Algorithmic analysis of spam filtering using artificial intelligence

Abstract

This paper presents methods for filtering spam messages using machine learning models in artificial intelligence. Machine learning algorithms are widely used for automatic spam detection because they can learn from large volumes of data and effectively classify new messages. Therefore, the most commonly used machine learning algorithms for spam filtering, namely Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM), are examined. Additionally, the differences between the models, their advantages, and limitations are identified