Python Project – Building a Spam Filter Using Naive Bayes

Project Goal: Build a spam filter for SMS messages using the Multinomial Naive Bayes algorithm, with the goal of achieving an accuracy greater than 80% for classifying new messages as spam or ham

Project Outcome: Successfully built a spam filter that achieved 98.02% accuracy on the test set, correctly classifying 1,092 out of 1,114 messages

Project Methods: Cleaned the data by removing punctuation and converting text to lowercase. Trained the model using a dataset of 5,572 manually classified SMS messages, split into 80% for training and 20% for testing. Finally, evaluated the model's performance on the test set by comparing predictions to actual labels