.052568655 -0.057875721
## YearsSinceLastPromotion YearsWithCurrManager
## Age 0.216513368 0.2020886024
## DailyRate -0.033228985 -0.0263631782
## DistanceFromHome 0.010028836 0.0144060484
## Education 0.054254334 0.0690653783
## EmployeeNumber -0.009019064 -0.0091966453
## EnvironmentSatisfaction 0.016193606 -0.0049987226
## Gender 0.026984577 0.0305989093
## 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
## OverTime -0.012238823 -0.0415859987
## 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
## NonTravel 0.020815313 0.0167155639
## TravelFrequently 0.023216146 0.0126279231
## TravelRarely -0.033877390 -0.0220231198
## SalesPos 0.034112138 0.0274149587
## RDPos -0.021497201 -0.0149627463
## HRPos -0.026930679 -0.0270791337
## EducationLS -0.002479523 0.0036356834
## EducationOther -0.039931117 -0.0117143725
## EducationMedical 0.022664541 -0.0115437939
## EducationMarketing 0.006219280 0.0334183135
## EducationTechnical -0.023699806 -0.0288676187
## EducationHR 0.003853133 -0.0008359179
## MaritalSingle -0.053089577 -0.0477928854
## MaritalMarried 0.054102473 0.0329719283
## MaritalDivorced -0.005278738 0.0140950653
## SalesExecutive 0.049201819 0.0830284142
## ResearchScienist -0.105237384 -0.1276081160
## LabTech -0.110099160 -0.1070719838
## ManufDir -0.007241257 0.0762069928
## HealthRep 0.075902411 0.0394071436
## Manager 0.224254871 0.1646950822
## SalesRep -0.085622332 -0.1687433909
## ResearchDir 0.074454513 0.1312789228
## HR -0.054603246 -0.0510055504
## NonTravel TravelFrequently TravelRarely
## Age -0.011214541 -0.0247431271 0.0287912152
## DailyRate 0.012096236 -0.0117756077 0.0020780730
## DistanceFromHome 0.023605129 0.0050814093 -0.0201163255
## Education 0.004524500 -0.0082920477 0.0041259521
## EmployeeNumber 0.022271713 -0.0079797857 -0.0079763517
## EnvironmentSatisfaction 0.003567674 -0.0126242125 0.0084956547
## Gender -0.050460839 0.0220153687 0.0146818075
## HourlyRate -0.016993738 -0.0188187015 0.0275413242
## JobInvolvement -0.045778886 0.0044236060 0.0267135328
## JobLevel -0.007294705 -0.0215572683 0.0234332898
## JobSatisfaction 0.019802094 0.0271169181 -0.0365619236
## MonthlyIncome -0.017260895 -0.0316579217 0.0387791389
## MonthlyRate 0.015278708 0.0003443148 -0.0104839872
## NumCompaniesWorked 0.002718234 -0.0397180314 0.0324006064
## OverTime -0.037163324 0.0293917456 -0.0005385846
## PercentSalaryHike 0.036591439 -0.0066745525 -0.0186486513
## PerformanceRating 0.018309932 0.0164628924 -0.0263896341
## RelationshipSatisfaction 0.021131914 0.0285004406 -0.0386403722
## StockOptionLevel 0.028806746 -0.0161421139 -0.0053027044
## TotalWorkingYears -0.029742053 -0.0121769326 0.0303203184
## TrainingTimesLastYear -0.020746439 0.0061931015 0.0084983904
## WorkLifeBalance 0.005779786 0.0101994399 -0.0126395725
## YearsAtCompany 0.007623469 0.0129914605 -0.0162739279
## YearsInCurrentRole 0.011548596 0.0016797056 -0.0091471606
## YearsSinceLastPromotion 0.020815313 0.0232161464 -0.0338773896
## YearsWithCurrManager 0.016715564 0.0126279231 -0.0220231198
## NonTravel 1.000000000 -0.1624345190 -0.5268498741
## TravelFrequently -0.162434519 1.0000000000 -0.7530917316
## TravelRarely -0.526849874 -0.7530917316 1.0000000000
## SalesPos 0.007282728 -0.0001595931 -0.0047184366
## RDPos -0.005012697 0.0033403757 0.0004649252
## HRPos -0.004755439 -0.0074846535 0.0096180553
## EducationLS 0.005310927 0.0311278698 -0.0303546649
## EducationOther -0.013389421 -0.0110035456 0.0184061093
## EducationMedical 0.012828445 -0.0053672586 -0.0039302840
## EducationMarketing -0.030567442 -0.0165855032 0.0346681989
## EducationTechnical 0.020835477 0.0118182623 -0.0240727166
## EducationHR 0.004171065 -0.0235690639 0.0175212077
## MaritalSingle -0.004622081 0.0277339116 -0.0208080761
## MaritalMarried -0.043634531 -0.0307851810 0.0556125291
## MaritalDivorced 0.057455207 0.0057790017 -0.0432874704
## SalesExecutive 0.031022241 -0.0101748855 -0.0119200920
## ResearchScienist -0.010115964 -0.0044609120 0.0105876502
## LabTech 0.009269524 0.0100234425 -0.0148148234
## ManufDir -0.013535659 0.0097828383 0.0005982601
## HealthRep 0.012878127 0.0080287107 -0.0155026757
## Manager 0.014077973 -0.0425826786 0.0272938675
## SalesRep -0.033780051 0.0554689235 -0.0252572887
## ResearchDir -0.021430985 -0.0235790300 0.0346004893
## HR -0.015890181 0.0018962335 0.0089616947
## SalesPos RDPos HRPos
## Age -0.0275487332 0.0178829603 0.020522881
## DailyRate -0.0036156827 0.0148706338 -0.026725634
## DistanceFromHome 0.0140845356 -0.0081173746 -0.012901397
## Education 0.0142151465 -0.0186035998 0.011435425
## EmployeeNumber 0.0154411997 -0.0419226342 0.063431415
## EnvironmentSatisfaction -0.0256060449 0.0279761266 -0.007596712
## Gender 0.0320171622 -0.0157604115 -0.035651713
## HourlyRate -0.0120472289 0.0186864963 -0.016551039
## JobInvolvement -0.0261065866 0.0231871113 0.004789372
## JobLevel 0.1143068393 -0.1078296845 -0.006157391
## JobSatisfaction 0.0134986414 -0.0027976358 -0.024068145
## MonthlyIncome 0.0639775198 -0.0647204522 0.006815078
## MonthlyRate 0.0163882677 -0.0054529810 -0.024389511
## NumCompaniesWorked -0.0320968948 0.0222373740 0.020617601
## OverTime 0.0058638041 -0.0030359720 -0.006178215
## PercentSalaryHike -0.0204031556 0.0307354141 -0.025888439
## PerformanceRating -0.0310496878 0.0327204295 -0.006385263
## RelationshipSatisfaction -0.0104886343 -0.0045868536 0.034582808
## StockOptionLevel -0.0157554082 0.0169265047 -0.003999559
## TotalWorkingYears -0.0147814400 0.0110868826 0.007507576
## TrainingTimesLastYear 0.0246884279 -0.0068185797 -0.040021815
## WorkLifeBalance 0.0513204666 -0.0699215667 0.047762871
## YearsAtCompany 0.0298048972 -0.0321813056 0.007944309
## YearsInCurrentRole 0.0468826828 -0.0281512865 -0.040286920
## YearsSinceLastPromotion 0.0341121379 -0.0214972009 -0.026930679
## YearsWithCurrManager 0.0274149587 -0.0149627463 -0.027079134
## NonTravel 0.0072827284 -0.0050126966 -0.004755439
## TravelFrequently -0.0001595931 0.0033403757 -0.007484654
## TravelRarely -0.0047184366 0.0004649252 0.009618055
## SalesPos 1.0000000000 -0.9068182539 -0.139649915
## RDPos -0.9068182539 1.0000000000 -0.290754221
## HRPos -0.1396499154 -0.2907542213 1.000000000
## EducationLS -0.1017910051 0.1273213763 -0.068039985
## EducationOther -0.0636952968 0.0647506692 -0.007526669
## EducationMedical -0.1680338501 0.1835484574 -0.049761221
## EducationMarketing 0.5276910949 -0.4785199173 -0.073692017
## EducationTechnical -0.0902747236 -0.1879539768 0.646435935
## EducationHR -0.0313088434 0.0385405256 -0.019469058
## MaritalSingle 0.0330020675 -0.0099900118 -0.051442616
## MaritalMarried 0.0053784106 -0.0199972324 0.034767421
## MaritalDivorced -0.0434508664 0.0351583173 0.016036876
## SalesExecutive 0.8088692708 -0.7334974198 -0.112958525
## ResearchScienist -0.3285762975 0.3623397479 -0.105351811
## LabTech -0.3052075422 0.3365696939 -0.097859059
## ManufDir -0.2183199952 0.2407538602 -0.070000201
## HealthRep -0.2064252458 0.2276368444 -0.066186373
## Manager 0.0352477873 -0.0713557186 0.087614668
## SalesRep 0.3706665234 -0.3361271695 -0.051763549
## ResearchDir -0.1583270225 0.1745962014 -0.050764583
## HR -0.1263807730 -0.2631275725 0.904982811
## EducationLS EducationOther EducationMedical
## Age 0.0168236980 -0.041465681 -0.006354488
## DailyRate 0.0040279302 -0.003893392 0.034201628
## DistanceFromHome -0.0244992945 -0.007968536 0.013486377
## Education 0.0131844280 0.038043115 -0.072334760
## EmployeeNumber -0.0006093985 0.010431992 -0.008688568
## EnvironmentSatisfaction -0.0245256600 0.064601596 -0.021298679
## Gender -0.0067703705 -0.022991977 0.013145862
## HourlyRate 0.0387590560 -0.042162974 -0.020417967
## JobInvolvement 0.0032275330 -0.011894660 0.017102530
## JobLevel -0.0084314053 -0.016724486 -0.014114342
## JobSatisfaction 0.0520041781 0.003379993 -0.022645460
## MonthlyIncome -0.0070543325 -0.022278709 0.001025323
## MonthlyRate 0.0255446098 -0.035605519 -0.001722569
## NumCompaniesWorked -0.0061306193 -0.012869705 0.024826499
## OverTime -0.0137872743 0.024969502 0.002245788
## PercentSalaryHike 0.0102094885 0.019297312 0.029116142
## PerformanceRating 0.0108530831 0.011448903 0.014867706
## RelationshipSatisfaction -0.0199727266 -0.020305220 0.030494189
## StockOptionLevel -0.0179928296 -0.042099753 0.033749757
## TotalWorkingYears -0.0036304137 -0.028934530 0.024890058
## TrainingTimesLastYear -0.0390180682 -0.008151216 0.070541828
## WorkLifeBalance -0.0397280249 0.031812267 0.001641074
## YearsAtCompany -0.0020189001 -0.030331043 0.010805171
## YearsInCurrentRole 0.0183432080 -0.017021439 -0.026418128
## YearsSinceLastPromotion -0.0024795235 -0.039931117 0.022664541
## YearsWithCurrManager 0.0036356834 -0.011714373 -0.011543794
## NonTravel 0.0053109270 -0.013389421 0.012828445
## TravelFrequently 0.0311278698 -0.011003546 -0.005367259
## TravelRarely -0.0303546649 0.018406109 -0.003930284
## SalesPos -0.1017910051 -0.063695297 -0.168033850
## RDPos 0.1273213763 0.064750669 0.183548457
## HRPos -0.0680399852 -0.007526669 -0.049761221
## EducationLS 1.0000000000 -0.203559621 -0.568773614
## EducationOther -0.2035596212 1.000000000 -0.165071536
## EducationMedical -0.5687736139 -0.165071536 1.000000000
## EducationMarketing -0.2916598561 -0.084646579 -0.236514198
## EducationTechnical -0.1145587213 -0.033247647 -0.092898503
## EducationHR -0.2630500264 -0.076343330 -0.213313779
## MaritalSingle 0.0214687864 0.004972185 -0.004248603
## MaritalMarried -0.0178660429 -0.009171103 -0.007138661
## MaritalDivorced -0.0026719437 0.005410851 0.013316068
## SalesExecutive -0.0911220525 -0.036995208 -0.133531752
## ResearchScienist 0.0437294400 0.005286472 0.039735215
## LabTech 0.0443586093 0.058759435 0.066262307
## ManufDir 0.0520234106 -0.010819978 0.035496321
## HealthRep 0.0290839860 0.017608517 0.034164674
## Manager -0.0111434239 -0.008046243 -0.001128474
## SalesRep -0.0432078892 -0.033774032 -0.051990485
## ResearchDir 0.0184008292 -0.006044423 0.062897852
## HR -0.0631188033 0.001593718 -0.042894950
## EducationMarketing EducationTechnical
## Age 0.0381619649 0.001695986
## DailyRate -0.0644491244 -0.043144017
## DistanceFromHome 0.0392942386 -0.002624327
## Education 0.0724054345 0.026478868
## EmployeeNumber -0.0144870705 0.035344918
## EnvironmentSatisfaction 0.0004786134 -0.006897812
## Gender 0.0241428330 -0.028955920
## HourlyRate 0.0044518153 -0.033669623
## JobInvolvement -0.0186573412 0.002078550
## JobLevel 0.0926984732 0.010408731
## JobSatisfaction -0.0235282394 -0.021466810
## MonthlyIncome 0.0625756007 0.021455961
## MonthlyRate -0.0115586312 0.009566752
## NumCompaniesWorked -0.0186108336 0.031007480
## OverTime 0.0146070389 0.004039726
## PercentSalaryHike -0.0277261225 -0.016141770
## PerformanceRating -0.0209184441 -0.016166682
## RelationshipSatisfaction -0.0065797949 0.041104786
## StockOptionLevel 0.0225600757 0.021205615
## TotalWorkingYears 0.0257785673 0.005504510
## TrainingTimesLastYear -0.0290464132 -0.037664148
## WorkLifeBalance 0.0185004924 -0.003967424
## YearsAtCompany 0.0313648590 -0.005145616
## YearsInCurrentRole 0.0251260063 -0.025443438
## YearsSinceLastPromotion 0.0062192799 -0.023699806
## YearsWithCurrManager 0.0334183135 -0.028867619
## NonTravel -0.0305674420 0.020835477
## TravelFrequently -0.0165855032 0.011818262
## TravelRarely 0.0346681989 -0.024072717
## SalesPos 0.5276910949 -0.090274724
## RDPos -0.4785199173 -0.187953977
## HRPos -0.0736920168 0.646435935
## EducationLS -0.2916598561 -0.114558721
## EducationOther -0.0846465787 -0.033247647
## EducationMedical -0.2365141978 -0.092898503
## EducationMarketing 1.0000000000 -0.047637168
## EducationTechnical -0.0476371678 1.000000000
## EducationHR -0.1093845854 -0.042964289
## MaritalSingle -0.0133225457 -0.072051104
## MaritalMarried 