Henglong_IST707_hw1
Henglong
9/23/2019
Load the data.
library(arules)## Loading required package: Matrix##
## Attaching package: 'arules'## The following objects are masked from 'package:base':
##
## abbreviate, writelibrary(arulesViz)## Loading required package: gridattrition_data<-read.csv("employee_attrition.csv",sep = ",",header = T)
DataCopy<- attrition_dataUnderstand the data
#View(DataCopy)
str(DataCopy)## 'data.frame': 1176 obs. of 35 variables:
## $ Age : int 30 52 42 55 35 51 42 23 38 27 ...
## $ Attrition : Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 2 ...
## $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 3 3 1 3 3 3 3 3 3 ...
## $ DailyRate : int 1358 1325 462 177 1029 1318 932 507 1153 1420 ...
## $ Department : Factor w/ 3 levels "Human Resources",..: 3 2 3 2 2 3 2 2 2 3 ...
## $ DistanceFromHome : int 16 11 14 8 16 26 1 20 6 2 ...
## $ Education : int 1 4 2 1 3 4 2 1 2 1 ...
## $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 4 4 2 3 2 2 5 3 ...
## $ EmployeeCount : int 1 1 1 1 1 1 1 1 1 1 ...
## $ EmployeeNumber : int 1479 813 936 1278 1529 851 827 1533 1782 667 ...
## $ EnvironmentSatisfaction : int 4 4 3 4 4 1 4 1 4 3 ...
## $ Gender : Factor w/ 3 levels "","Female","Male": 3 2 2 3 2 2 2 3 2 3 ...
## $ HourlyRate : int 96 82 68 37 91 66 43 97 40 85 ...
## $ JobInvolvement : int 3 3 2 2 2 3 2 3 2 3 ...
## $ JobLevel : int 2 2 2 4 3 4 2 2 1 1 ...
## $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 8 3 8 1 1 4 5 3 3 9 ...
## $ JobSatisfaction : int 3 3 3 2 2 3 4 3 3 1 ...
## $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 2 2 3 1 3 2 2 3 2 1 ...
## $ MonthlyIncome : int 5301 3149 6244 13577 8606 16307 6062 2272 3702 3041 ...
## $ MonthlyRate : int 2939 21821 7824 25592 21195 5594 4051 24812 16376 16346 ...
## $ NumCompaniesWorked : int 8 8 7 1 1 2 9 0 1 0 ...
## $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ...
## $ OverTime : Factor w/ 3 levels "","No","Yes": 2 2 2 3 2 2 3 2 2 2 ...
## $ PercentSalaryHike : int 15 20 17 15 19 14 13 14 11 11 ...
## $ PerformanceRating : int 3 4 3 3 3 3 3 3 3 NA ...
## $ RelationshipSatisfaction: int 3 2 1 4 4 3 4 2 2 2 ...
## $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ...
## $ StockOptionLevel : int 2 1 0 1 0 1 1 0 1 1 ...
## $ TotalWorkingYears : int 4 9 10 34 11 29 8 5 5 5 ...
## $ TrainingTimesLastYear : int 2 3 6 3 3 2 4 2 3 3 ...
## $ WorkLifeBalance : int 2 3 3 3 1 2 3 3 3 3 ...
## $ YearsAtCompany : int 2 5 5 33 11 20 4 4 5 4 ...
## $ YearsInCurrentRole : int 1 2 4 9 8 6 3 3 4 3 ...
## $ YearsSinceLastPromotion : int 2 1 0 15 3 4 0 1 0 0 ...
