Support Vector Machine

Data Processing

• Splitting the data as training and test set, using createDataPartition() function from the caret package.
• Choosing centering and scaling for SVM by setting preProcess = c("center", "scale")
• Applying a 20-fold cross-validation as the trainControl, and trained the train data set with SVM methods.
• The RMSE using SVM is 0.3477 with R_square = 0.6200 and MAE 0.2588.

Plot the SVM prediction vs Actual average rating