CaseStudy2 Work Place Attrition & Salary
Joseph Lazarus
2/17/2021
R Markdown
This R Markdown document is part of SMU’s Master’s in Data Science Program DS 6306 “Doing Data Science.” Student’s are given a data set and asked to make predictions using data science methods and techniques learned in the course. For this case study we are asumming that we have been hired by a company called DDSAnalytics that specializes in talent management. The company wants to gain a competitive edge by providing its customers with accurate predictions regarding attrition (employee turnover) and monthly salary.
We will start by importing the following data for analysis:
CaseStudy2-Data.csv:
- Later we will import the folowing data sets that will used to make our predictions for the class contest.
- CaseStudy2CompSet No Attritioin.csv
- CaseStudy2CompSet No Solary.csv
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.0 v stringr 1.4.0
## v tidyr 1.1.3 v forcats 0.5.1
## v readr 1.4.0## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## corrplot 0.84 loaded## Loading required package: grid## Loading required package: rpart##
## Attaching package: 'BBmisc'## The following object is masked from 'package:grid':
##
## explode## The following objects are masked from 'package:dplyr':
##
## coalesce, collapse## The following object is masked from 'package:base':
##
## isFALSELoad Theme for plots
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Data Preparation #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## ID Age Attrition BusinessTravel DailyRate Department
## 1 1 32 No Travel_Rarely 117 Sales
## 2 2 40 No Travel_Rarely 1308 Research & Development
## 3 3 35 No Travel_Frequently 200 Research & Development
## 4 4 32 No Travel_Rarely 801 Sales
## 5 5 24 No Travel_Frequently 567 Research & Development
## 6 6 27 No Travel_Frequently 294 Research & Development
## DistanceFromHome Education EducationField EmployeeCount EmployeeNumber
## 1 13 4 Life Sciences 1 859
## 2 14 3 Medical 1 1128
## 3 18 2 Life Sciences 1 1412
## 4 1 4 Marketing 1 2016
## 5 2 1 Technical Degree 1 1646
## 6 10 2 Life Sciences 1 733
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement JobLevel
## 1 2 Male 73 3 2
## 2 3 Male 44 2 5
## 3 3 Male 60 3 3
## 4 3 Female 48 3 3
## 5 1 Female 32 3 1
## 6 4 Male 32 3 3
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## 1 Sales Executive 4 Divorced 4403
## 2 Research Director 3 Single 19626
## 3 Manufacturing Director 4 Single 9362
## 4 Sales Executive 4 Married 10422
## 5 Research Scientist 4 Single 3760
## 6 Manufacturing Director 1 Divorced 8793
## MonthlyRate NumCompaniesWorked Over18 OverTime PercentSalaryHike
## 1 9250 2 Y No 11
## 2 17544 1 Y No 14
## 3 19944 2 Y No 11
## 4 24032 1 Y No 19
## 5 17218 1 Y Yes 13
## 6 4809 1 Y No 21
## PerformanceRating RelationshipSatisfaction StandardHours StockOptionLevel
## 1 3 3 80 1
## 2 3 1 80 0
## 3 3 3 80 0
## 4 3 3 80 2
## 5 3 3 80 0
## 6 4 3 80 2
## TotalWorkingYears TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 1 8 3 2 5
## 2 21 2 4 20
## 3 10 2 3 2
## 4 14 3 3 14
## 5 6 2 3 6
## 6 9 4 2 9
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 1 2 0 3
## 2 7 4 9
## 3 2 2 2
## 4 10 5 7
## 5 3 1 3
## 6 7 1 7## The following object is masked from package:vcd:
##
## JobSatisfaction## 'data.frame': 870 obs. of 36 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : chr "No" "No" "No" "No" ...
