classlib_kNN.R

install.packages("class")
library(class)

#Normalization
normalize <- function(x) {
  return ((x - min(x)) / (max(x) - min(x))) }
norm <- as.data.frame(lapply(data[,1:4], normalize))

set.seed(123)
data_spl <- sample(1:nrow(norm),size=nrow(norm)*0.7,replace = FALSE) 

train2 <- data[data_spl,] # 70% training data
test2 <- data[-data_spl,] # remaining 30% test data

train_labels <- data[data_spl,5]
test_labels <-data[-data_spl,5]
knn_pred <- knn(train=train2, test=test2, cl=train_labels, k=1)