Basic Neural Network

basicNN.R

library(neuralnet)

# creating training data set
TKS=   c(20,10,30,20,80,30,90,99,99,40,12,32)
CSS=   c(90,20,40,50,50,80,90,99,99,10,20,30)
Placed=c(1 ,0 ,0 ,0 ,1 ,1 ,1 ,1 ,1 ,0 ,0 ,0)

df=data.frame(TKS,CSS,Placed)

nn=neuralnet(Placed~TKS+CSS,data=df, hidden=3, act.fct = "logistic", linear.output = FALSE)

plot(nn)

TKS=c(90,40,85,32)
CSS=c(85,20,90,12)
test=data.frame(TKS,CSS)

Predict=compute(nn,test)
Predict$net.result

prob <- Predict$net.result
pred <- ifelse(prob>0.5, 1, 0)
pred