neurel network

library(ggplot2)
library(neuralnet)
concrete <- read.csv("concrete.csv")
set.seed((1234))

normalize <- function(x){
  return((x-min(x))/(max(x)-min(x)))
}

concrete_norm <- as.data.frame(lapply(concrete,normalize))

concrete_train <- concrete_norm[1:773,]
concrete_test <- concrete_norm[774:1030,]

library(neuralnet)
concrete_model <- neuralnet(strength~cement+slag+water+ash+superplastic+coarseagg+fineagg+age,data=concrete_train,hidden=2)

plot(concrete_model)

model_results <- compute(concrete_model, concrete_test[1:8])

predicted_stregth <- model_results$net.result

cor(predicted_stregth, concrete_test$strength)

##           [,1]
## [1,] 0.7713785

model_results <- compute(concrete_model, concrete_test[1:8])

predicted_stregth <- model_results$net.result

cor(predicted_stregth, concrete_test$strength)

##           [,1]
## [1,] 0.7713785