Support Vector machines are a very recent technique used for making decision boundaries. This machine learning method has been created by Russian scientists Vladimir Vapnik and Hava Siegelmann in early 90's. In this article we will see the concept on which support vector machines and an implementation on Scikit-Learn with Python. Suppose we have a space where we have samples of data points belonging to two classes (Orange and green points in our case) The question is how to divide the orange points from the green points. The first idea of SVM is that we want to draw a straight line. BUT WHICH LINE ? As we see in the last figure, there an infinity of lines that solve the problem. But which one to choose? Well we would like to draw a straight line inside the widest street that separates the orange samples from the green samples. So the approach is to try to put the line inside the street in such a way that the street between the two classes be