0.0184910957 0.057339038
## MaritalDivorced -0.0072118532 0.012107046
## SalesExecutive 0.4573081080 -0.073020450
## ResearchScienist -0.1733867862 -0.068103197
## LabTech -0.1610553021 -0.063259612
## ManufDir -0.1152055173 -0.045250645
## HealthRep -0.1089287640 -0.042785250
## Manager 0.0255765886 0.082270516
## SalesRep 0.1330650552 -0.033461818
## ResearchDir -0.0835477598 -0.032816050
## HR -0.0666900085 0.549751407
## EducationHR MaritalSingle MaritalMarried
## Age -0.0276043878 -0.119185174 0.083919210
## DailyRate 0.0308688652 -0.075835152 0.040034696
## DistanceFromHome -0.0148018457 -0.027445259 0.030232069
## Education -0.0267418736 0.004167543 -0.001865293
## EmployeeNumber 0.0059377909 -0.035189439 0.053933219
## EnvironmentSatisfaction 0.0277134086 0.009035491 -0.022180173
## Gender -0.0038857007 0.032752023 0.007803972
## HourlyRate 0.0112833354 -0.033435958 0.036432322
## JobInvolvement -0.0045194172 -0.045253287 0.028324424
## JobLevel -0.0547066030 -0.087071634 0.050547075
## JobSatisfaction -0.0197949685 0.024571362 -0.010314939
## MonthlyIncome -0.0496951180 -0.089361354 0.056767393
## MonthlyRate -0.0045352404 0.037259862 -0.034688787
## NumCompaniesWorked -0.0138187217 -0.019160847 -0.016142078
## OverTime -0.0177231566 -0.006498395 -0.013502215
## PercentSalaryHike -0.0427012937 -0.001385890 0.020895441
## PerformanceRating -0.0217292808 -0.001045371 0.009584628
## RelationshipSatisfaction -0.0110435560 0.040817026 -0.043382251
## StockOptionLevel -0.0245603012 -0.638956874 0.225574233
## TotalWorkingYears -0.0415765587 -0.089529354 0.053512130
## TrainingTimesLastYear 0.0082892505 0.024128838 -0.029602421
## WorkLifeBalance 0.0219621205 0.014921371 -0.006387995
## YearsAtCompany -0.0213989837 -0.070935377 0.044924691
## YearsInCurrentRole 0.0096829879 -0.086485602 0.065487762
## YearsSinceLastPromotion 0.0038531329 -0.053089577 0.054102473
## YearsWithCurrManager -0.0008359179 -0.047792885 0.032971928
## NonTravel 0.0041710653 -0.004622081 -0.043634531
## TravelFrequently -0.0235690639 0.027733912 -0.030785181
## TravelRarely 0.0175212077 -0.020808076 0.055612529
## SalesPos -0.0313088434 0.033002068 0.005378411
## RDPos 0.0385405256 -0.009990012 -0.019997232
## HRPos -0.0194690580 -0.051442616 0.034767421
## EducationLS -0.2630500264 0.021468786 -0.017866043
## EducationOther -0.0763433305 0.004972185 -0.009171103
## EducationMedical -0.2133137786 -0.004248603 -0.007138661
## EducationMarketing -0.1093845854 -0.013322546 0.018491096
## EducationTechnical -0.0429642886 -0.072051104 0.057339038
## EducationHR 1.0000000000 0.014265200 0.002709657
## MaritalSingle 0.0142652002 1.000000000 -0.629980781
## MaritalMarried 0.0027096571 -0.629980781 1.000000000
## MaritalDivorced -0.0192427195 -0.366690527 -0.491506349
## SalesExecutive -0.0588433828 0.006210157 0.005750591
## ResearchScienist 0.0762175922 0.053521622 -0.039987225
## LabTech -0.0265887905 0.019872553 -0.009232869
## ManufDir 0.0078172775 -0.021331292 0.002819161
## HealthRep 0.0186806012 -0.030125984 0.004913044
## Manager -0.0389463962 -0.055175985 0.049982244
## SalesRep 0.0571845105 0.072439209 -0.023658624
## ResearchDir -0.0229053861 -0.042298518 0.008271081
## HR -0.0086226407 -0.052320302 0.030994608
## MaritalDivorced SalesExecutive ResearchScienist
## Age 0.0331203513 -0.0020014187 -0.1465176799
## DailyRate 0.0370801775 -0.0005128889 -0.0026242310
## DistanceFromHome -0.0054400885 0.0307609872 -0.0109856507
## Education -0.0024388573 0.0533980715 0.0007092034
## EmployeeNumber -0.0251487953 0.0232634148 -0.0176864222
## EnvironmentSatisfaction 0.0164386013 -0.0244213742 0.0019403931
## Gender -0.0460761808 0.0053480142 -0.0097451458
## HourlyRate -0.0061498622 -0.0118861963 0.0200336786
## JobInvolvement 0.0168147061 -0.0114131300 0.0476044710
## JobLevel 0.0370871845 0.1274899816 -0.3877883387
## JobSatisfaction -0.0151969688 0.0126037402 0.0205030615
## MonthlyIncome 0.0322031782 