## $ YearsWithCurrManager : int 2 4 3 0 3 17 2 2 4 2 ...summary(DataCopy)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 No :991 Non-Travel :110 Min. : 102.0
## 1st Qu.:30.00 Yes:185 Travel_Frequently:227 1st Qu.: 461.8
## Median :36.00 Travel_Rarely :839 Median : 796.0
## Mean :36.96 Mean : 800.4
## 3rd Qu.:43.00 3rd Qu.:1162.0
## Max. :60.00 Max. :1499.0
##
## Department DistanceFromHome Education
## Human Resources : 54 Min. : 1.000 Min. :1.000
## Research & Development:764 1st Qu.: 2.000 1st Qu.:2.000
## Sales :358 Median : 7.000 Median :3.000
## Mean : 9.496 Mean :2.895
## 3rd Qu.: 14.000 3rd Qu.:4.000
## Max. :224.000 Max. :5.000
## NA's :2
## EducationField EmployeeCount EmployeeNumber
## Human Resources : 25 Min. :1 Min. : 1.0
## Life Sciences :477 1st Qu.:1 1st Qu.: 499.8
## Marketing :127 Median :1 Median :1032.5
## Medical :381 Mean :1 Mean :1036.4
## Other : 69 3rd Qu.:1 3rd Qu.:1574.5
## Technical Degree: 97 Max. :1 Max. :2068.0
##
## EnvironmentSatisfaction Gender HourlyRate JobInvolvement
## Min. :1.000 : 1 Min. : 30.00 Min. :1.000
## 1st Qu.:2.000 Female:482 1st Qu.: 48.00 1st Qu.:2.000
## Median :3.000 Male :693 Median : 66.00 Median :3.000
## Mean :2.705 Mean : 65.82 Mean :2.741
## 3rd Qu.:4.000 3rd Qu.: 83.00 3rd Qu.:3.000
## Max. :4.000 Max. :100.00 Max. :4.000
##
## JobLevel JobRole JobSatisfaction
## Min. :1.000 Sales Executive :263 Min. :1.00
## 1st Qu.:1.000 Research Scientist :220 1st Qu.:2.00
## Median :2.000 Laboratory Technician :209 Median :3.00
## Mean :2.069 Manufacturing Director :122 Mean :2.71
## 3rd Qu.:3.000 Healthcare Representative:108 3rd Qu.:4.00
## Max. :5.000 Manager : 79 Max. :4.00
## NA's :1 (Other) :175
## MaritalStatus MonthlyIncome MonthlyRate NumCompaniesWorked
## Divorced:266 Min. : 1009 Min. : 2094 Min. :0.000
## Married :545 1st Qu.: 2954 1st Qu.: 8275 1st Qu.:1.000
## Single :365 Median : 4950 Median :14488 Median :2.000
## Mean : 6526 Mean :14468 Mean :2.709
## 3rd Qu.: 8354 3rd Qu.:20627 3rd Qu.:4.000
## Max. :19973 Max. :26999 Max. :9.000
##
## Over18 OverTime PercentSalaryHike PerformanceRating
## Y:1176 : 1 Min. :11.0 Min. :3.000
## No :838 1st Qu.:12.0 1st Qu.:3.000
## Yes:337 Median :14.0 Median :3.000
## Mean :15.3 Mean :3.163
## 3rd Qu.:18.0 3rd Qu.:3.000
## Max. :25.0 Max. :4.000
## NA's :1 NA's :1
## RelationshipSatisfaction StandardHours StockOptionLevel TotalWorkingYears
## Min. :1.000 Min. :80 Min. :0.0000 Min. : 0.0
## 1st Qu.:2.000 1st Qu.:80 1st Qu.:0.0000 1st Qu.: 6.0
## Median :3.000 Median :80 Median :1.0000 Median : 10.0
## Mean :2.718 Mean :80 Mean :0.7959 Mean : 11.4
## 3rd Qu.:4.000 3rd Qu.:80 3rd Qu.:1.0000 3rd Qu.: 15.0
## Max. :4.000 Max. :80 Max. :3.0000 Max. :114.0
## NA's :1 NA's :2
## TrainingTimesLastYear WorkLifeBalance YearsAtCompany YearsInCurrentRole
## Min. :0.00 Min. :1.000 Min. : 0.000 Min. : 0.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.: 3.000 1st Qu.: 2.000
## Median :3.00 Median :3.000 Median : 5.000 Median : 3.000
## Mean :2.81 Mean :2.747 Mean : 6.918 Mean : 4.151
## 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.: 9.000 3rd Qu.: 7.000
## Max. :6.00 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 : 2.125 Mean : 4.242
## 3rd Qu.: 2.000 3rd Qu.: 7.000
## Max. :15.000 Max. :219.000
## NA's :1Data Preprocessing & cleaning
DataCopy$EmployeeCount<-NULL
DataCopy$Over18<-NULL
DataCopy$StandardHours<-NULL
