An implementation of a Spam Filter in Python that uses the Naive Bayes Model to classify emails as spam or ham.

The program uses a small portion of NLTK to tokenize and clean out the "noise" words that may appear across the text.

The reference I used was Machine Learning for Hackers by Drew Conway and John Myles White, Chapter 3. The code is in R but should be easy to follow.

The data sets were obtained from the SpamAssassin Public Corpus, which can be found here.

Also this was a hobby project, so I do not have any associated project report.

Simply run the script 'ClassifierDemo.py' once you're in the program directory:

python ClassifierDemo.py

As the results will show, the classifier has roughly 90% success in distinguishing ham and spam from hard ham mails.

You will have to uncomment the last few lines in the script Classifier.py. Then simply run the command

python SpamClassifier.py

and you should be good to go.

As always, feel free to contact me at [email protected] for suggestions for improvement or bug reports. Thank you!!