The task here is to build a spam filter using Bayesian learning.  You should implement the naive Bayes learning algorithm (NB) described in class, and apply it to learn a classifier that distinguishes spam from legitimate email messages as accurately as possible.

You must write a preprocessor that converts an email message into a vector of feature values.  Use your creativity in choosing features that you think are likely to be predictive, i.e. likely to be different for the two classes of messages.  Use at least 100 different features, and make sure your NB implementation can handle at least 200 features and 2000 training examples.  Most of your features are likely to be words, but also use other features such as the time of day of the message.  Use human intelligence to avoid features that may be highly predictive for the training data, but that will not be useful for test data, for example the date of the email message.

Here is a training set of about 500 spam messages and here is a training set of about 500 legitimate messages.  You will have to divide each file into its separate email messages; this is not difficult.  You must also write a script to preprocess each message, to compute the value for it of each feature that you use.

• In your experience, is the NB algorithm useful for spam filtering?
• What are the strengths and weaknesses of using NB compared to hand-coding a spam recognizer?
• What programming problems did you have to solve in order to make your implementation of NB successful?
• What have you learned about the practicalities of data mining applications, like this one?