## $ BusinessTravel : chr "Travel_Rarely" "Travel_Rarely" "Travel_Frequently" "Travel_Rarely" ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : chr "Sales" "Research & Development" "Research & Development" "Sales" ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : chr "Life Sciences" "Medical" "Life Sciences" "Marketing" ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 859 1128 1412 2016 1646 733 1448 1105 1055 1597 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : chr "Male" "Male" "Male" "Female" ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : chr "Sales Executive" "Research Director" "Manufacturing Director" "Sales Executive" ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : chr "Divorced" "Single" "Single" "Married" ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ Over18 : chr "Y" "Y" "Y" "Y" ...
## $ OverTime : chr "No" "No" "No" "No" ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction: int 3 1 3 3 3 3 1 3 4 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...## [1] 870 36## integer(0)## ID Age Attrition
## 0 0 0
## BusinessTravel DailyRate Department
## 0 0 0
## DistanceFromHome Education EducationField
## 0 0 0
## EmployeeCount EmployeeNumber EnvironmentSatisfaction
## 0 0 0
## Gender HourlyRate JobInvolvement
## 0 0 0
## JobLevel JobRole JobSatisfaction
## 0 0 0
## MaritalStatus MonthlyIncome MonthlyRate
## 0 0 0
## NumCompaniesWorked Over18 OverTime
## 0 0 0
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 0 0 0
## StandardHours StockOptionLevel TotalWorkingYears
## 0 0 0
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany
## 0 0 0
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 0 0 0## ID Age Attrition BusinessTravel
## Min. : 1.0 Min. :18.00 Length:870 Length:870
## 1st Qu.:218.2 1st Qu.:30.00 Class :character Class :character
## Median :435.5 Median :35.00 Mode :character Mode :character
## Mean :435.5 Mean :36.83
## 3rd Qu.:652.8 3rd Qu.:43.00
## Max. :870.0 Max. :60.00
## DailyRate Department DistanceFromHome Education
## Min. : 103.0 Length:870 Min. : 1.000 Min. :1.000
## 1st Qu.: 472.5 Class :character 1st Qu.: 2.000 1st Qu.:2.000
## Median : 817.5 Mode :character Median : 7.000 Median :3.000
## Mean : 815.2 Mean : 9.339 Mean :2.901
## 3rd Qu.:1165.8 3rd Qu.:14.000 3rd Qu.:4.000
## Max. :1499.0 Max. :29.000 Max. :5.000
## EducationField EmployeeCount EmployeeNumber EnvironmentSatisfaction
## Length:870 Min. :1 Min. : 1.0 Min. :1.000
## Class :character 1st Qu.:1 1st Qu.: 477.2 1st Qu.:2.000
## Mode :character Median :1 Median :1039.0 Median :3.000
## Mean :1 Mean :1029.8 Mean :2.701
## 3rd Qu.:1 3rd Qu.:1561.5 3rd Qu.:4.000
## Max. :1 Max. :2064.0 Max. :4.000
## Gender HourlyRate JobInvolvement JobLevel
## Length:870 Min. : 30.00 Min. :1.000 Min. :1.000
## Class :character 1st Qu.: 48.00 1st Qu.:2.000 1st Qu.:1.000
## Mode :character Median : 66.00 Median :3.000 Median :2.000
## Mean : 65.61 Mean :2.723 Mean :2.039
## 3rd Qu.: 83.00 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :100.00 