library(neuralnet)
library(nnet)
library(NeuralNetTools)
library(plyr)

Startups <- read.csv(file.choose())
View(Startups)
class(Startups)

Startups$State <- as.numeric(revalue(Startups$State, c("New York"="0", "California"="1","Florida"="2")))
str(Startups)
Startups <- as.data.frame(Startups)

attach(Startups)

plot(R.D.Spend, Profit)
plot(Administration, Profit)
plot(Marketing.Spend, Profit)
plot(State, Profit)
#windows()

pairs(Startups)
cor(Startups)
summary(Startups)

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

Startups_norm<-as.data.frame(lapply(Startups,FUN=normalize))

summary(Startups_norm$Profit) # Normalized form of profit
summary(Startups$profit)
set.seed(123)

ind <- sample(2, nrow(Startups_norm), replace = TRUE, prob = c(0.7,0.3))
Startups_train <- Startups_norm[ind==1,]
startups_test  <- Startups_norm[ind==2,]


# training model


startups_model <- neuralnet(Profit~R.D.Spend+Administration
                            +Marketing.Spend+State,data = Startups_train)
str(startups_model)

plot(startups_model, rep = "best")

summary(startups_model)
par(mar = numeric(4), family = 'serif')
plotnet(startups_model, alpha = 0.6)
set.seed(12323)
model_results <- compute(startups_model,startups_test[1:4])
predicted_profit <- model_results$net.result

# test model

cor(predicted_profit,startups_test$Profit)
str_max <- max(Startups$Profit)
str_min <- min(Startups$Profit)

unnormalize <- function(x, min, max) { 
  return( (max - min)*x + min )
}


ActualProfit_pred <- unnormalize(predicted_profit,str_min,str_max)
head(ActualProfit_pred)
set.seed(12345)
Startups_model2 <- neuralnet(Profit~R.D.Spend+Administration
                             +Marketing.Spend+State,data = Startups_train,
                             hidden = 2)

plot(Startups_model2 ,rep = "best")
summary(Startups_model2)

model_results2<-compute(Startups_model2,startups_test[1:4])
predicted_Profit2<-model_results2$net.result

cor(predicted_Profit2,startups_test$Profit)
plot(predicted_Profit2,startups_test$Profit)
par(mar = numeric(4), family = 'serif')

plotnet(Startups_model2, alpha = 0.6)