KNN

data <- iris
row_lables = data[,5]
data[,1:4] <- scale(data[,1:4])
set.seed(123)
size <- floor(0.8 * nrow(data))
train_ind <- sample(seq_len(nrow(data)),size = size)
train_lables <- data[train_ind,5]
test_lables <- row_lables[-train_ind]
data_train <- data[train_ind,1:4]
data_test <- data[-train_ind,1:4]

 View(data_test)
 View(data_train)

library(class)
prediction <- knn(train = data_train,
                  test = data_test,
                  cl = train_lables,
                  k = round(sqrt(nrow(data_train))))

plot_prediction <- data.frame(
  data_test$Sepal.Length,
  data_test$Sepal.Width,
  data_test$Petal.Length,
  data_test$Petal.Width,
  prediction = prediction)
colnames(plot_prediction) <- c('sepal.lenght',
                               'sepal.width',
                               'petal.length',
                               'petal.width')