Max. :4.000 Max. :5.000
## JobRole JobSatisfaction MaritalStatus MonthlyIncome
## Length:870 Min. :1.000 Length:870 Min. : 1081
## Class :character 1st Qu.:2.000 Class :character 1st Qu.: 2840
## Mode :character Median :3.000 Mode :character Median : 4946
## Mean :2.709 Mean : 6390
## 3rd Qu.:4.000 3rd Qu.: 8182
## Max. :4.000 Max. :19999
## MonthlyRate NumCompaniesWorked Over18 OverTime
## Min. : 2094 Min. :0.000 Length:870 Length:870
## 1st Qu.: 8092 1st Qu.:1.000 Class :character Class :character
## Median :14074 Median :2.000 Mode :character Mode :character
## Mean :14326 Mean :2.728
## 3rd Qu.:20456 3rd Qu.:4.000
## Max. :26997 Max. :9.000
## PercentSalaryHike PerformanceRating RelationshipSatisfaction StandardHours
## Min. :11.0 Min. :3.000 Min. :1.000 Min. :80
## 1st Qu.:12.0 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:80
## Median :14.0 Median :3.000 Median :3.000 Median :80
## Mean :15.2 Mean :3.152 Mean :2.707 Mean :80
## 3rd Qu.:18.0 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:80
## Max. :25.0 Max. :4.000 Max. :4.000 Max. :80
## 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 :0.7839 Mean :11.05 Mean :2.832 Mean :2.782
## 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 : 6.962 Mean : 4.205 Mean : 2.169
## 3rd Qu.:10.000 3rd Qu.: 7.000 3rd Qu.: 3.000
## Max. :40.000 Max. :18.000 Max. :15.000
## YearsWithCurrManager
## Min. : 0.00
## 1st Qu.: 2.00
## Median : 3.00
## Mean : 4.14
## 3rd Qu.: 7.00
## Max. :17.00| Name | df |
| Number of rows | 870 |
| Number of columns | 36 |
| _______________________ | |
| Column type frequency: | |
| character | 9 |
| numeric | 27 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Attrition | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| BusinessTravel | 0 | 1 | 10 | 17 | 0 | 3 | 0 |
| Department | 0 | 1 | 5 | 22 | 0 | 3 | 0 |
| EducationField | 0 | 1 | 5 | 16 | 0 | 6 | 0 |
| Gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| JobRole | 0 | 1 | 7 | 25 | 0 | 9 | 0 |
| MaritalStatus | 0 | 1 | 6 | 8 | 0 | 3 | 0 |
| Over18 | 0 | 1 | 1 | 1 | 0 | 1 | 0 |
| OverTime | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ID | 0 | 1 | 435.50 | 251.29 | 1 | 218.25 | 435.5 | 652.75 | 870 | ▇▇▇▇▇ |
| Age | 0 | 1 | 36.83 | 8.93 | 18 | 30.00 | 35.0 | 43.00 | 60 | ▂▇▇▃▂ |
| DailyRate | 0 | 1 | 815.23 | 401.12 | 103 | 472.50 | 817.5 | 1165.75 | 1499 | ▇▇▇▇▇ |
| DistanceFromHome | 0 | 1 | 9.34 | 8.14 | 1 | 2.00 | 7.0 | 14.00 | 29 | ▇▅▂▂▂ |
| Education | 0 | 1 | 2.90 | 1.02 | 1 | 2.00 | 3.0 | 4.00 | 5 | ▂▅▇▆▁ |
| EmployeeCount | 0 | 1 | 1.00 | 0.00 | 1 | 1.00 | 1.0 | 1.00 | 1 | ▁▁▇▁▁ |
| EmployeeNumber | 0 | 1 | 1029.83 | 604.79 | 1 | 477.25 | 1039.0 | 1561.50 | 2064 | ▇▇▇▇▇ |
| EnvironmentSatisfaction | 0 | 1 | 2.70 | 1.10 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▆▁▇▇ |
| HourlyRate | 0 | 1 | 65.61 | 20.13 | 30 | 48.00 | 66.0 | 83.00 | 100 | ▇▇▆▇▇ |
| JobInvolvement | 0 | 1 | 2.72 | 0.70 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▁ |
