Using a labeled dataset of spam and non-spam emails, we train the Naive Bayes classifier. It learns the probabilities of different words appearing in spam or non-spam emails, using this information to classify new incoming emails.

Evaluating the performance

To assess the performance of our classifier, we use evaluation metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how well the Naive Bayes algorithm is performing in filtering out spam emails.

Future Scope and Improvements

The Naive Bayes algorithm has proven to be an effective classification tool, but there is always room for improvement. Researchers continue to explore enhancements, such as relaxing the independence assumption or incorporating more advanced techniques like ensemble methods.

Conclusion