R Code

# 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

# Data called ‘Social_Network_Ads’ User ID Gender Age EstimatedSalary Purchased 15624510 Male 19 19000 0 15810944 Male 35 20000 0 15668575 Female 26 43000 0 15603246 Female 27 57000 0 15804002 Male 19 76000 0 15728773 Male 27 58000 0 15598044 Female 27 84000 0 15694829 Female 32 150000 1 15600575 Male 25 33000 0 15727311 Female 35 65000 0 15570769 Female 26 80000 0 15606274 Female 26 52000 0 15746139 Male 20