0.0477916330 -0.3451800266
## MonthlyRate -0.0002268508 0.0118542759 -0.0270083536
## NumCompaniesWorked 0.0408239957 0.0059132619 -0.0439812236
## OverTime 0.0234621714 0.0063405837 0.0543778139
## PercentSalaryHike -0.0234776458 -0.0466827464 0.0325374807
## PerformanceRating -0.0103096766 -0.0414012061 0.0194162175
## RelationshipSatisfaction 0.0061985836 -0.0048356740 -0.0031164012
## StockOptionLevel 0.4462847451 0.0157559614 -0.0116345323
## TotalWorkingYears 0.0362912209 -0.0122413576 -0.2281189296
## TrainingTimesLastYear 0.0084047599 0.0132410767 -0.0521255716
## WorkLifeBalance -0.0090799771 0.0320915249 -0.0586126377
## YearsAtCompany 0.0257276949 0.0426023162 -0.1540621022
## YearsInCurrentRole 0.0185317834 0.0923491523 -0.1313143127
## YearsSinceLastPromotion -0.0052787380 0.0492018190 -0.1052373844
## YearsWithCurrManager 0.0140950653 0.0830284142 -0.1276081160
## NonTravel 0.0574552067 0.0310222410 -0.0101159637
## TravelFrequently 0.0057790017 -0.0101748855 -0.0044609120
## TravelRarely -0.0432874704 -0.0119200920 0.0105876502
## SalesPos -0.0434508664 0.8088692708 -0.3285762975
## RDPos 0.0351583173 -0.7334974198 0.3623397479
## HRPos 0.0160368763 -0.1129585253 -0.1053518112
## EducationLS -0.0026719437 -0.0911220525 0.0437294400
## EducationOther 0.0054108506 -0.0369952080 0.0052864722
## EducationMedical 0.0133160682 -0.1335317519 0.0397352151
## EducationMarketing -0.0072118532 0.4573081080 -0.1733867862
## EducationTechnical 0.0121070456 -0.0730204499 -0.0681031966
## EducationHR -0.0192427195 -0.0588433828 0.0762175922
## MaritalSingle -0.3666905265 0.0062101574 0.0535216221
## MaritalMarried -0.4915063494 0.0057505907 -0.0399872251
## MaritalDivorced 1.0000000000 -0.0138528685 -0.0121151488
## SalesExecutive -0.0138528685 1.0000000000 -0.2657752702
## ResearchScienist -0.0121151488 -0.2657752702 1.0000000000
## LabTech -0.0112240977 -0.2468730021 -0.2302483841
## ManufDir 0.0205432141 -0.1765923353 -0.1647004715
## HealthRep 0.0278970141 -0.1669710380 -0.1557270798
## Manager 0.0019967547 -0.1457648550 -0.1359489375
## SalesRep -0.0528898027 -0.1305859953 -0.1217922339
## ResearchDir 0.0375242525 -0.1280658632 -0.1194418094
## HR 0.0215407456 -0.1022255237 -0.0953415783
## LabTech ManufDir HealthRep
## Age -0.143175521 0.0497263415 0.0988246854
## DailyRate -0.006727581 -0.0053020117 0.0401408008
## DistanceFromHome 0.012368886 0.0118476059 0.0229159772
## Education -0.063566028 -0.0052904671 0.0242699705
## EmployeeNumber -0.019721726 -0.0143503179 0.0259454946
## EnvironmentSatisfaction -0.001532922 0.0591775598 0.0140901038
## Gender -0.067793081 0.0651974297 -0.0068233316
## HourlyRate 0.018028428 -0.0143939045 0.0145989897
## JobInvolvement -0.022723562 -0.0219385715 0.0012718839
## JobLevel -0.344608162 0.1148958643 0.1157044190
## JobSatisfaction -0.015710233 -0.0137466166 0.0163668290
## MonthlyIncome -0.320905799 0.0556839252 0.0681768500
## MonthlyRate -0.016056496 0.0077112734 0.0038286158
## NumCompaniesWorked -0.021120992 0.0095804967 0.0269550862
## OverTime -0.044773784 -0.0103017801 -0.0003822196
## PercentSalaryHike -0.020627667 0.0346821237 0.0205911345
## PerformanceRating 0.010796355 0.0297748611 -0.0009276345
## RelationshipSatisfaction -0.010690952 0.0036400458 -0.0050899098
## StockOptionLevel 0.013386179 0.0077345939 0.0140213681
## TotalWorkingYears -0.215425543 0.0640771086 0.1121593833
## TrainingTimesLastYear 0.053998428 -0.0139866473 -0.0124324723
## WorkLifeBalance -0.028208598 0.0020107695 -0.0261014591
## YearsAtCompany -0.150181068 0.0319677101 0.0697577172
## YearsInCurrentRole -0.131321689 0.0678769970 0.0546950264
## YearsSinceLastPromotion -0.110099160 -0.0072412567 0.0759024109
## YearsWithCurrManager -0.107071984 0.0762069928 0.0394071436
## NonTravel 0.009269524 -0.0135356586 0.0128781272
## TravelFrequently 0.010023443 0.0097828383 0.0080287107
## TravelRarely -0.014814823 0.0005982601 -0.0155026757
