#Simple Linear Regresseion

Salary = read.csv(file.choose())

#Splitting the data into training and test sets

library(caTools)

set.seed(123)

split = sample.split(Salary$Salary,SplitRatio = 2/3)

training_set = subset(Salary, split==T)

test_set = subset(Salary, split==F)

#Fitting simple linear regression to the Training Set

regressor = lm(formula = Salary ~ YearsExperience,

data = training_set)

#predecting the test set results

Y_pred = predict(regressor,newdata = test_set)

Y_pred

?predict

#Visualising Traing set

library(ggplot2)

ggplot()+

geom_point(aes(x=training_set$YearsExperience, y=training_set$Salary),

color='red')+

geom_line(aes(x=training_set$YearsExperience,

y =predict(regressor,newdata = training_set)),

color='blue')+

ggtitle('Salary Vs Experience (Training Set)')+

xlab("Years of Experience")+

ylab("Salary")+

geom_smooth()

#Visualising Test set

ggplot()+

geom_point(aes(x=test_set$YearsExperience, y=test_set$Salary),

color='red')+

geom_line(aes(x=training_set$YearsExperience,

y =predict(regressor,newdata = training_set)),

color='blue')+

ggtitle('Salary Vs Experience (Test Set)')+

xlab("Years of Experience")+

ylab("Salary")