Cyberanalytics: An Examination of Machine Learning Algorithms For Spam Filtering

The study contributes to the field of information systems and technology by developing an advanced spam detection system, enhancing cybersecurity measures and supporting cyberanalytics initiatives. Our findings demonstrate the capability to classify emails with a high degree of accuracy, reaching an impressive 95.87% accuracy rate, thereby confirming the effectiveness of robust spam filters. Among several machine learning algorithms evaluated, the Random Forest (RF) classifier stands out, exhibiting superior performance with a low misclassification rate of 4.13%. This research also explores key features that distinguish spam from legitimate emails, providing valuable insights for the development of high-quality, relevant curriculum. We conclude by highlighting the role of empirical analysis and rigorous methodological evaluation in crafting adaptable technological solutions that safeguard organizational interests in the digital era.