knn

Classification template

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)

## Warning: package 'caTools' was built under R version 3.4.2

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 classifier to the Training set

Create your classifier here

Predicting the Test set results with K-NN trainding set

library(class)

## Warning: package 'class' was built under R version 3.4.2

y_pred = knn(train=training_set[,-3],
             test = test_set[,-3],
             cl= training_set[,3], 
             k=5)

Making the Confusion Matrix

cm = table(test_set[, 3], y_pred)

Visualising the Training set results

library(ElemStatLearn)

## Warning: package 'ElemStatLearn' was built under R version 3.4.2

set = training_set
X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
grid_set = expand.grid(X1, X2)
colnames(grid_set) = c('Age', 'EstimatedSalary')
y_grid = knn(train=training_set[,-3],
             test=grid_set,
             cl=training_set[,3],
             k=5)
plot(set[, -3],
     main = 'KNN (Training set)',
     xlab = 'Age', ylab = 'Estimated Salary',
     xlim = range(X1), ylim = range(X2))
contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))

Visualising the Test set results

library(ElemStatLearn)
set = test_set
X1 = seq(min(set[, 1]) - 1, max(set[, 1]) + 1, by = 0.01)
X2 = seq(min(set[, 2]) - 1, max(set[, 2]) + 1, by = 0.01)
grid_set = expand.grid(X1, X2)
colnames(grid_set) = c('Age', 'EstimatedSalary')
y_grid = knn(train=training_set[,-3],
             test=grid_set[,-3],
             cl=training_set[,3],
             k=5)
plot(set[, -3], main = 'KNN(Test set)',
     xlab = 'Age', ylab = 'Estimated Salary',
     xlim = range(X1), ylim = range(X2))
contour(X1, X2, matrix(as.numeric(y_grid), length(X1), length(X2)), add = TRUE)
points(grid_set, pch = '.', col = ifelse(y_grid == 1, 'springgreen3', 'tomato'))
points(set, pch = 21, bg = ifelse(set[, 3] == 1, 'green4', 'red3'))