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SVM models can be built in R using a pre-processed data set.
The data set should be split into train and test data after loading it into the R environment.
The data can be trained using linear and non-linear kernels
The hyperparameters associated with the kernels can be tuned to find the value which will give us the best test set performance.
It is possible to train both classification and regression SVM models in R.
The difference in classification and regression models is in the type of the response variable.