DataCopy$EmployeeNumber<-NULL
DataCopy$NumCompaniesWorked<-NULL
DataCopy$TrainingTimesLastYear<-NULL
DataCopy$JobSatisfaction <- as.factor(DataCopy$JobSatisfaction)
DataCopy$RelationshipSatisfaction <- as.factor(DataCopy$RelationshipSatisfaction)
DataCopy$PerformanceRating <- as.factor(DataCopy$PerformanceRating)
DataCopy$JobLevel <- as.factor(DataCopy$JobLevel)
DataCopy$Education <- as.factor(DataCopy$Education)
DataCopy$EnvironmentSatisfaction <- as.factor(DataCopy$EnvironmentSatisfaction)
DataCopy$HourlyRate <- as.factor(DataCopy$HourlyRate)
DataCopy$JobInvolvement <- as.factor(DataCopy$JobInvolvement)
DataCopy$StockOptionLevel <- as.factor(DataCopy$StockOptionLevel)
DataCopy$WorkLifeBalance <- as.factor(DataCopy$WorkLifeBalance)
DataCopy$DistanceFromHome <- as.factor(DataCopy$DistanceFromHome)
DataCopy$Gender <- as.factor(DataCopy$Gender)
DataCopy$PercentSalaryHike <- as.factor(DataCopy$PercentSalaryHike)
DataCopy$YearsSinceLastPromotion <- as.factor(DataCopy$YearsSinceLastPromotion)
DataCopy$DailyRate<-as.factor(DataCopy$DailyRate)
DataCopy$MonthlyIncome <- as.factor(DataCopy$MonthlyIncome)
DataCopy$MonthlyRate <- as.factor(DataCopy$MonthlyRate)
DataCopy$YearsInCurrentRole<-as.factor(DataCopy$YearsInCurrentRole)
DataCopy$YearsAtCompany<-as.factor(DataCopy$YearsAtCompany)
DataCopy$YearsWithCurrManager<-as.factor(DataCopy$YearsWithCurrManager)
summary(DataCopy)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 No :991 Non-Travel :110 329 : 5
## 1st Qu.:30.00 Yes:185 Travel_Frequently:227 408 : 5
## Median :36.00 Travel_Rarely :839 530 : 5
## Mean :36.96 691 : 5
## 3rd Qu.:43.00 147 : 4
## Max. :60.00 267 : 4
## (Other):1148
## Department DistanceFromHome Education
## Human Resources : 54 2 :168 1:138
## Research & Development:764 1 :154 2:235
## Sales :358 7 : 73 3:452
## 9 : 72 4:314
## 3 : 68 5: 37
## (Other):639
## NA's : 2
## EducationField EnvironmentSatisfaction Gender
## Human Resources : 25 1:233 : 1
## Life Sciences :477 2:229 Female:482
## Marketing :127 3:366 Male :693
## Medical :381 4:348
## Other : 69
## Technical Degree: 97
##
## HourlyRate JobInvolvement JobLevel JobRole
## 98 : 26 1: 67 1 :430 Sales Executive :263
## 54 : 25 2:295 2 :432 Research Scientist :220
## 42 : 23 3:690 3 :172 Laboratory Technician :209
## 43 : 23 4:124 4 : 84 Manufacturing Director :122
## 48 : 23 5 : 57 Healthcare Representative:108
## 66 : 23 NA's: 1 Manager : 79
## (Other):1033 (Other) :175
## JobSatisfaction MaritalStatus MonthlyIncome MonthlyRate OverTime
## 1:244 Divorced:266 2342 : 4 4223 : 3 : 1
## 2:216 Married :545 2559 : 3 2125 : 2 No :838
## 3:353 Single :365 2741 : 3 3339 : 2 Yes:337
## 4:363 3452 : 3 4658 : 2
## 5562 : 3 5355 : 2
## 6142 : 3 6319 : 2
## (Other):1157 (Other):1163
## PercentSalaryHike PerformanceRating RelationshipSatisfaction
## 11 :169 3 :984 1 :220
## 13 :167 4 :191 2 :242
## 12 :152 NA's: 1 3 :362
## 14 :150 4 :351
## 15 : 80 NA's: 1
## (Other):457
## NA's : 1
## StockOptionLevel TotalWorkingYears WorkLifeBalance