| JobLevel | 0 | 1 | 2.04 | 1.09 | 1 | 1.00 | 2.0 | 3.00 | 5 | ▇▇▃▂▁ |
| JobSatisfaction | 0 | 1 | 2.71 | 1.11 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| MonthlyIncome | 0 | 1 | 6390.26 | 4597.70 | 1081 | 2839.50 | 4945.5 | 8182.00 | 19999 | ▇▅▂▁▁ |
| MonthlyRate | 0 | 1 | 14325.62 | 7108.38 | 2094 | 8092.00 | 14074.5 | 20456.25 | 26997 | ▇▇▇▇▇ |
| NumCompaniesWorked | 0 | 1 | 2.73 | 2.52 | 0 | 1.00 | 2.0 | 4.00 | 9 | ▇▃▂▂▁ |
| PercentSalaryHike | 0 | 1 | 15.20 | 3.68 | 11 | 12.00 | 14.0 | 18.00 | 25 | ▇▅▃▂▁ |
| PerformanceRating | 0 | 1 | 3.15 | 0.36 | 3 | 3.00 | 3.0 | 3.00 | 4 | ▇▁▁▁▂ |
| RelationshipSatisfaction | 0 | 1 | 2.71 | 1.10 | 1 | 2.00 | 3.0 | 4.00 | 4 | ▅▅▁▇▇ |
| StandardHours | 0 | 1 | 80.00 | 0.00 | 80 | 80.00 | 80.0 | 80.00 | 80 | ▁▁▇▁▁ |
| StockOptionLevel | 0 | 1 | 0.78 | 0.86 | 0 | 0.00 | 1.0 | 1.00 | 3 | ▇▇▁▂▁ |
| TotalWorkingYears | 0 | 1 | 11.05 | 7.51 | 0 | 6.00 | 10.0 | 15.00 | 40 | ▇▇▂▁▁ |
| TrainingTimesLastYear | 0 | 1 | 2.83 | 1.27 | 0 | 2.00 | 3.0 | 3.00 | 6 | ▂▇▇▂▃ |
| WorkLifeBalance | 0 | 1 | 2.78 | 0.71 | 1 | 2.00 | 3.0 | 3.00 | 4 | ▁▃▁▇▂ |
| YearsAtCompany | 0 | 1 | 6.96 | 6.02 | 0 | 3.00 | 5.0 | 10.00 | 40 | ▇▃▁▁▁ |
| YearsInCurrentRole | 0 | 1 | 4.20 | 3.64 | 0 | 2.00 | 3.0 | 7.00 | 18 | ▇▃▂▁▁ |
| YearsSinceLastPromotion | 0 | 1 | 2.17 | 3.19 | 0 | 0.00 | 1.0 | 3.00 | 15 | ▇▁▁▁▁ |
| YearsWithCurrManager | 0 | 1 | 4.14 | 3.57 | 0 | 2.00 | 3.0 | 7.00 | 17 | ▇▂▅▁▁ |
## 'data.frame': 870 obs. of 38 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction: int 3 1 3 3 3 3 1 3 4 2 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...
## $ iJobRole : int 8 6 5 8 7 5 7 8 9 1 ...
## $ iDepartment : int 3 2 2 3 2 2 2 3 3 2 ...
## $ iMaritalStatus : int 1 3 3 2 3 1 2 1 2 2 ...
## $ iBusinessTravel : int 3 3 2 3 2 2 3 3 3 2 ...
## $ iEducation : int 4 3 2 4 1 2 5 4 4 4 ...
## $ iAttrition : int 1 1 1 1 1 1 1 1 1 1 ...#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Exploratoration into Data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## 0% 25% 50% 75% 100%
## 18 30 35 43 60## No Yes
## 37.41233 33.78571## 0% 25% 50% 75% 100%
## 0 1 2 4 9## No Yes
## 2.660274 3.078571## 0% 25% 50% 75% 100%
## 11 12 14 18 25## No Yes
## 15.17534 15.32857## 0% 25% 50% 75% 100%
## 0 6 10 15 40## No Yes
## 11.602740 8.185714## 0% 25% 50% 75% 100%
## 1081.0 2839.5 4945.5 8182.0 19999.0## No Yes
## 6702.000 4764.786## 0% 25% 50% 75% 100%
## 0 2 3 7 18## No Yes
## 4.453425 2.907143## 0% 25% 50% 75% 100%
## 0 2 3 7 17## No Yes
## 4.369863 2.942857
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## 0% 25% 50% 75% 100%
## 1081.0 2839.5 4945.5 8182.0 19999.0## 0% 25% 50% 75% 100%
## 0 2 3 7 18## No Yes
## 4.453425 2.907143## 0% 25% 50% 75% 100%
## 0 2 3 7 18## No Yes
## 4.453425 2.907143## 0% 25% 50% 75% 100%
## 0 3 5 10 40## No Yes
## 7.301370 5.192857## integer(0)## 'data.frame': 870 obs. of 43 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Age : int 32 40 35 32 24 27 41 37 34 34 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 2 3 2 2 3 3 3 2 ...
