员工离职预测
2017-10-16 14:35
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员工离职预测
library(dplyr)library(psych)library(ggplot2)library(randomForest)str(train)'data.frame': 1100 obs. of 31 variables: $ X...Age : int 37 54 34 39 28 24 29 36 33 34 ... $ Attrition : int 0 0 1 0 1 0 0 0 0 0 ... $ BusinessTravel : Factor w/ 3 levels "Non-Travel","Travel_Frequently",..: 3 2 2 3 2 3 3 3 3 3 ... $ Department : Factor w/ 3 levels "Human Resources",..: 2 2 2 2 2 3 2 3 2 2 ... $ DistanceFromHome : int 1 1 7 1 1 4 9 2 4 2 ... $ Education : int 4 4 3 1 3 1 5 2 4 4 ... $ EducationField : Factor w/ 6 levels "Human Resources",..: 2 2 2 2 4 4 5 4 4 6 ... $ EmployeeNumber : int 77 1245 147 1026 1111 1445 455 513 305 1383 ... $ EnvironmentSatisfaction : int 1 4 1 4 1 4 2 2 3 3 ... $ Gender : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 1 2 2 1 1 ... $ JobInvolvement : int 2 3 1 2 2 3 2 2 2 3 ... $ JobLevel : int 2 3 2 4 1 2 1 3 1 2 ... $ JobRole : Factor w/ 9 levels "Healthcare Representative",..: 5 5 3 5 3 8 3 8 7 1 ... $ JobSatisfaction : int 3 3 3 4 2 3 4 3 2 4 ... $ MaritalStatus : Factor w/ 3 levels "Divorced","Married",..: 1 1 3 2 1 2 3 2 2 3 ... $ MonthlyIncome : int 5993 10502 6074 12742 2596 4162 3983 7596 2622 6687 ... $ NumCompaniesWorked : int 1 7 1 1 1 1 0 1 6 1 ... $ Over18 : Factor w/ 1 level "Y": 1 1 1 1 1 1 1 1 1 1 ... $ OverTime : Factor w/ 2 levels "No","Yes": 1 1 2 1 1 2 1 1 1 1 ... $ PercentSalaryHike : int 18 17 24 16 15 12 17 13 21 11 ... $ PerformanceRating : int 3 3 4 3 3 3 3 3 4 3 ... $ RelationshipSatisfaction: int 3 1 4 3 1 3 3 2 4 4 ... $ StandardHours : int 80 80 80 80 80 80 80 80 80 80 ... $ StockOptionLevel : int 1 1 0 1 2 2 0 2 0 0 ... $ TotalWorkingYears : int 7 33 9 21 1 5 4 10 7 14 ... $ TrainingTimesLastYear : int 2 2 3 3 2 3 2 2 3 2 ... $ WorkLifeBalance : int 4 1 3 3 3 3 3 3 3 4 ... $ YearsAtCompany : int 7 5 9 21 1 5 3 10 3 14 ... $ YearsInCurrentRole : int 5 4 7 6 0 4 2 9 2 11 ... $ YearsSinceLastPromotion : int 0 1 0 11 0 0 2 9 1 4 ... $ YearsWithCurrManager : int 7 4 6 8 0 3 2 0 1 11 ...