YearsAtCompany
## 0:500 Min. : 0.0 1: 65 5 :163
## 1:482 1st Qu.: 6.0 2:272 1 :130
## 2:128 Median : 10.0 3:734 2 :107
## 3: 66 Mean : 11.4 4:105 3 :103
## 3rd Qu.: 15.0 4 : 94
## Max. :114.0 10 : 94
## NA's :2 (Other):485
## YearsInCurrentRole YearsSinceLastPromotion YearsWithCurrManager
## 2 :305 0 :472 2 :292
## 0 :188 1 :289 0 :206
## 7 :186 2 :123 7 :170
## 3 :114 7 : 58 3 :113
## 4 : 86 4 : 49 4 : 78
## 8 : 69 (Other):184 8 : 75
## (Other):228 NA's : 1 (Other):242distance<-as.character(DataCopy$DistanceFromHome)
distance[is.na(distance)]<-'Not Known'
DataCopy$DistanceFromHome<-as.factor(distance)
job_level<-as.character(DataCopy$JobLevel)
job_level[is.na(job_level)]<-'Not Known'
DataCopy$JobLevel<-as.factor(job_level)
percent_hike<-as.character(DataCopy$PercentSalaryHike)
percent_hike[is.na(percent_hike)]<-'Not Known'
DataCopy$PercentSalaryHike<-as.factor(percent_hike)
performance<-as.character(DataCopy$PerformanceRating)
performance[is.na(performance)]<-'Not Known'
DataCopy$PerformanceRating<-as.factor(performance)
relation<-as.character(DataCopy$RelationshipSatisfaction)
relation[is.na(relation)]<-'Not Known'
DataCopy$RelationshipSatisfaction<-as.factor(relation)
tot<-as.character(DataCopy$TotalWorkingYears)
tot[is.na(tot)]<-'Not Known'
DataCopy$TotalWorkingYears<-as.factor(tot)
years<-as.character(DataCopy$YearsSinceLastPromotion)
years[is.na(years)]<-'Not Known'
DataCopy$YearsSinceLastPromotion<-as.factor(years)
summary(DataCopy)## Age Attrition BusinessTravel DailyRate
## Min. :18.00 No :991 Non-Travel :110 329 : 5
## 1st Qu.:30.00 Yes:185 Travel_Frequently:227 408 : 5
## Median :36.00 Travel_Rarely :839 530 : 5
## Mean :36.96 691 : 5
## 3rd Qu.:43.00 147 : 4
## Max. :60.00 267 : 4
## (Other):1148
## Department DistanceFromHome Education
## Human Resources : 54 2 :168 1:138
## Research & Development:764 1 :154 2:235
## Sales :358 7 : 73 3:452
## 9 : 72 4:314
## 10 : 68 5: 37
## 3 : 68
## (Other):573
## EducationField EnvironmentSatisfaction Gender
## Human Resources : 25 1:233 : 1
## Life Sciences :477 2:229 Female:482
## Marketing :127 3:366 Male :693
## Medical :381 4:348
## Other : 69
## Technical Degree: 97
##
## HourlyRate JobInvolvement JobLevel
## 98 : 26 1: 67 1 :430
## 54 : 25 2:295 2 :432
## 42 : 23 3:690 3 :172
## 43 : 23 4:124 4 : 84
## 48 : 23 5 : 57
## 66 : 23 Not Known: 1
## (Other):1033
## JobRole JobSatisfaction MaritalStatus
## Sales Executive :263 1:244 Divorced:266
## Research Scientist :220 2:216 Married :545
## Laboratory Technician :209 3:353 Single :365
## Manufacturing Director :122 4:363
## Healthcare Representative:108
## Manager : 79
## (Other) :175
## MonthlyIncome MonthlyRate OverTime PercentSalaryHike
## 2342 : 4 4223 : 3 : 1 11 :169
## 2559 : 3 2125 : 2 No :838 13 :167
## 2741 : 3 3339 : 2 Yes:337 12 :152
## 3452 : 3 4658 : 2 14 :150
## 5562 : 3 5355 : 2 15 : 80
## 6142 : 3 6319 : 2 18 : 73
## (Other):1157 (Other):1163 (Other):385
## PerformanceRating RelationshipSatisfaction StockOptionLevel
