A Comparative Study of Machine Learning Algorithms for Email Spam Detection

The conflict between spam emails and user inboxes has recently gained attention from cybercriminals, making the identification of spam a critical process for both users and businesses. In this regard, we analyze the performance of three widely used machine learning techniques for classifying email spam using the UCI Spambase dataset—Naive Bayes, Support Vector Machines (SVM) and Random Forest (RF). Each model will be evaluated based on achieved accuracy, precision, recall, F1 score computed value alongside training time. Although Random Forest Classifier performed best with greater accuracy measurement than comparative models, Naive Bayes classifier excelled at fast processing speeds.