## SalesPos -0.305207542 -0.2183199952 -0.2064252458
## RDPos 0.336569694 0.2407538602 0.2276368444
## HRPos -0.097859059 -0.0700002012 -0.0661863734
## EducationLS 0.044358609 0.0520234106 0.0290839860
## EducationOther 0.058759435 -0.0108199781 0.0176085170
## EducationMedical 0.066262307 0.0354963210 0.0341646736
## EducationMarketing -0.161055302 -0.1152055173 -0.1089287640
## EducationTechnical -0.063259612 -0.0452506455 -0.0427852502
## EducationHR -0.026588791 0.0078172775 0.0186806012
## MaritalSingle 0.019872553 -0.0213312920 -0.0301259841
## MaritalMarried -0.009232869 0.0028191614 0.0049130437
## MaritalDivorced -0.011224098 0.0205432141 0.0278970141
## SalesExecutive -0.246873002 -0.1765923353 -0.1669710380
## ResearchScienist -0.230248384 -0.1647004715 -0.1557270798
## LabTech 1.000000000 -0.1529867689 -0.1446515760
## ManufDir -0.152986769 1.0000000000 -0.1034716611
## HealthRep -0.144651576 -0.1034716611 1.0000000000
## Manager -0.126280080 -0.0903302265 -0.0854087560
## SalesRep -0.113130219 -0.0809239136 -0.0765149282
## ResearchDir -0.110946959 -0.0793621921 -0.0750382942
## HR -0.088560767 -0.0633489788 -0.0598975303
## Manager SalesRep ResearchDir
## Age 0.294247754 -0.175785309 1.858911e-01
## DailyRate -0.013223882 0.005375384 -2.124095e-05
## DistanceFromHome -0.039189607 -0.015994183 -2.235060e-02
## Education 0.028453048 -0.091465208 4.969407e-02
## EmployeeNumber -0.035057933 0.006254728 -1.398311e-02
## EnvironmentSatisfaction 0.010729647 0.002948602 -4.868922e-02
## Gender 0.033879964 0.028877417 6.121276e-03
## HourlyRate 0.012659370 -0.018703398 -2.512846e-02
## JobInvolvement 0.017112423 -0.027281700 1.519993e-02
## JobLevel 0.552744116 -0.216559364 4.143185e-01
## JobSatisfaction -0.005619617 0.001413056 -6.217308e-03
## MonthlyIncome 0.619573112 -0.201513535 4.858184e-01
## MonthlyRate 0.031716818 -0.001199807 2.587549e-02
## NumCompaniesWorked 0.042124574 -0.104494473 9.792522e-02
## OverTime -0.011085553 0.003347138 2.400250e-03
## PercentSalaryHike -0.005393648 0.031102210 -1.701723e-02
## PerformanceRating 0.032050099 -0.006214413 -3.574376e-02
## RelationshipSatisfaction 0.025637649 -0.024858834 -5.492391e-03
## StockOptionLevel -0.015637365 -0.048067495 1.580678e-02
## TotalWorkingYears 0.465836777 -0.207726137 3.121477e-01
## TrainingTimesLastYear 0.003052087 0.040376633 -4.526890e-03
## WorkLifeBalance 0.005136642 0.045147625 3.440270e-02
## YearsAtCompany 0.330965169 -0.163464375 1.539183e-01
## YearsInCurrentRole 0.167498557 -0.149750880 1.363322e-01
## YearsSinceLastPromotion 0.224254871 -0.085622332 7.445451e-02
## YearsWithCurrManager 0.164695082 -0.168743391 1.312789e-01
## NonTravel 0.014077973 -0.033780051 -2.143098e-02
## TravelFrequently -0.042582679 0.055468924 -2.357903e-02
## TravelRarely 0.027293867 -0.025257289 3.460049e-02
## SalesPos 0.035247787 0.370666523 -1.583270e-01
## RDPos -0.071355719 -0.336127169 1.745962e-01
## HRPos 0.087614668 -0.051763549 -5.076458e-02
## EducationLS -0.011143424 -0.043207889 1.840083e-02
## EducationOther -0.008046243 -0.033774032 -6.044423e-03
## EducationMedical -0.001128474 -0.051990485 6.289785e-02
## EducationMarketing 0.025576589 0.133065055 -8.354776e-02
## EducationTechnical 0.082270516 -0.033461818 -3.281605e-02
## EducationHR -0.038946396 0.057184511 -2.290539e-02
## MaritalSingle -0.055175985 0.072439209 -4.229852e-02
## MaritalMarried 0.049982244 -0.023658624 8.271081e-03
## MaritalDivorced 0.001996755 -0.052889803 3.752425e-02
## SalesExecutive -0.145764855 -0.130585995 -1.280659e-01
## ResearchScienist -0.135948937 -0.121792234 -1.194418e-01
## LabTech -0.126280080 -0.113130219 -1.109470e-01
## ManufDir -0.090330226 -0.080923914 -7.936219e-02
## HealthRep -0.085408756 -0.076514928 -7.503829e-02
## Manager 1.000000000 -0.066797138 -6.550804e-02
## SalesRep -0.066797138 1.000000000 -5.868653e-02
## ResearchDir -0.065508044 -0.058686527 1.000000e+00
## HR -0.052290235 -0.046845122 -4.594107e-02
## HR
## Age -0.029856364
## DailyRate -0.021155772
## DistanceFromHome -0.024089075
## Education -0.005295147
## EmployeeNumber 0.067286638
## EnvironmentSatisfaction -0.022014028
## Gender -0.036082434
## HourlyRate -0.016189351
## JobInvolvement -0.004951807
## JobLevel -0.100922311
## JobSatisfaction -0.029681470
## MonthlyIncome -0.092249731
## MonthlyRate -0.027470358
## NumCompaniesWorked 0.020578071
## OverTime -0.014026145
## PercentSalaryHike -0.021032050
## PerformanceRating -0.010154141
## RelationshipSatisfaction 0.044168514
## StockOptionLevel -0.009864499
## TotalWorkingYears -0.076482406
## TrainingTimesLastYear -0.035901571
## WorkLifeBalance 0.043886733
## YearsAtCompany -0.052568655
## YearsInCurrentRole -0.057875721
## YearsSinceLastPromotion -0.054603246
## YearsWithCurrManager -0.051005550
## NonTravel -0.015890181
## TravelFrequently 0.001896234
## TravelRarely 0.008961695
## SalesPos -0.126380773
## RDPos -0.263127572
## HRPos 0.904982811
## EducationLS -0.063118803
## EducationOther 0.001593718
## EducationMedical -0.042894950
## EducationMarketing -0.066690008
## EducationTechnical 0.549751407
## EducationHR -0.008622641
## MaritalSingle -0.052320302
## MaritalMarried 0.030994608
## MaritalDivorced 0.021540746
## SalesExecutive -0.102225524
## ResearchScienist -0.095341578
## LabTech -0.088560767
## ManufDir -0.063348979
## HealthRep -0.059897530
## Manager -0.052290235
## SalesRep -0.046845122
## ResearchDir -0.045941075
## HR 1.000000000library(tidyverse)## -- Attaching packages ------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## v ggplot2 2.2.1 v purrr 0.2.4
## v tibble 1.4.2 v dplyr 0.7.4
## v tidyr 0.8.0 v stringr 1.3.0
## v readr 1.1.1 v forcats 0.3.0## Warning: package 'ggplot2' was built under R version 3.4.4## -- Conflicts ---------------------------------------------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()ggplot(data = df, aes(x=df$YearsAtCompany,fill=factor(df$Attrition)))+
geom_histogram(binwidth = 3)+
scale_x_continuous("Years at Company")+
scale_y_discrete("Count")+
guides(fill=guide_legend(title="Attrition"))+
scale_fill_manual(values=c("blue","red")) 
ggplot(data = df, aes(x=df$MonthlyIncome,fill=factor(df$Attrition)))+
geom_histogram() +
scale_x_continuous("Monthly Income")+
scale_y_discrete("Count")+
guides(fill=guide_legend(title="Attrition"))+
scale_fill_manual(values=c("blue","red")) ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = df, aes(x=df$DailyRate,fill=factor(df$Attrition)))+
geom_histogram(binwidth = 25)+
scale_x_continuous("Daily Rate")+
scale_y_discrete("Count")+
guides(fill=guide_legend(title="Attrition"))+
scale_fill_manual(values=c("blue","red")) 
a <- cor(df[,-c(2)])
corrplot(a, method = "number")
library(PerformanceAnalytics)## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last##
## Attaching package: 'PerformanceAnalytics'## The following object is masked from 'package:graphics':
##
## legendchart.Correlation(df[,c(3:10)],histogram=TRUE)
#chart.Correlation(df[,c(3:15)],histogram=TRUE)library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphapairs.panels(df[,c(10:20)], ellipses=TRUE, pch=1, lm=TRUE, cex.cor=1, smoother=F, stars = T, main="IBM Attrition")
library(caTools)
library(ISLR)
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectlibrary(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftset.seed(222)
split <- sample.split(df$Attrition, SplitRatio = 0.80)
#get training and test data
dftrain <- subset(df, split == TRUE)
dftest <- subset(df, split == FALSE)library(stats)
LogReg <- train(Attrition~., data=dftrain,
method='glm', family=binomial(link='logit'),
preProcess=c('scale', 'center'))## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingsummary(LogReg)##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7091 -0.4636 -0.2367 -0.0746 3.5462
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.86473 5.27854 -0.543 0.587328
