Naive Bayes Spam Filter

Introduction

It is a spam filter project programmed with python. It uses a Naive Bayes classifier and other machine learning algorithms to identify spam messages. It uses a specific dataset that comes from UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/spambase). But you can always modify the code to suit your situation.

The classifier reflects accuracy of around 88.25%. By changing the constant "c" in the classifying equation, the final accuracy could reach up to around 98%.

Where to download

Source Code (Click to check)

Report:

About this project

1: This project is one of the projects in the SMU CS5320 Artificial Intelligence class.