describe(train)
vars n mean sd median trimmed mad min max range skew kurtosis se X...Age 1 1100 37.00 9.04 36.0 36.51 8.90 18 60 42 0.44 -0.43 0.27 Attrition 2 1100 0.16 0.37 0.0 0.08 0.00 0 1 1 1.83 1.36 0.01 BusinessTravel* 3 1100 2.62 0.66 3.0 2.77 0.00 1 3 2 -1.47 0.81 0.02 Department* 4 1100 2.26 0.52 2.0 2.25 0.00 1 3 2 0.23 -0.41 0.02 DistanceFromHome 5 1100 9.43 8.20 7.0 8.36 7.41 1 29 28 0.91 -0.35 0.25 Education 6 1100 2.92 1.02 3.0 2.99 1.48 1 5 4 -0.30 -0.55 0.03 EducationField* 7 1100 3.22 1.32 3.0 3.06 1.48 1 6 5 0.58 -0.65 0.04 EmployeeNumber 8 1100 1028.16 598.92 1026.5 1027.04 782.81 1 2065 2064 0.02 -1.22 18.06 EnvironmentSatisfaction 9 1100 2.73 1.10 3.0 2.78 1.48 1 4 3 -0.33 -1.21 0.03 Gender* 10 1100 1.59 0.49 2.0 1.62 0.00 1 2 1 -0.38 -1.86 0.01 JobInvolvement 11 1100 2.73 0.71 3.0 2.74 0.00 1 4 3 -0.54 0.34 0.02 JobLevel 12 1100 2.05 1.11 2.0 1.89 1.48 1 5 4 1.04 0.40 0.03 JobRole* 13 1100 5.43 2.46 6.0 5.59 2.97 1 9 8 -0.34 -1.22 0.07 JobSatisfaction 14 1100 2.73 1.11 3.0 2.79 1.48 1 4 3 -0.33 -1.24 0.03 MaritalStatus* 15 1100 2.11 0.73 2.0 2.14 1.48 1 3 2 -0.18 -1.12 0.02 MonthlyIncome 16 1100 6483.62 4715.29 4857.0 5639.41 3166.09 1009 19999 18990 1.38 1.04 142.17 NumCompaniesWorked 17 1100 2.68 2.51 2.0 2.35 1.48 0 9 9 1.03 -0.02 0.08 Over18* 18 1100 1.00 0.00 1.0 1.00 0.00 1 1 0 NaN NaN 0.00 OverTime* 19 1100 1.28 0.45 1.0 1.22 0.00 1 2 1 0.99 -1.02 0.01 PercentSalaryHike 20 1100 15.24 3.63 14.0 14.85 2.97 11 25 14 0.79 -0.35 0.11 PerformanceRating 21 1100 3.15 0.36 3.0 3.07 0.00 3 4 1 1.93 1.72 0.01 RelationshipSatisfaction 22 1100 2.70 1.10 3.0 2.75 1.48 1 4 3 -0.29 -1.23 0.03 StandardHours 23 1100 80.00 0.00 80.0 80.00 0.00 80 80 0 NaN NaN 0.00 StockOptionLevel 24 1100 0.79 0.84 1.0 0.67 1.48 0 3 3 0.95 0.34 0.03 TotalWorkingYears 25 1100 11.22 7.83 10.0 10.27 5.93 0 40 40 1.15 0.99 0.24 TrainingTimesLastYear 26 1100 2.81 1.29 3.0 2.74 1.48 0 6 6 0.50 0.49 0.04 WorkLifeBalance 27 1100 2.75 0.70 3.0 2.76 0.00 1 4 3 -0.60 0.47 0.02 YearsAtCompany 28 1100 7.01 6.22 5.0 5.94 4.45 0 37 37 1.81 4.01 0.19 YearsInCurrentRole 29 1100 4.21 3.62 3.0 3.83 4.45 0 18 18 0.95 0.61 0.11 YearsSinceLastPromotion 30 1100 2.23 3.31 1.0 1.49 1.48 0 15 15 1.94 3.30 0.10 YearsWithCurrManager 31 1100 4.12 3.60 3.0 3.76 4.45 0 17 17 0.86 0.26 0.11#删除 常量
name<-names(train) train<-train[name!="Over18" & name!="StandardHours" & name!="EmployeeNumber"]#重编码
train$Gender<-as.integer(train$Gender)-1 train$OverTime<-as.integer(train$OverTime)-1#Age 和 Attrition
ggplot(train, aes(X...Age, fill = factor(Attrition))) +
geom_histogram(bins=30) +
facet_grid(.~Gender)+
labs(fill="Attrition")+ xlab("Age")+ylab("Total Count")
#小结：train$X...Age[train$X...Age>=18 & train$X...Age <25]<-1 train$X...Age[train$X...Age>=25 & train$X...Age <35]<-2 train$X...Age[train$X...Age>=35 & train$X...Age <45]<-3 train$X...Age[train$X...Age>=45 & train$X...Age <55]<-4 train$X...Age[train$X...Age>=55 ]<-5#Department 和 JobLevel
