Exploring Machine Learning Algorithms for Email Spam Filtering

Abstract

Spams are irrelevant or inappropriate emails, and they are on the rise across different digital communication channels globally. These unsolicited and unwelcome perilous emails lead to adverse impacts like security risks, time and resources wastage, financial loss, reputation damage, and legal consequences. Therefore, intelligent anti-spam email filters are required to prevent such emails from being delivered to the recipient's mailbox. Artificial Intelligence and Machine Learning algorithms provide trustworthy countermeasures and affordable solutions to tackle this global problem. Machine Learning techniques like Logistics Regression, Decision Tree, Support Vector Machines, K-nearest neighbor, Naïve Bayes Classifier, and Random Forest provide solutions to classify spam emails with reasonable accuracy and precision. This research explores different approaches to identifying spam emails and developing machine-learning algorithms to filter such emails automatically. Results manifest that the Support Vector Machine and Naïve Bayes Model most accurately classified spam emails. The Support Vector Machine exhibited the best balance between high precision and recall, making it highly effective in identifying spam messages with fewer false positives.