View difference between Paste ID: WZyx4Lhc and NeEv64aL

 #loading data set
 data = iris
 #details of data set
 summary(data)
 #display top 5 rows
 head(data)
 
 #writing a function to normalise a column
 normalise = function(x){
   return ((x - min(x))/(max(x)-min(x)))
 }
 
 #yanking columns from the data set and normalizing them
 data$Sepal.Length = normalise(data$Sepal.Length)
 data$Sepal.Width = normalise(data$Sepal.Width)
 data$Petal.Length = normalise(data$Petal.Length)
 data$Petal.Width = normalise(data$Petal.Width)
 
 #randomizing data to make a data set for training
 ind = sample(1:nrow(data), size = 0.9*nrow(data), replace = FALSE )
 training_data = data[ind,]
 
 #creating testing data (500 IQ move)
 test_data = data[-ind,]
 
 #creating label for later verification (5th col of table)
 test_data_label = test_data[,5]
 
 #removing 5th column from test_data
 test_data = test_data[-5]
 
 #creating training data label
 training_data_label = training_data[,5]
 training_data = training_data[,-5]
 
 #load package
 library(class)
 library(caret)
 library(ggplot2)
 
 #executing(implementing?) KNN algorithm
 model = knn(training_data, test_data, training_data_label, k=11)
 
 #comparing the model and the test data labels
 model
 test_data_label
 
 #evaluating the performance of the model
 confusionMatrix(model, test_data_label)
 
 #there is another
 
 #resetting training data
 training_data = data[ind,]
 
 #another model
 model = train(Species ~., data = training_data, method = 'knn')
 
 #another prediction
 prediction = predict(model, test_data)
 
 #evaluating the performance of the new model
 confusionMatrix(prediction, test_data_label)