Support Vector Machine (SVM) is a popular machine-learning technique that performs the classification by constructing the hyper-planes in the multi-dimensional space that separates the different classes (Vapnik, 1995). The radial basis function (RBF) is used as the kernel, and the grid search is harnessed to optimize the penalty parameter C (1,000, 5,000, 10,000, 50,000, and 100,000) and the kernel parameter gamma (0.0001, 0.0005, 0.001, 0.005, 0.01, and 0.1).