• DEEPAKPANDI P
• Dr.S.Gnanapriya
• Dr.S.Gnana Priya

The proliferation of unsolicited and malicious emails, commonly known as spam, has led to a pressing need for effective detection mechanisms. Traditional rule-based approaches, while effective in the past, struggle to keep up with the rapidly evolving nature of spam tactics. Machine Learning (ML) offers a dynamic and adaptive solution to spam detection, leveraging data-driven techniques to classify emails based on patterns, content, and sender behavior. This paper presents an ML-based spam email detector that employs a variety of algorithms, including Naive Bayes, Decision Trees, and Support Vector Machines (SVM), to identify and filter spam emails. By training models on large datasets of labeled email messages, the system can learn to differentiate between legitimate and spam emails with high accuracy. Performance is evaluated through precision, recall, and F1-score, demonstrating the effectiveness of machine learning in combating the ongoing spam email problem. Email communication has become an integral part of modern life, both personally and professionally. However, the convenience of email also brings challenges, one of the most significant being the inundation of spam emails unsolicited and often harmful messages that clutter inboxes and pose security risks.

SVM, F1-score, Naïve Bayes, Decision Trees

"Spam Email Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.e242-e246, September-2024, Available :http://www.jetir.org/papers/JETIR2409431.pdf

"Spam Email Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppe242-e246, September-2024, Available at : http://www.jetir.org/papers/JETIR2409431.pdf