ggplot(train, aes(x = JobLevel, fill = as.factor(Attrition))) +
geom_bar() +
facet_wrap(~ Department)+
xlab("Job Level")+
ylab("Total Count")+
labs(fill = "Attrition")
train$Department<-as.character(train$Department) train$Department[train$Department=="Human Resources"]<-"1" train$Department[train$Department=="Sales"]<-"2" train$Department[train$Department=="Research & Development"]<-"3" train$Department<-as.integer(train$Department)#小结：不同部门相同级别之间存在明显差异，研发部门1，2级别和销售部1，2，3级别流动性较大。#Department 和 BusinessTravel
ggplot(train, aes(x = BusinessTravel, fill = as.factor(Attrition))) +
geom_bar() +
facet_wrap(~ Department)+
xlab("BusinessTravel")+
ylab("Total Count")+
labs(fill = "Attrition")
train$BusinessTravel<-as.character(train$BusinessTravel) train$BusinessTravel[train$BusinessTravel=="Non-Travel"]<-"1" train$BusinessTravel[train$BusinessTravel=="Travel_Frequently"]<-"2" train$BusinessTravel[train$BusinessTravel=="Travel_Rarely"]<-"3" train$BusinessTravel<-as.integer(train$BusinessTravel)#小结：是否经常出差，并不是影响离职的关键因素，但偶然出差的员工离职率最高。研发部、销售部、人力资源部依次下降。#EducationField 和 Attrition
ggplot(train,aes(EducationField,fill=as.factor(Attrition)))+
geom_bar(stat="count",position="dodge")+
xlab("EducationField")+
ylab("Total Count")+
labs(fill="Attrition")
#小结：专业领域和离职之间无明显关系#MaritalStatus 和 Attritionggplot(train,aes(MaritalStatus,fill=as.factor(Attrition)))+
geom_bar(stat="count",position="dodge")+
xlab("MaritalStatus")+
ylab("Total Count")+
labs(fill="Attrition")
train$MaritalStatus<-as.character(train$MaritalStatus) train$MaritalStatus[train$MaritalStatus=="Divorced"]<-1 train$MaritalStatus[train$MaritalStatus=="Married"]<-2 train$MaritalStatus[train$MaritalStatus=="Single"]<-3 train$MaritalStatus<-as.integer(train$MaritalStatus)#小结：婚姻情况和离职有一点关系#EnvironmentSatisfaction 和 Attrition
ggplot(train, aes(x = EnvironmentSatisfaction, fill = as.factor(Attrition))) +
geom_bar() +
facet_wrap(~ JobLevel)+
xlab("JobLevel")+
ylab("Total Count")+
labs(fill = "Attrition")
#小结：满意度和离职之间无明显关系#MonthlyIncome 和 Attritionggplot(train,aes(MonthlyIncome, fill = factor(Attrition))) + geom_density(alpha = 0.8)+ labs(fill="Attrition")
#小结：低收入者明显在职意向不稳定train$MonthlyIncome[train$MonthlyIncome<=3000]<-1 train$MonthlyIncome[train$MonthlyIncome>3000 & train$MonthlyIncome<=6000]<-2 train$MonthlyIncome[train$MonthlyIncome>6000 & train$MonthlyIncome<=9000]<-3 train$MonthlyIncome[train$MonthlyIncome>9000 & train$MonthlyIncome<=12000]<-4 train$MonthlyIncome[train$MonthlyIncome>12000 & train$MonthlyIncome<=17000]<-5 train$MonthlyIncome[train$MonthlyIncome>17000]<-6#关联关系
corrgram(train[,-c(7,12)],lower.panel=panel.pie,upper.panel=NULL)
#抽样set.seed(1)ind<-sample(2,nrow(train),replace=TRUE,prob=c(0.7,0.3))train.df<-train[ind==1,]test.df<-train[ind==2,]
#随机森林
rf<-randomForest(factor(Attrition)~.,data=train.df)varImpPlot(rf)
