This is the second part of a series on spam classification.

The first part of this series talks about how to label a large amount of raw messages as either spam or ham. This labelled data is then used as training data for the classifier which will be described in part two. The third part will deal with how to run the streaming classifier in a production environment. Finally, the fourth part will talk about how to visualize and test the classifier.