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The separable data with non-linear attributes cannot be tackled by a simple Support Vector Machine algorithm due to which we use a modified version of it called Kernel Support Vector Machine. Essentially in K-SVM it presents the data from a non-linear lower dimension to a linear higher dimension form as such that the attributes belonging to variable classes are assigned to different dimensions. We use a simple Python-SciKit Learn Library to implement and use K-SVM.

For training purposes, we use the SVC class of the library. The difference is in the values for the Kernel parameters of SVC class. In simple SVM’s we use “Linear” for Kernel parameters but in K-SVM we use Gaussian, Sigmoid, Polynomial, etc. wherein we have used Sigmoid.

The only limitation observed in our case is that though this method achieves the highest accuracy but not up to the mark. Hence more advanced models like Deep Learning may be applied in near future for more concrete results.