Just a little example on how to use the Support Vector Machines model in Python. Support Vector Machines simply separate or classify data based on groupings, by dividing up the surface into “hyperplanes” (if the data point is in hyperplane ‘A’, it’s most likely related to that cluster instead of the cluster in hyperplane ‘B’). Again – a very simplistic description, but it’s not terribly complex to understand at its highest level. A good description in detail can be located here. Interesting to note this can be calculated linearly and non-linearly (particularly in the third dimension). In this example we’re utilizing a cancer dataset that is provided within Scikit learn, and we’re going to predict values based on the “target” field therein. The dataset can be imported as shown in the code.