SVR

Importing the dataset

dataset = read.csv("C:/RClass/Position_Salaries.csv")
dataset = dataset[2:3]

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)

Fitting SVR to the dataset

# install.packages('e1071')
library(e1071)

## Warning: package 'e1071' was built under R version 4.1.3

regressor = svm(formula = Salary ~ .,
                data = dataset,
                type = 'eps-regression',
                kernel = 'radial')

Predicting a new result

y_pred = predict(regressor, data.frame(Level = 6.5))

Visualising the SVR results

# install.packages('ggplot2')
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.1.3

ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = dataset$Level, y = predict(regressor, newdata = dataset)),
            colour = 'blue') +
  ggtitle('Truth or Bluff (SVR)') +
  xlab('Level') +
  ylab('Salary')

Visualising the SVR results (for higher resolution and smoother curve)

# install.packages('ggplot2')
library(ggplot2)
x_grid = seq(min(dataset$Level), max(dataset$Level), 0.1)
ggplot() +
  geom_point(aes(x = dataset$Level, y = dataset$Salary),
             colour = 'red') +
  geom_line(aes(x = x_grid, y = predict(regressor, newdata = data.frame(Level = x_grid))),
            colour = 'blue') +
  ggtitle('Truth or Bluff (SVR)') +
  xlab('Level') +
  ylab('Salary')