Support Vector Machines (SVM) learning combines of both the instance-based nearest neighbor algorithm and the linear regression modeling. Support Vector Machines can be imagined as a surface that creates a boundary (hyperplane) between points of data plotted in multidimensional that represents examples and their feature values. Since it is likely that the line that leads to the greatest separation will generalize the best to the future data, SVM involves a search for the Maximum Margin Hyperplane (MMH) that creates the greatest separation between the 2 classes. If the data ara not linearly separable is used a slack variable, which creates a soft margin that allows some points to fall on the incorrect side of the margin. But, in many real-world applications, the relationship between variables are nonlinear. A key featureof the SVMs are their ability to map the problem to a higher dimension space using a process known as the Kernel trick, this involves a process of constructing ne...