## $ DailyRate : int 117 1308 200 801 567 294 1283 309 1333 653 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 2 3 2 2 2 3 3 2 ...
## $ DistanceFromHome : int 13 14 18 1 2 10 5 10 10 10 ...
## $ Education : int 4 3 2 4 1 2 5 4 4 4 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 4 2 3 6 2 4 2 2 6 ...
## $ EnvironmentSatisfaction : int 2 3 3 3 1 4 2 4 3 4 ...
## $ Gender : Factor w/ 2 levels "Female","Male": 2 2 2 1 1 2 2 1 1 2 ...
## $ HourlyRate : int 73 44 60 48 32 32 90 88 87 92 ...
## $ JobInvolvement : int 3 2 3 3 3 3 4 2 3 2 ...
## $ JobLevel : int 2 5 3 3 1 3 1 2 1 2 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 6 5 8 7 5 7 8 9 1 ...
## $ JobSatisfaction : int 4 3 4 4 4 1 3 4 3 3 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 3 3 2 3 1 2 1 2 2 ...
## $ MonthlyIncome : int 4403 19626 9362 10422 3760 8793 2127 6694 2220 5063 ...
## $ MonthlyRate : int 9250 17544 19944 24032 17218 4809 5561 24223 18410 15332 ...
## $ NumCompaniesWorked : int 2 1 2 1 1 1 2 2 1 1 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 1 2 2 2 1 ...
## $ PercentSalaryHike : int 11 14 11 19 13 21 12 14 19 14 ...
## $ PerformanceRating : int 3 3 3 3 3 4 3 3 3 3 ...
## $ RelationshipSatisfaction : int 3 1 3 3 3 3 1 3 4 2 ...
## $ StockOptionLevel : int 1 0 0 2 0 2 0 3 1 1 ...
## $ TotalWorkingYears : int 8 21 10 14 6 9 7 8 1 8 ...
## $ TrainingTimesLastYear : int 3 2 2 3 2 4 5 5 2 3 ...
## $ WorkLifeBalance : int 2 4 3 3 3 2 2 3 3 2 ...
## $ YearsAtCompany : int 5 20 2 14 6 9 4 1 1 8 ...
## $ YearsInCurrentRole : int 2 7 2 10 3 7 2 0 1 2 ...
## $ YearsSinceLastPromotion : int 0 4 2 5 1 1 0 0 0 7 ...
## $ YearsWithCurrManager : int 3 9 2 7 3 7 3 0 0 7 ...
## $ iJobRole : int 8 6 5 8 7 5 7 8 9 1 ...
## $ iDepartment : int 3 2 2 3 2 2 2 3 3 2 ...
## $ iMaritalStatus : int 1 3 3 2 3 1 2 1 2 2 ...
## $ iBusinessTravel : int 3 3 2 3 2 2 3 3 3 2 ...
## $ iEducation : int 4 3 2 4 1 2 5 4 4 4 ...
## $ iAttrition : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Age.Group : Factor w/ 4 levels "Senior","Undergrad",..: 4 3 3 4 4 4 3 3 4 4 ...
## $ MonthlyIncome.Group : Factor w/ 4 levels "Above.Avg","Avg",..: 2 3 3 3 2 3 4 1 4 1 ...
## $ YearsWithCurrManager.Group: Factor w/ 4 levels "2thru4","4thru6",..: 1 3 4 3 1 3 1 4 4 3 ...
## $ YearsInCurrentRole.Group : Factor w/ 4 levels "5&above","Lessthan2",..: 2 1 2 1 3 1 2 2 2 2 ...
## $ YearsAtCompany.Group : Factor w/ 4 levels "10&above","3thru5",..: 2 1 4 1 3 3 2 4 4 3 ...## [1] 43#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Prepare data for Modeling Train Test SPlit #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## [1] 870 17## [1] 609 17## [1] 261 17## 'data.frame': 609 obs. of 17 variables:
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Age.Group : Factor w/ 4 levels "Senior","Undergrad",..: 3 3 4 4 4 3 1 3 4 4 ...
