Classifiers and Algorithms in Spam Detection

With the rapid growth of digital communication, spam messages and harmful content have become a significant problem. This issue affects email, social media, and messaging platforms. This paper examines the classifiers and algorithms used for spam detection. It examines the role of machine learning classifiers, including Naïve Bayes, Support Vector Machines (SVM), Decision Trees, Random Forest, and Logistic Regression. These classifiers evaluate text and, in some cases, images to distinguish between legitimate and spam messages. By understanding these methods, users and developers can enhance system security, improve the user experience, and mitigate the negative impact of spam.