Enhanced Spam Detection Using Supervised Learning Models

This study presents an efficient spam detection algorithm that makes use of supervised learning methods. The model performs admirably by utilizing feature selection techniques like Chi-Square and Mutual Information in conjunction with algorithms like Random Forest, SVM, and Naive Bayes. Based on experimental results, it is able to greatly outperform conventional spam filtering systems in effectively identifying spam messages while decreasing false positives. The complete methodology of the study, which integrates the advantages of multiple machine learning algorithms and feature selection techniques, makes a significant contribution to the field. This method increases the efficiency and scalability of the spam detection system in addition to improving detection accuracy. In the current digital era, the suggested approach provides a useful means of preventing spam and establishing dependable communication networks.