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Support Vector Machine (SVM) is another simple algorithm which performs relatively good with less computational cost. SVM can be used both for regression and classification problems but it is widely used for classification. The most used three kernels are: Linear kernel, Polynomial kernel, Radial Basis Function (RBF) and Support Vector Regression (SVR) SVR can handle highly non-linear data using the kernel function. The function implicitly maps the features to higher dimensions meaning higher feature space.