data <- read.csv("F:/winequality-white.csv")
#shuffle
data <- data[sample(1:nrow(data)),]
set.seed(123)
data$quality = as.factor(data$quality)
div = 0.7
index <- 1:nrow(data)
position <- sample(index, trunc(nrow(data) * div))
test <- data[-position,]
train <- data[position,]
result = data[nrow(test),]
result$pred = -1
model_nnet <- nnet(as.factor(quality) ~ ., data=train, size=10, maxit=1000)
pred<- predict(model_nnet, test, type="class")

predicted
true   3   4   5   6   7   9
   3   0   1   3   1   0   0
   4   0   5  27  17   1   0
   5   1   6 259 155   3   0
   6   0   3 152 431  79   2
   7   0   0   5 164  98   1
   8   0   0   1  26  25   0
   9   0   0   0   2   2   0

data <- read.csv("F:/winequality-white.csv")
set.seed(123)
#shuffle
data <- data[sample(1:nrow(data)),]
data$quality = as.factor(data$quality)
div = 0.7
index <- 1:nrow(data)
position <- sample(index, trunc(nrow(data) * div))
test <- data[-position,]
train <- data[position,]
result = data[nrow(test),]
result$pred = -1
model_nnet <- nnet(as.factor(quality) ~ ., data=train, size=10, maxit=1000)
pred<- predict(model_nnet, test, type="class")
table(true=test$quality, predicted=pred)

predicted
true   4   5   6   7
   3   1   5   1   0
   4   7  27  15   1
   5   3 234 179   4
   6   2 135 480  57
   7   1  10 169  92
   8   0   0  33  13
   9   0   0   0   1