## 3 :984 1 :220 0:500
## 4 :191 2 :242 1:482
## Not Known: 1 3 :362 2:128
## 4 :351 3: 66
## Not Known: 1
##
##
## TotalWorkingYears WorkLifeBalance YearsAtCompany YearsInCurrentRole
## 10 :166 1: 65 5 :163 2 :305
## 6 : 95 2:272 1 :130 0 :188
## 8 : 87 3:734 2 :107 7 :186
## 9 : 74 4:105 3 :103 3 :114
## 5 : 73 4 : 94 4 : 86
## 7 : 67 10 : 94 8 : 69
## (Other):614 (Other):485 (Other):228
## YearsSinceLastPromotion YearsWithCurrManager
## 0 :472 2 :292
## 1 :289 0 :206
## 2 :123 7 :170
## 7 : 58 3 :113
## 4 : 49 4 : 78
## 3 : 42 8 : 75
## (Other):143 (Other):242Ploting
# Column 'Age'
hist(DataCopy$Age,xlab = 'Age',main = 'Histogram of Age',col = blues9)
DataCopy$Age_Group<-DataCopy$Age
DataCopy$Age_Group<-ifelse((DataCopy$Age>=18 & DataCopy$Age<=32),'Young',DataCopy$Age_Group)
DataCopy$Age_Group<-ifelse((DataCopy$Age>32 & DataCopy$Age<=47),'Medium',DataCopy$Age_Group)
DataCopy$Age_Group<-ifelse((DataCopy$Age>47 & DataCopy$Age<=60),'Old',DataCopy$Age_Group)
DataCopy$Age_Group<-as.factor(DataCopy$Age_Group)
DataCopy$Age<-NULL
# Column 'Gender'
DataCopy$Gender_Dummy<-DataCopy$Gender
DataCopy$Gender_Dummy<-ifelse((DataCopy$Gender=='Male'),'1',DataCopy$Gender_Dummy)
DataCopy$Gender_Dummy<-ifelse((DataCopy$Gender=='Female'),'0',DataCopy$Gender_Dummy)
DataCopy$Gender_Dummy<-as.factor(DataCopy$Gender_Dummy)
DataCopy$Gender<-NULL
table(DataCopy$Gender_Dummy,DataCopy$Attrition)##
## No Yes
## 0 412 70
## 1 579 115# Column 'Education'
table(DataCopy$Education,DataCopy$Attrition)##
## No Yes
## 1 117 21
## 2 200 35
## 3 375 77
## 4 267 47
## 5 32 5# The attrition = Yes count for highly educated people is less when compared to others.
# Column 'Business Travel'
table(DataCopy$BusinessTravel)##
## Non-Travel Travel_Frequently Travel_Rarely
## 110 227 839# Column 'Overtime'
DataCopy$OverTime_Dummy<-DataCopy$OverTime
DataCopy$OverTime_Dummy<-ifelse((DataCopy$OverTime=='Yes'),'1',DataCopy$OverTime_Dummy)
DataCopy$OverTime_Dummy<-ifelse((DataCopy$OverTime=='No'),'0',DataCopy$OverTime_Dummy)
DataCopy$OverTime_Dummy<-as.factor(DataCopy$OverTime_Dummy)
DataCopy$OverTime<-NULL
# Column 'Business Travel'
table(DataCopy$Attrition,DataCopy$BusinessTravel)##
## Non-Travel Travel_Frequently Travel_Rarely
## No 99 172 720
## Yes 11 55 119# People who do not travel have the lowest attrition percentage.
# Attrition Rate for people who do not travel= 10%
# Attrition Rate for people who frequently travel= 24.22%
# Attrition Rate for people who rarely travel= 14.18%
# Column 'Job Involvement'
table(DataCopy$JobInvolvement,DataCopy$Attrition)##
## No Yes
## 1 44 23
## 2 244 51
## 3 592 98
## 4 111 13# People with more job involvement have a less attrition rate.Association Rule & supervised Mining
rules_1<-apriori(DataCopy)## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.8 0.1 1 none FALSE TRUE 5 0.1 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 117
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[3338 item(s), 1176 transaction(s)] done [0.01s].
