ML Using R Section 7 Support Vector Regression

# Import data set
dataset <-  read.csv("G:\\RStudio\\udemy\\ml\\Machine Learning AZ\\Part 2 - Regression\\Section 7 - Support Vector Regression (SVR)\\SVR\\Position_Salaries.csv")
dataset <-  dataset[2:3]
head(dataset)

head(dataset, n = 10)

# Polynomial Regression
# taking care of missing values
# Test for missing values
sum(is.na(dataset$Level))

[1] 0

sum(is.na(dataset$Salary))

[1] 0

There is no missing values in the dataset.

# fitting the regression model to the dataset
# install.packages("e1071"")
library(e1071)
regressor <-  svm(formula = Salary ~ ., data = dataset, type = "eps-regression")
# for svm classification you use c-classification
summary(regressor)

Call:
svm(formula = Salary ~ ., data = dataset, type = "eps-regression")


Parameters:
   SVM-Type:  eps-regression 
 SVM-Kernel:  radial 
       cost:  1 
      gamma:  1 
    epsilon:  0.1 


Number of Support Vectors:  6

# predicting a new result 
y_pred = predict(regressor, data.frame(Level = 6.5))
y_pred

       1 
177861.1 

# Fitting the  regression model
# add a new column in the dataframe
# Plot
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 (Regression Model)") +
  xlab("Levels") +
  ylab("Salary")

# visualizing polynomial regression

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 (Regression Model)") +
  xlab("Levels") +
  ylab("Salary")