Which part of the code is wrong for artificial neural network in R? [on hold]

I'm trying to predict a sales demand by using artificial neural network and 10 fold cross validation.

So, I coded by using nnet package as follows:

library(nnet)
library(plyr)
k = 10
question$id <- sample(1:k, nrow(question), replace = TRUE)
list <- 1:k
prediction <- data.frame()
testsetCopy <- data.frame()
progress.bar <- create_progress_bar("text")
progress.bar$init(k)
for(i in 1:k){
trainingset <- subset(question, id %in% list[-i])
testset <- subset(question, id %in% c(i))
mymodel <- nnet(Sales1~ResidentA+ResidentB+ResidentD+DOW+Weather+Amt_Rainfall+Air_Quality+Avg_Temp+Humidity,
data=trainingset, size=7, decay=0.1)
temp <- as.data.frame(predict(mymodel, testset[,-5]))
prediction <- rbind(prediction, temp)
testsetCopy <- rbind(testsetCopy, as.data.frame(testset[,3]))
progress.bar$step()
}


However, the result showed less than 1 that it seems something wrong.

So May I ask a question how to make it the correct way? (I changed size and decay before but it didn't work)

Thank you.

*here is a sample file link: 1drv.ms/f/s!At97BnO9168kmz68slWFwqPGXDrp

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