R_simple_linear_regression.R

dataset = read.csv('Salary_Data.csv')


# dataset$Age = ifelse(is.na(dataset$Age),
#                      ave(dataset$Age, FUN = function(x) mean(x, na.rm = TRUE)),
#                      dataset$Age)
# dataset$Salary = ifelse(is.na(dataset$Salary),
#                         ave(dataset$Salary, FUN = function(x) mean(x, na.rm = TRUE)),
#                         dataset$Salary)


# dataset$Country = factor(dataset$Country,
#                          levels = c('France', 'Spain', 'Germany'),
#                          labels = c(1, 2, 3))
# dataset$Purchased = factor(dataset$Purchased,
#                            levels = c('No', 'Yes'),
#                            labels = c(0, 1))


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)

# training_set[, 2:3] = scale(training_set[, 2:3])
# test_set[, 2:3] = scale(test_set[, 2:3])

regressor = lm(formula = Salary ~ YearsExperience,
               data = training_set)

y_pred = predict(regressor, newdata = test_set)

# install.packages('ggplot2')
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') +
  geom_smooth(method = 'lm') +
  ggtitle('Salary vs Experience (Training set)') +
  xlab('Years of experience') +
  ylab('Salary')

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') +
  geom_smooth(method = 'lm') +
  ggtitle('Salary vs Experience (Test set)') +
  xlab('Years of experience') +
  ylab('Salary')