## sorting and recoding items ... [70 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 done [0.01s].
## writing ... [3216 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_1 <- sort (rules_1, by="confidence",decreasing=TRUE)
inspect(head(rules_1,5) )## lhs rhs support confidence lift count
## [1] {JobRole=Manufacturing Director} => {Department=Research & Development} 0.1037415 1 1.539267 122
## [2] {EducationField=Marketing} => {Department=Sales} 0.1079932 1 3.284916 127
## [3] {PercentSalaryHike=14} => {PerformanceRating=3} 0.1275510 1 1.195122 150
## [4] {PercentSalaryHike=12} => {PerformanceRating=3} 0.1292517 1 1.195122 152
## [5] {PercentSalaryHike=13} => {PerformanceRating=3} 0.1420068 1 1.195122 167plot(rules_1,jitter=0)
rules_2 <- apriori(DataCopy, parameter = list(supp = 0.5, conf = 0.5))## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.5 0.1 1 none FALSE TRUE 5 0.5 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 588
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[3338 item(s), 1176 transaction(s)] done [0.01s].
## sorting and recoding items ... [9 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 done [0.00s].
## writing ... [37 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].rules_2 <- sort (rules_2, by="confidence",decreasing=TRUE)
inspect(head(rules_2,5) )## lhs rhs support confidence lift count
## [1] {PerformanceRating=3,
## OverTime_Dummy=0} => {Attrition=No} 0.5306122 0.8914286 1.057841 624
## [2] {OverTime_Dummy=0} => {Attrition=No} 0.6352041 0.8914081 1.057816 747
## [3] {Department=Research & Development} => {Attrition=No} 0.5671769 0.8730366 1.036015 667
## [4] {BusinessTravel=Travel_Rarely,
## PerformanceRating=3} => {Attrition=No} 0.5187075 0.8615819 1.022422 610
## [5] {WorkLifeBalance=3} => {Attrition=No} 0.5374150 0.8610354 1.021774 632plot(rules_2,jitter=0)
rules_3 <- apriori(DataCopy, parameter=list (supp=0.1,conf = 0.5, maxtime = 10), appearance = list (default="lhs",rhs=c("Attrition=Yes","Attrition=No")),control = list(verbose = F))
rules_3 <- sort (rules_3, by="confidence",decreasing=TRUE)
inspect(head(rules_3,5))## lhs rhs support confidence lift count
## [1] {PerformanceRating=3,
## StockOptionLevel=1,
## WorkLifeBalance=3,
## Age_Group=Medium} => {Attrition=No} 0.1105442 0.9774436 1.159913 130
## [2] {StockOptionLevel=1,
## WorkLifeBalance=3,
## Age_Group=Medium} => {Attrition=No} 0.1369048 0.9757576 1.157912 161
## [3] {MaritalStatus=Married,
## StockOptionLevel=1,
## Age_Group=Medium,
## OverTime_Dummy=0} => {Attrition=No} 0.1020408 0.9756098 1.157737 120
## [4] {Department=Research & Development,
## StockOptionLevel=1,
## WorkLifeBalance=3,
## OverTime_Dummy=0} => {Attrition=No} 0.1105442 0.9701493 1.151257 130
## [5] {JobInvolvement=3,
## StockOptionLevel=1,
## WorkLifeBalance=3,
## OverTime_Dummy=0} => {Attrition=No} 0.1011905 0.9674797 1.148089 119plot(rules_3,jitter=0)
DataCopy[,27]## [1] Young Old Medium Old Medium Old Medium Young Medium
## [10] Young Young Medium Young Medium Medium Young Young Young
## [19] Medium Medium Medium Young Medium Medium Young Medium Medium
## [28] Medium Medium Young Young Medium Medium Medium Medium Medium
## [37] Old Medium Young Young Medium Old Young Medium Medium
## [46] Old Medium Medium Medium Medium Medium Young Medium Old
## [55] Medium Medium Medium Young Medium Medium Medium Young Old
## [64] Medium Medium Medium Medium Young Old Old Medium Young
## [73] Young Medium Medium Young Old Young Young Medium Medium
## [82] Medium Medium Medium Medium Medium Young Old Young Young
## [91] Medium Old Medium Old Young Old Medium Medium Old
## [100] Medium Medium Young Medium Young Medium Medium Medium Medium
## [109] Medium Medium Medium Medium Old Old Young Medium Medium
## [118] Young Young Young Young Medium Young Young Medium Medium
## [127] Young Young Medium Young Young Young Old