## Age -0.33332 0.14220 -2.344 0.019079 *
## DailyRate -0.10033 0.10038 -1.000 0.317530
## DistanceFromHome 0.39333 0.10044 3.916 9.00e-05 ***
## Education 0.06423 0.10406 0.617 0.537043
## EmployeeNumber -0.04837 0.10399 -0.465 0.641860
## EnvironmentSatisfaction -0.56144 0.10450 -5.372 7.77e-08 ***
## Gender -0.20316 0.10367 -1.960 0.050040 .
## HourlyRate 0.01394 0.10210 0.137 0.891370
## JobInvolvement -0.38254 0.09894 -3.867 0.000110 ***
## JobLevel -0.04886 0.39739 -0.123 0.902152
## JobSatisfaction -0.36358 0.10217 -3.559 0.000373 ***
## MonthlyIncome -0.16286 0.44012 -0.370 0.711354
## MonthlyRate 0.13645 0.10194 1.338 0.180740
## NumCompaniesWorked 0.54532 0.11039 4.940 7.82e-07 ***
## OverTime 0.96351 0.10081 9.558 < 2e-16 ***
## PercentSalaryHike -0.17541 0.16682 -1.052 0.293022
## PerformanceRating 0.11391 0.16759 0.680 0.496677
## RelationshipSatisfaction -0.28264 0.10259 -2.755 0.005868 **
## StockOptionLevel -0.12958 0.14944 -0.867 0.385879
## TotalWorkingYears -0.55524 0.25607 -2.168 0.030131 *
## TrainingTimesLastYear -0.21854 0.10744 -2.034 0.041941 *
## WorkLifeBalance -0.26334 0.10138 -2.598 0.009387 **
## YearsAtCompany 0.90345 0.26712 3.382 0.000719 ***
## YearsInCurrentRole -0.76769 0.19360 -3.965 7.33e-05 ***
## YearsSinceLastPromotion 0.62612 0.15241 4.108 3.99e-05 ***
## YearsWithCurrManager -0.58714 0.18317 -3.205 0.001349 **
## NonTravel -0.22207 0.12824 -1.732 0.083319 .
## TravelFrequently 0.36662 0.09376 3.910 9.23e-05 ***
## TravelRarely NA NA NA NA
## SalesPos 6.77696 285.41043 0.024 0.981056
## RDPos 6.26769 295.80141 0.021 0.983095
## HRPos NA NA NA NA
## EducationLS -0.49259 0.16931 -2.909 0.003621 **
## EducationOther -0.08428 0.11945 -0.706 0.480421
## EducationMedical -0.47227 0.16357 -2.887 0.003886 **
## EducationMarketing -0.12511 0.13976 -0.895 0.370682
## EducationTechnical -0.06592 0.12952 -0.509 0.610790
## EducationHR NA NA NA NA
## MaritalSingle 0.53020 0.18081 2.932 0.003363 **
## MaritalMarried 0.12985 0.14801 0.877 0.380303
## MaritalDivorced NA NA NA NA
## SalesExecutive -6.13338 255.79602 -0.024 0.980870
## ResearchScienist -5.49180 247.01540 -0.022 0.982262
## LabTech -4.88404 238.61650 -0.020 0.983670
## ManufDir -4.15344 187.19968 -0.022 0.982299
## HealthRep -3.83252 171.56021 -0.022 0.982177
## Manager -3.66432 157.19666 -0.023 0.981403
## SalesRep -3.32272 150.73029 -0.022 0.982413
## ResearchDir -3.13619 138.71807 -0.023 0.981963
## HR NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1040.18 on 1175 degrees of freedom
## Residual deviance: 668.21 on 1130 degrees of freedom
## AIC: 760.21
##
## Number of Fisher Scoring iterations: 15confusionMatrix(predict(LogReg, dftest), dftest$Attrition)## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 235 27
## Yes 12 20
##
## Accuracy : 0.8673
## 95% CI : (0.8231, 0.9039)
## No Information Rate : 0.8401
## P-Value [Acc > NIR] : 0.11460
##
## Kappa : 0.4329
## Mcnemar's Test P-Value : 0.02497
##
## Sensitivity : 0.9514
## Specificity : 0.4255
## Pos Pred Value : 0.8969
## Neg Pred Value : 0.6250
## Prevalence : 0.8401
## Detection Rate : 0.7993
## Detection Prevalence : 0.8912
## Balanced Accuracy : 0.6885
##
## 'Positive' Class : No
## LDA <- train(Attrition~., data=dftrain,
method='lda',
preProcess=c('scale', 'center'))## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinear
## Warning in lda.default(x, grouping, ...): variables are collinearLDA## Linear Discriminant Analysis
##
## 1176 samples
## 50 predictor
## 2 classes: 'No', 'Yes'
##
## Pre-processing: scaled (50), centered (50)
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 1176, 1176, 1176, 1176, 1176, 1176, ...
## Resampling results:
##
## Accuracy Kappa
## 0.8691368 0.4334666confusionMatrix(predict(LDA, dftrain), dftrain$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 960 94
## Yes 26 96
##
## Accuracy : 0.898
## 95% CI : (0.8792, 0.9147)
## No Information Rate : 0.8384
## P-Value [Acc > NIR] : 2.862e-09
##
## Kappa : 0.5598
## Mcnemar's Test P-Value : 9.581e-10
##
## Sensitivity : 0.9736
## Specificity : 0.5053
## Pos Pred Value : 0.9108
## Neg Pred Value : 0.7869
## Prevalence : 0.8384
## Detection Rate : 0.8163
## Detection Prevalence : 0.8963
## Balanced Accuracy : 0.7394
##
## 'Positive' Class : No
## confusionMatrix(predict(LDA, dftest), dftest$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 239 32
## Yes 8 15
##
## Accuracy : 0.8639
## 95% CI : (0.8194, 0.901)
## No Information Rate : 0.8401
## P-Value [Acc > NIR] : 0.1500429
##
## Kappa : 0.3615
## Mcnemar's Test P-Value : 0.0002762
##
## Sensitivity : 0.9676
## Specificity : 0.3191
## Pos Pred Value : 0.8819
## Neg Pred Value : 0.6522
## Prevalence : 0.8401
## Detection Rate : 0.8129
## Detection Prevalence : 0.9218
## Balanced Accuracy : 0.6434
##
## 'Positive' Class : No
## RPART <- train(Attrition ~ ., data=dftrain, method="rpart")
RPART## CART
##
## 1176 samples
## 50 predictor
## 2 classes: 'No', 'Yes'
##
## No pre-processing
## Resampling: Bootstrapped (25 reps)
## Summary of sample sizes: 1176, 1176, 1176, 1176, 1176, 1176, ...
## Resampling results across tuning parameters:
##
## cp Accuracy Kappa
## 0.01578947 0.8328835 0.26966092
## 0.01842105 0.8364535 0.26531013
## 0.07368421 0.8374209 0.07880081
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was cp = 0.07368421.confusionMatrix(predict(RPART, dftest), dftest$Attrition)## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 247 47
## Yes 0 0
##
## Accuracy : 0.8401
## 95% CI : (0.7931, 0.8801)
## No Information Rate : 0.8401
## P-Value [Acc > NIR] : 0.5388
##
## Kappa : 0
## Mcnemar's Test P-Value : 1.949e-11
##
## Sensitivity : 1.0000
## Specificity : 0.0000
## Pos Pred Value : 0.8401
## Neg Pred Value : NaN
## Prevalence : 0.8401
## Detection Rate : 0.8401
## Detection Prevalence : 1.0000
## Balanced Accuracy : 0.5000
##
## 'Positive' Class : No
## #write.csv(df, "C:/Users/dev/Documents/1 UW Tacoma/580 Social media/IBMAttritionClean.csv")