ggplot2员工离职预测

ggplot2-员工离职预测ggplot2-员工离职预测data<-read.csv("E:/pfm_train.csv",sep=",",header=TRUE)#从指定位置读取训练样本fit.full<-glm(Attrition~.,data=data[,-c(8,18,23)],family=binomial())#初步回归，AIC:730.18summary(fit.full)#模型拟合函数结果的结果摘要step(fit.full)#通过AIC选择基于公式的模型。fit.reduce<-glm(formula=Attrition~Age+BusinessTravel+Department+#逐步回归优化，AIC:721.3员工离职预测ggplot2-员工离职预测DistanceFromHome+EducationField+EnvironmentSatisfaction+Gender+JobInvolvement+JobLevel+JobSatisfaction+MaritalStatus+NumCompaniesWorked+OverTime+RelationshipSatisfaction+TotalWorkingYears+TrainingTimesLastYear+WorkLifeBalance+YearsAtCompany+YearsInCurrentRole+YearsSinceLastPromotion+YearsWithCurrManager,family=binomial(),data=data[,-c(8,18,23)])员工离职预测ggplot2-员工离职预测#拟合函数#formula要你喝的模型的符号描述，这里全部都要拟合，family模型使用的函数binomial-logitsummary(fit.reduce)#模型拟合函数结果的结果摘要test<-predict(fit.full,newdata=data,type="response")test1<-predict(fit.reduce,newdata=data,type="response")test[test<0.5]<-0test[test>=0.5]<-1#0.5以下按0处理，0.5以上按1处理员工离职预测ggplot2-员工离职预测result<-cbind(test,data$Attrition)#按Attrtion组合table(test,data$Attrition)data[,2]<-as.factor(as.vector(data)[,2])#首先将数值型因子进行了标准化，确保所有的因子在一个量纲上，接着对已经标准化的数据进行主成分分析，消除因子中的高相关性install.packages("caret")install.packages("ipred")library(caret)library(ipred)员工离职预测ggplot2-员工离职预测p_2009<-preProcess(data[,-c(2,8,18,23)],method=c("scale","center","pca"))#主成分分析重组各个特征值src1_2009_p<-cbind(Attrition=data[,2],predict(p_2009,data[,-c(2,8,18,23)]))fit.full<-glm(Attrition~.,data=src1_2009_p,family=binomial())#AIC:728.81summary(fit.full)step(fit.full)fit.reduce<-glm(formula=Attrition~BusinessTravel+EducationField+Gender+#716.06JobRole+MaritalStatus+OverTime+PC1+PC4+PC7+PC8+PC9+PC13+PC14+PC15,family=binomial(),data=src1_2009_p)summary(fit.reduce)员工离职预测ggplot2-员工离职预测test<-predict(fit.full,newdata=src1_2009_p,type="response")test1<-predict(fit.reduce,newdata=src1_2009_p,type="response")test[test<0.5]<-0test[test>=0.5]<-1test1[test1<0.5]<-0test1[test1>=0.5]<-1result<-cbind(test,data$Attrition)table(test,data$Attrition)data1<-read.csv("E:/pfm_test.csv",sep=",",header=TRUE)员工离职预测ggplot2-员工离职预测pre_data1<-predict(p_2009,data1[,-c(7,17,22)])result<-predict(fit.reduce,pre_data1,interval="prediction",level=0.95)result1<-predict(fit.full,pre_data1,interval="prediction",level=0.95)#预测result1<-resultresult1[result1>=0.5]<-1result1[result1<0.5]<-0table(result1)员工离职预测ggplot2-员工离职预测z=0o=0for(iinresult1){if(i==0){z=z+1}else{o=o+1}}df=data.frame(result1)bar=ggplot(df,aes(factor(result1),fill=factor(resul