Young Young
## [136] Medium Old Young Young Young Medium Old Medium Medium
## [145] Old Medium Medium Old Medium Young Medium Medium Young
## [154] Medium Young Medium Young Young Old Young Young Medium
## [163] Young Medium Medium Old Young Medium Medium Young Medium
## [172] Medium Medium Medium Medium Young Medium Medium Young Medium
## [181] Young Medium Medium Old Medium Young Medium Old Medium
## [190] Young Medium Young Medium Old Young Medium Old Medium
## [199] Medium Old Young Young Young Medium Old Medium Young
## [208] Medium Medium Medium Young Medium Medium Young Medium Young
## [217] Medium Medium Medium Medium Young Medium Young Medium Medium
## [226] Young Medium Medium Young Medium Medium Young Medium Young
## [235] Young Young Young Young Young Young Medium Medium Young
## [244] Medium Medium Young Young Medium Young Medium Young Young
## [253] Young Young Old Medium Medium Medium Young Young Old
## [262] Medium Young Old Young Young Young Medium Medium Medium
## [271] Medium Young Young Medium Old Medium Medium Medium Old
## [280] Medium Medium Medium Young Old Young Young Young Medium
## [289] Medium Medium Medium Young Medium Young Medium Young Medium
## [298] Medium Medium Old Young Young Medium Medium Medium Young
## [307] Young Medium Young Medium Medium Old Medium Young Medium
## [316] Medium Medium Medium Old Old Medium Medium Young Old
## [325] Old Old Medium Young Medium Old Medium Medium Medium
## [334] Medium Old Medium Medium Medium Medium Young Young Young
## [343] Medium Medium Young Young Young Young Medium Young Old
## [352] Medium Medium Medium Medium Old Young Old Medium Medium
## [361] Old Medium Medium Old Young Medium Young Young Young
## [370] Young Young Old Medium Old Young Medium Medium Medium
## [379] Young Old Young Young Young Medium Young Medium Medium
## [388] Medium Medium Young Young Young Medium Young Young Young
## [397] Young Young Medium Medium Medium Young Young Young Young
## [406] Old Medium Old Young Young Medium Young Medium Medium
## [415] Medium Medium Medium Old Old Medium Medium Young Medium
## [424] Old Medium Medium Medium Medium Medium Medium Young Medium
## [433] Old Young Old Medium Young Medium Medium Medium Medium
## [442] Young Medium Medium Young Medium Young Medium Medium Young
## [451] Young Medium Young Old Medium Medium Medium Old Medium
## [460] Young Old Young Medium Young Medium Young Medium Medium
## [469] Old Medium Old Young Medium Young Young Medium Medium
## [478] Young Young Old Young Medium Medium Medium Medium Young
## [487] Young Medium Medium Old Medium Medium Young Medium Medium
## [496] Medium Young Young Medium Medium Medium Medium Young Medium
## [505] Young Medium Old Medium Young Old Medium Medium Medium
## [514] Young Medium Medium Old Young Old Medium Young Medium
## [523] Medium Medium Medium Young Young Medium Young Medium Young
## [532] Medium Young Old Young Young Medium Young Young Old
## [541] Medium Young Young Young Medium Medium Medium Medium Medium
## [550] Medium Medium Medium Medium Medium Medium Medium Medium Young
## [559] Young Young Old Old Young Young Medium Young Old
## [568] Medium Medium Young Medium Medium Young Young Young Medium
## [577] Medium Medium Medium Young Medium Medium Young Medium Young
## [586] Young Young Young Young Young Medium Medium Medium Medium
## [595] Medium Medium Medium Medium Medium Medium Medium Young Medium
## [604] Young Medium Young Medium Medium Medium Young Medium Young
## [613] Young Medium Old Young Young Young Old Medium Medium
## [622] Old Medium Medium Young Old Medium Old Old Medium
## [631] Young Medium Old Medium Medium Young Medium Old Medium
## [640] Old Young Old Medium Medium Young Young Young Medium
## [649] Medium Medium Medium Medium Young Young Medium Young Medium
## [658] Medium Medium Medium Young Medium Young Medium Young Medium
