A sophisticated SVM with Dimensionality for non-linearly separable data.
Implemented a program using sklearn library catering to the dual problems and used Kernal functions to move data to higher dimensions in case it is non linearly separable in its native state.
This implementation uses 4 different kernels to find the most optimized values of alpha for high prediction confidence.
