library(neuralnet)

data1<-read.csv("C:/Users/LENOVO/Desktop/mbaper.csv")

data1$Specialization<-as.factor(data1$Specialization)

nn <- neuralnet(Specialization~Percentage_in_10_Class+

                Percentage_in_12_Class+

                  Percentage_in_Under_Graduate+

                  percentage_MBA,data=data1,

                hidden=c(3,3,3), 

                linear.output=FALSE, 

                threshold=0.01)

#hidden---c(m,n, o,...),  m, n, o= number of neurons in 1,2,3...layer

nn$result.matrix

plot(nn)

#-----------------

library(caret)

library(nnet)

data1$Specialization<-as.factor(data1$Specialization)

nn1<-train(Specialization~Percentage_in_10_Class+

             Percentage_in_12_Class+

             Percentage_in_Under_Graduate+

             percentage_MBA,data=data1, method= "nnet", 

           Size=4, maxit =50)

#size is the number of neurons in hidden layer

nn1

predict1<-predict(nn1,data1)

confusionMatrix(data1$Specialization,predict1)

#----------

nn1<-train(  percentage_MBA~Percentage_in_10_Class+

             Percentage_in_12_Class+

             Percentage_in_Under_Graduate,

           data=data1, method= "nnet")

nn1

predict1<-predict(nn1,data1)

RMSE(predict1,data1$percentage_MBA)