## [667] Young Medium Medium Medium Medium Medium Medium Medium Old
## [676] Old Old Medium Medium Medium Medium Medium Medium Medium
## [685] Young Medium Medium Young Medium Medium Old Old Old
## [694] Young Medium Old Old Young Old Young Medium Young
## [703] Medium Medium Medium Medium Young Medium Young Old Old
## [712] Medium Old Old Medium Young Medium Young Medium Medium
## [721] Medium Medium Medium Medium Medium Old Medium Medium Medium
## [730] Medium Medium Medium Young Young Medium Old Medium Young
## [739] Medium Medium Medium Medium Medium Medium Medium Young Young
## [748] Young Medium Medium Medium Medium Medium Young Old Young
## [757] Medium Young Medium Young Medium Medium Medium Medium Old
## [766] Medium Old Medium Young Medium Medium Young Young Medium
## [775] Medium Young Young Medium Medium Medium Medium Medium Medium
## [784] Medium Old Medium Young Medium Old Young Young Medium
## [793] Young Young Young Old Young Young Young Medium Old
## [802] Old Medium Medium Young Medium Medium Medium Medium Medium
## [811] Old Young Young Young Old Young Young Medium Medium
## [820] Medium Old Old Young Young Young Medium Old Young
## [829] Medium Medium Young Medium Old Young Medium Young Young
## [838] Young Old Medium Medium Medium Young Medium Medium Young
## [847] Medium Medium Young Medium Medium Medium Medium Medium Young
## [856] Young Medium Medium Old Medium Young Young Medium Old
## [865] Old Medium Young Young Medium Medium Medium Young Medium
## [874] Young Medium Young Old Medium Medium Medium Medium Medium
## [883] Young Young Medium Young Young Medium Young Young Old
## [892] Young Medium Medium Medium Medium Young Young Medium Medium
## [901] Medium Medium Old Old Medium Medium Medium Young Medium
## [910] Medium Old Young Medium Young Old Old Young Medium
## [919] Young Young Medium Young Medium Young Medium Young Old
## [928] Old Medium Medium Medium Medium Old Medium Young Medium
## [937] Young Old Young Young Medium Old Old Medium Old
## [946] Old Medium Medium Young Young Medium Old Young Medium
## [955] Old Medium Old Young Old Medium Medium Medium Medium
## [964] Medium Medium Medium Young Old Medium Medium Young Young
## [973] Medium Medium Medium Medium Medium Young Young Medium Young
## [982] Young Young Medium Medium Young Medium Medium Young Medium
## [991] Old Young Young Medium Young Old Young Medium Medium
## [1000] Young Medium Old Medium Young Young Medium Young Medium
## [1009] Medium Medium Old Medium Old Medium Young Medium Young
## [1018] Old Medium Medium Medium Young Medium Young Young Medium
## [1027] Medium Medium Young Old Medium Young Medium Young Old
## [1036] Medium Medium Medium Medium Medium Medium Young Medium Young
## [1045] Medium Medium Old Young Medium Old Young Young Young
## [1054] Young Medium Medium Young Medium Young Medium Young Old
## [1063] Young Young Young Old Medium Old Medium Young Medium
## [1072] Young Medium Young Old Young Young Medium Medium Medium
## [1081] Medium Young Young Young Medium Medium Young Medium Young
## [1090] Medium Young Young Young Young Medium Young Young Medium
## [1099] Young Young Young Medium Medium Medium Young Medium Young
## [1108] Medium Young Young Young Medium Medium Young Medium Medium
## [1117] Young Medium Medium Young Medium Medium Young Medium Young
## [1126] Medium Young Medium Old Old Medium Old Young Young
## [1135] Young Medium Old Medium Young Medium Medium Medium Medium
## [1144] Medium Young Medium Old Medium Old Young Medium Medium
## [1153] Medium Medium Old Medium Young Old Young Young Medium
## [1162] Old Medium Medium Medium Old Medium Old Medium Medium
## [1171] Medium Young Medium Old Medium Old
## Levels: Medium Old Youngshiny link
https://henglong.shinyapps.io/henglongNewhw1/
HTML Link file:///Users/henglong/Documents/IST%20707/homework/hw1/henglong-so.html