logisticRegression.R

#Loading the dataset
#Machine Learning From A-z kirill eremenko
library("tidyverse")
library(caTools)
dataset=read_csv("Data/Social_Network_Ads.csv")

#Splitting the dataset
set.seed(123)
split=sample.split(dataset$Purchased,SplitRatio=3/4)
training_set = subset(dataset,split==TRUE)
test_set=subset(dataset,split==FALSE)

#Feature Scaling
training_set[,1:2] =scale(training_set[,1:2])
test_set[,1:2]=scale(test_set[,1:2])

#Train the Logistic Regression on Training_set
classifier = glm(formula=Purchased~.,
                 family=binomial,
                 data=training_set)

#predict the result on test set
prob_pred = predict(classifier,type='response',newdata=test_set[-3])
y_pred=ifelse(prob_pred>0.5,1,0)
cm = table(test_set$Purchased,y_pred)

#Visualising the training set result
#install.packages('ElemStatLearn')
library(ElemStatLearn)
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')
prob_set = predict(classifier,type='response',
                   newdata=grid_set)
y_grid = ifelse(prob_set > 0.5,1,0)
plot(set[,-3],main='Logistic Regression(Training Set)',
     xlab = 'Age',
     ylab = 'EstimatedSalary',
     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 result
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')
prob_set = predict(classifier,type='response',
                   newdata=grid_set)
y_grid = ifelse(prob_set > 0.5,1,0)
plot(set[,-3],main='Logistic Regression(Test Set)',
     xlab = 'Age',
     ylab = 'EstimatedSalary',
     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'))