practice1r.R

# Importing the dataset
dataset = read.csv('Salary_Data.csv')

# Splitting the dataset into the Training set and Test set
#install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Salary, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)

# Feature Scaling
# training_set = scale(training_set)
# test_set = scale(test_set)
regressor = lm(formula = Salary ~ YearsExperience,
               data = training_set)
# Predicting the test set results
y_pred = predict(regressor, newdata = test_set)

# Visualisation of training results 
library(ggplot2)
ggplot()+
  geom_point(aes(x= training_set$YearsExperience, y = training_set$Salary),
             colour = 'red')+
  geom_line(aes(x=training_set$YearsExperience, y = predict(regressor, newdata = training_set)),
             colour = 'blue')+
  ggtitle('Salary vs Experience (training set)')+
  xlab('Years of Experience')+
  ylab('Salary')

# Visualisation of test results 
library(ggplot2)
ggplot()+
  geom_point(aes(x= test_set$YearsExperience, y = test_set$Salary),
             colour = 'red')+
  geom_line(aes(x=training_set$YearsExperience, y = predict(regressor, newdata = training_set)),
            colour = 'blue')+
  ggtitle('Salary vs Experience (test set)')+
  xlab('Years of Experience')+
  ylab('Salary')