library("e1071", lib.loc="/Library/Frameworks/R.framework/Versions/3.4/Resources/library")
setwd("~/Desktop/Machine Learning A-Z/Part 2 - Regression/Section 4 - Simple Linear Regression")
dataset = read.csv('Salary_Data.csv')
View(dataset)
# Splitting the dataset into the Training set and Test set
# install.packages('caTools')
library(caTools)
set.seed(123)
split = sample.split(dataset$Salary, SplitRatio = 2/3)
training_set = subset(dataset, split == TRUE)
test_set = subset(dataset, split == FALSE)
View(training_set)
View(training_set)
View(test_set)
regressor = lm(formula = Salary ~ YearsExperience,
data = training_set)
summary(regressor)
y_pred = predict(regressor, newdata=test_set)
y_pred
install.packages('ggplot2')
library(ggplot2)
ggplot() +
geom_point(aes(x = training_set$YearsExperience,y=training_set$Salary),
colour = 'red') +
geom_line(aes(x = training_set$YearsExperience, y = predict(regressor,newdata=training_set)),
colour = 'blue') +
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') +
ggtitle ('Salary vs Experience (Test set)') +
xlab ('Years of experience') +
ylab('Salary')
