Email Spam Detection Using Machine Learning

Abstract

This project focuses on detecting spam emails and SMS messages using Machine Learning. The system classifies messages into Spam and Ham (Not Spam) categories. A labeled dataset is used to train the model after preprocessing the text through cleaning and feature extraction. The Multinomial Naive Bayes algorithm is applied for classification, with the dataset split into 80% training and 20% testing. The model achieves high accuracy of about 97%, making it effective for email filtering and cybersecurity applications.

Research Team