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Rprogram
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Rprogram
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library(class)
library(kknn)
library(knncat)
library(rpart.plot)
library(C50)
library(rpart)
minmax<-function(a,min,max) #Normalisation Function
{
w<-((a-min)/(max-min))
return(w)
}
options(max.print = 99999999)
HR1<-read.csv("F:/kdd/HR1.csv") #Loading Preprocessed CSV File
#Normalisation
HR1[,1]<-as.factor(minmax(HR1[,1],min(HR1[,1]),max(HR1[,1])))
HR1[,2]<-as.factor(minmax(HR1[,2],min(HR1[,2]),max(HR1[,2])))
HR1[,3]<-as.factor(minmax(HR1[,3],min(HR1[,3]),max(HR1[,3])))
HR1[,4]<-as.factor(minmax(HR1[,4],min(HR1[,4]),max(HR1[,4])))
HR1[,5]<-as.factor(minmax(HR1[,5],min(HR1[,5]),max(HR1[,5])))
HR1[,6]<-as.factor(minmax(HR1[,6],min(HR1[,6]),max(HR1[,6])))
HR1[,8]<-as.factor(minmax(HR1[,8],min(HR1[,8]),max(HR1[,8])))
HR1[,9]<-as.factor(minmax(HR1[,9],min(HR1[,9]),max(HR1[,9])))
HR1[,10]<-as.factor(minmax(HR1[,10],min(HR1[,10]),max(HR1[,10])))
HR1[,11]<-as.factor(minmax(HR1[,11],min(HR1[,11]),max(HR1[,11])))
HR1[,12]<-as.factor(minmax(HR1[,12],min(HR1[,12]),max(HR1[,12])))
HR1[,13]<-as.factor(minmax(HR1[,13],min(HR1[,13]),max(HR1[,13])))
HR1[,14]<-as.factor(minmax(HR1[,14],min(HR1[,14]),max(HR1[,14])))
HR1[,15]<-as.factor(minmax(HR1[,15],min(HR1[,15]),max(HR1[,15])))
HR1[,16]<-as.factor(minmax(HR1[,16],min(HR1[,16]),max(HR1[,16])))
HR1[,17]<-as.factor(minmax(HR1[,17],min(HR1[,17]),max(HR1[,17])))
HR1[,18]<-as.factor(minmax(HR1[,18],min(HR1[,18]),max(HR1[,18])))
HR1[,19]<-as.factor(minmax(HR1[,19],min(HR1[,19]),max(HR1[,19])))
HR1[,20]<-as.factor(minmax(HR1[,20],min(HR1[,20]),max(HR1[,20])))
HR1[,21]<-as.factor(minmax(HR1[,21],min(HR1[,21]),max(HR1[,21])))
HR1[,7]<-as.factor(HR1[,7])
ran<-sample(1:14999, 4500, replace=FALSE) #Randomly selecting Test and training Data
test<-HR1[ran,]
train<-HR1[-ran,]
#1)implementing KNN
pred1<-knn(train[,-7],test[,-7],train[,7],k=2)#for k=2
table(Predict=pred1, Actual=test[,7])
pred1<-knn(train[,-7],test[,-7],train[,7],k=3)#for k=3
table(Predict=pred1, Actual=test[,7])
pred1<-knn(train[,-7],test[,-7],train[,7],k=10)#for k=10
table(Predict=pred1, Actual=test[,7])
pred1<-knn(train[,-7],test[,-7],train[,7],k=55)#for k=55
table(Predict=pred1, Actual=test[,7])
#2)Implementing KKNN
predict_k1<-kknn(formula=left~.,train,test,kernel="triangular",k=2) #For k=2
fit <- fitted(predict_k1)
view1<-table(Predict=fit,Actual=test[,7])
View(view1)
predict_k1<-kknn(formula=left~.,train,test,kernel="triangular",k=3) #For k=3
fit <- fitted(predict_k1)
view2<-table(Predict=fit,Actual=test[,7])
View(view2)
predict_k1<-kknn(formula=left~.,train,test,kernel="triangular",k=55)#For k=55
fit <- fitted(predict_k1)
view1<-table(Predict=fit,Actual=test[,7])
View(view1)
predict_k1<-kknn(formula=left~.,train,test,kernel="triangular",k=75) #For k=75
fit <- fitted(predict_k1)
view2<-table(Predict=fit,Actual=test[,7])
View(view2)
#3)Implementing C5.0
training_left<-C5.0(train[,-7],train[,7])
summary(training_left)
predict<-predict(training_left,test)
table(Predicted=predict, Actual=test[,7])
plot(training_left)
#4)Implementing CART
index_left<-sample(nrow(HR1),as.integer(0.70*nrow(HR1)))
training<-rpart(left~., data=HR1[index_left,],method = "class")
testing<-HR1[-index_left,]
predictcart<-predict(training,testing,type="class")
left<-HR1[-index_left,7]
table(Predicted=predictcart,Actual=left)