# Support Vector Regression (SVR)

rm(list = ls())

# Importing the dataset
dataset <- read.csv(file.path(getwd(),'Data/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')

if (!require("e1071"))
  install.packages("e1071") # to install the package if you don't have it
library(e1071)
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)
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') +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))

# 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') +
  theme_bw() +
  theme(plot.title = element_text(hjust = 0.5))