## $ DistanceFromHome : int 9 11 8 24 5 2 29 1 10 3 ...
## $ MonthlyIncome.Group : Factor w/ 4 levels "Above.Avg","Avg",..: 3 4 4 2 2 3 2 1 2 1 ...
## $ TotalWorkingYears : int 24 5 8 6 7 20 9 10 5 6 ...
## $ OverTime : Factor w/ 2 levels "No","Yes": 1 2 1 1 2 1 1 1 2 1 ...
## $ YearsAtCompany : int 1 2 3 4 6 19 6 9 5 2 ...
## $ StockOptionLevel : int 0 1 0 0 2 0 0 0 1 0 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 4 3 2 3 7 1 7 1 7 8 ...
## $ JobLevel : int 5 1 1 1 1 3 1 2 1 2 ...
## $ JobInvolvement : int 2 3 4 3 4 3 3 4 3 1 ...
## $ Education : int 2 4 2 3 2 4 3 4 4 2 ...
## $ EnvironmentSatisfaction: int 4 4 4 4 1 3 3 2 4 4 ...
## $ WorkLifeBalance : int 3 3 3 3 2 3 2 2 4 3 ...
## $ YearsInCurrentRole : int 0 2 2 3 2 6 5 7 3 2 ...
## $ YearsAtCompany.Group : Factor w/ 4 levels "10&above","3thru5",..: 4 4 4 2 3 1 3 3 2 4 ...
## $ YearsWithCurrManager : int 1 2 2 2 5 8 3 8 0 2 ...#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # find important Variables #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Type 'citation("pROC")' for a citation.##
## Attaching package: 'pROC'## The following objects are masked from 'package:stats':
##
## cov, smooth, var## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## lift## No Yes
## Age.Group 0.5103284 0.5103284
## DistanceFromHome 0.5786201 0.5786201
## MonthlyIncome.Group 0.6164635 0.6164635
## TotalWorkingYears 0.6683950 0.6683950
## OverTime 0.6629622 0.6629622
## YearsAtCompany 0.6513220 0.6513220## No Yes
## Age.Group 0.5103284 0.5103284
## DistanceFromHome 0.5786201 0.5786201
## MonthlyIncome.Group 0.6164635 0.6164635
## TotalWorkingYears 0.6683950 0.6683950
## OverTime 0.6629622 0.6629622
## YearsAtCompany 0.6513220 0.6513220
## StockOptionLevel 0.6624561 0.6624561
## JobRole 0.5880397 0.5880397
## JobLevel 0.6551126 0.6551126
## JobInvolvement 0.6287647 0.6287647
## Education 0.5766267 0.5766267
## EnvironmentSatisfaction 0.5530366 0.5530366
## WorkLifeBalance 0.5292605 0.5292605
## YearsInCurrentRole 0.6472423 0.6472423
## YearsAtCompany.Group 0.6029952 0.6029952
## YearsWithCurrManager 0.6325759 0.6325759## [1] 0.6683950 0.6629622 0.6624561 0.6551126 0.6513220 0.6472423 0.6325759
## [8] 0.6287647 0.6164635 0.6029952 0.5880397 0.5786201 0.5766267 0.5530366
## [15] 0.5292605 0.5103284#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Begin Modeling #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 1. Support Vector Model #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 215 45
## Yes 0 1
##
## Accuracy : 0.8276
## 95% CI : (0.7762, 0.8714)
## No Information Rate : 0.8238
## P-Value [Acc > NIR] : 0.4746
##
## Kappa : 0.0353
##
## Mcnemar's Test P-Value : 5.412e-11
##
## Sensitivity : 1.00000
## Specificity : 0.02174
## Pos Pred Value : 0.82692
## Neg Pred Value : 1.00000
## Prevalence : 0.82375
## Detection Rate : 0.82375
## Detection Prevalence : 0.99617
## Balanced Accuracy : 0.51087
##
## 'Positive' Class : No
## #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 2. Model Decesion Tree #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Attrition is 0.00 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Above.Avg or Avg or High
## JobRole is Healthcare Representative or Human Resources or Laboratory Technician or