# Importing the dataset dataset = read.csv('Social_Network_Ads.csv') dataset = dataset[3:5] # Encoding the target feature as factor dataset$Purchased = factor(dataset$Purchased, levels = c(0, 1)) # Splitting the dataset into the Training set and Test set # install.packages('caTools') library(caTools) set.seed(123) split = sample.split(dataset$Purchased, SplitRatio = 0.75) training_set = subset(dataset, split == TRUE) test_set = subset(dataset, split == FALSE) # Feature Scaling training_set[-3] = scale(training_set[-3]) test_set[-3] = scale(test_set[-3]) # Fitting Decision Tree Classification to the Training set # install.packages('rpart') library(rpart) classifier = rpart(formula = Purchased ~ ., data = training_set) # Predicting the Test set results y_pred = predict(classifier, newdata = test_set[-3], type = 'class') # Making the Confusion Matrix cm = table(test
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