Support Vector Regression (SVR) based Prediction with R

In this video we will learn:- - Support Vector Regression (SVR). - Linear Regression v/s Support Vector Regression. - Kernel, Radial Basis Kernel, Polynomial Kernel, Linear Kernel, Cost Function in the context of Support Vector Regression. - Overfitting, Underfitting, Bias, Variance in the context of Support Vector Regression. - Support Vector, Epsilon Insensitive Tube, Slack Variable in the context of Support Vector Regression. - Support Vector Regression model Creation with R. - Outlier detection in the context of Support Vector Regression model. Code for SVR - # Importing the dataset dataset = read.csv('Position_Salaries.csv') dataset = dataset[2:3] #missing value checking sum(is.na(dataset$Level)) # Fitting SVR to the dataset # install.packages('e1071') library(e1071) regressor = svm(formula = Salary ~ ., data = dataset, type = 'eps-regression', kernel = 'radial') # Predicting a new result y_pred = predict(regressor, data.frame(Level = 6.5)) # Visualising the SVR results # install.packages('ggplot2') library(ggplot2) ggplot() + geom_point(aes(x = dataset$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') # 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') Dataset (copy & save as Position_Salaries.csv) Position Level Salary Business Analyst 1 45000 Junior Consultant 2 50000 Senior Consultant 3 60000 Manager 4 80000 Country Manager 5 110000 Region Manager 6 150000 Partner 7 200000 Senior Partner 8 300000 C-level 9 500000 CEO 10 1000000