Manufacturing Director or Research Director or Research Scientist
## YearsAtCompany.Group is 10&above or 3thru5
##
## Attrition is 0.06 when
## OverTime is Yes
## StockOptionLevel >= 1
## JobLevel >= 2
##
## Attrition is 0.07 when
## OverTime is No
## TotalWorkingYears >= 3
##
## Attrition is 0.08 when
## OverTime is No
## StockOptionLevel >= 1
## TotalWorkingYears < 3
##
## Attrition is 0.15 when
## OverTime is Yes
## StockOptionLevel >= 1
## DistanceFromHome < 13
## JobLevel < 2
##
## Attrition is 0.19 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Above.Avg or Avg or High
## JobRole is Healthcare Representative or Human Resources or Laboratory Technician or Manufacturing Director or Research Director or Research Scientist
## YearsAtCompany.Group is 5thru10 or LessThan3
## JobInvolvement >= 3
##
## Attrition is 0.38 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Above.Avg or Avg or High
## JobRole is Manager or Sales Executive or Sales Representative
## DistanceFromHome < 8
##
## Attrition is 0.38 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Low
## Age.Group is Veteran
##
## Attrition is 0.62 when
## OverTime is No
## StockOptionLevel < 1
## TotalWorkingYears < 3
##
## Attrition is 0.67 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Above.Avg or Avg or High
## JobRole is Healthcare Representative or Human Resources or Laboratory Technician or Manufacturing Director or Research Director or Research Scientist
## YearsAtCompany.Group is 5thru10 or LessThan3
## JobInvolvement < 3
##
## Attrition is 0.75 when
## OverTime is Yes
## StockOptionLevel >= 1
## DistanceFromHome >= 13
## JobLevel < 2
##
## Attrition is 0.88 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Above.Avg or Avg or High
## JobRole is Manager or Sales Executive or Sales Representative
## DistanceFromHome >= 8
##
## Attrition is 0.89 when
## OverTime is Yes
## StockOptionLevel < 1
## MonthlyIncome.Group is Low
## Age.Group is Undergrad or Young-Professional
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 201 30
## Yes 14 16
##
## Accuracy : 0.8314
## 95% CI : (0.7804, 0.8748)
## No Information Rate : 0.8238
## P-Value [Acc > NIR] : 0.41022
##
## Kappa : 0.3275
##
## Mcnemar's Test P-Value : 0.02374
##
## Sensitivity : 0.9349
## Specificity : 0.3478
## Pos Pred Value : 0.8701
## Neg Pred Value : 0.5333
## Prevalence : 0.8238
## Detection Rate : 0.7701
## Detection Prevalence : 0.8851
## Balanced Accuracy : 0.6414
##
## 'Positive' Class : No
## #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # 3. KNN Model #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Confusion Matrix and Statistics
##
## Reference
## Prediction No Yes
## No 209 43
## Yes 6 3
##
## Accuracy : 0.8123
## 95% CI : (0.7595, 0.8578)
## No Information Rate : 0.8238
## P-Value [Acc > NIR] : 0.7192
##
## Kappa : 0.0546
##
## Mcnemar's Test P-Value : 2.706e-07
##
## Sensitivity : 0.97209
## Specificity : 0.06522
## Pos Pred Value : 0.82937
## Neg Pred Value : 0.33333
## Prevalence : 0.82375
## Detection Rate : 0.80077
## Detection Prevalence : 0.96552
## Balanced Accuracy : 0.51866
##
## 'Positive' Class : No
## #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Hyper Parameter tunning #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
