NN

# creating training data set

TKS=c(20,10,30,20,80,30)

CSS=c(90,20,40,50,50,80)

Placed=c(1,0,0,0,1,1)

# Here, you will combine multiple columns or features into a single set of data

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

# load library

require(neuralnet)

# fit neural network

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

                linear.output = FALSE)

# plot neural network

plot(nn)

# creating test set

TKS=c(30,40,85)

CSS=c(85,50,40)

test=data.frame(TKS,CSS)

## Prediction using neural network

Predict=compute(nn,test)

Predict$net.result

# Converting probabilities into binary classes setting threshold level 0.5

prob <- Predict$net.result

pred <- ifelse(prob>0.5, 1, 0)

pred