Email Spam Detection With Machine Learning

Authors: Rajnish Kumar Chauhan, Praveen Yadav, Vishakha Kashyap, Assistant Professor Dr. Chhaya Singh

Abstract: Email remains one of the most widely used communication tools, but the increasing volume of spam messages has become a persistent issue for both individuals and organizations. Traditional rule-based filtering methods struggle to keep up with the ever-changing techniques used by spammers, leading to inefficiencies in detection. To address this challenge, this study explores a machine learning-based approach to improve spam classification and enhance email security. The research applies algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors (KNN) to differentiate between spam and legitimate emails. By analyzing key features like email content, subject lines, and sender details, the model learns to identify patterns commonly found in spam messages. Performance evaluation using standard datasets demonstrates that machine learning significantly improves detection accuracy, speed, and adaptability compared to conventional methods. The findings suggest that machine learning offers a robust and scalable solution to the growing problem of email spam.