#准确率prediction <- predict(rf,newdata=test.df,type="response")misClasificError <- mean(prediction != test.df$Attrition)print(paste('Accuracy',1-misClasificError))[1] "Accuracy 0.876506024096386"#逻辑回归gf<-glm(Attrition~.,data=train.df,family = binomial(link=logit))summary(gf)Call:glm(formula = Attrition ~ ., family = binomial(link = logit),data = train.df)Deviance Residuals:Min 1Q Median 3Q Max-1.6113 -0.5048 -0.2459 -0.0860 3.4737Coefficients:Estimate Std. Error z value Pr(>|z|)(Intercept) -1.31753 3.58064 -0.368 0.712903X...Age -0.36136 0.17640 -2.049 0.040508 *BusinessTravel 0.05908 0.20110 0.294 0.768928Department 0.39070 0.93431 0.418 0.675820DistanceFromHome 0.05274 0.01486 3.550 0.000386 ***Education -0.17039 0.12279 -1.388 0.165239EducationFieldLife Sciences -0.43924 1.20639 -0.364 0.715785EducationFieldMarketing 0.14995 1.25574 0.119 0.904948EducationFieldMedical -0.55928 1.20602 -0.464 0.642835EducationFieldOther 0.07420 1.32247 0.056 0.955256EducationFieldTechnical Degree 0.62904 1.22665 0.513 0.608084EnvironmentSatisfaction -0.50299 0.11646 -4.319 1.57e-05 ***Gender 0.49495 0.26618 1.859 0.062965 .JobInvolvement -0.67266 0.17777 -3.784 0.000154 ***JobLevel -0.18383 0.39279 -0.468 0.639777JobRoleHuman Resources 2.92472 2.10883 1.387 0.165474JobRoleLaboratory Technician 2.11121 0.82806 2.550 0.010785 *JobRoleManager 2.09557 1.12796 1.858 0.063193 .JobRoleManufacturing Director 1.22695 0.84649 1.449 0.147211JobRoleResearch Director 1.49258 1.17894 1.266 0.205501JobRoleResearch Scientist 1.44543 0.82801 1.746 0.080868 .JobRoleSales Executive 2.17131 1.20040 1.809 0.070479 .JobRoleSales Representative 3.29933 1.28712 2.563 0.010367 *JobSatisfaction -0.63089 0.11767 -5.361 8.26e-08 ***MaritalStatus 0.94530 0.25146 3.759 0.000170 ***MonthlyIncome -0.03459 0.24924 -0.139 0.889628NumCompaniesWorked 0.13934 0.05418 2.572 0.010119 *OverTime 2.18546 0.27520 7.941 2.00e-15 ***PercentSalaryHike -0.05939 0.05572 -1.066 0.286492PerformanceRating 0.86885 0.55923 1.554 0.120266RelationshipSatisfaction -0.33278 0.11625 -2.863 0.004201 **StockOptionLevel 0.01585 0.21361 0.074 0.940835TotalWorkingYears -0.04047 0.04220 -0.959 0.337593TrainingTimesLastYear -0.15291 0.10058 -1.520 0.128425WorkLifeBalance -0.21648 0.16944 -1.278 0.201398YearsAtCompany 0.07885 0.05340 1.477 0.139745YearsInCurrentRole -0.13861 0.06449 -2.149 0.031612 *YearsSinceLastPromotion 0.14022 0.05867 2.390 0.016857 *YearsWithCurrManager -0.11790 0.06483 -1.819 0.068956 .---Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(Dispersion parameter for binomial family taken to be 1)Null deviance: 717.22 on 767 degrees of freedomResidual deviance: 457.00 on 729 degrees of freedomAIC: 535Number of Fisher Scoring iterations: 6#准确率
prediction <- predict(gf,newdata=test.df,type="response")prediction <- ifelse(prediction > 0.5,1,0)misClasificError <- mean(prediction != test.df$Attrition)print(paste('Accuracy',1-misClasificError))[1] "Accuracy 0.858433734939759"
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