Artificial Intelligence Model for Email Spam Detection

The continuous attack of email spam on internet users has geometrically increased and necessitated the need for a more robust and dependable anti-spam technology for filtering email spam. Presently, individuals and organizations often lose millions of dollars to fraud by mere opening or responding to email spam sent to their email inboxes despite the anti-spam software in existence. This has brought about major economic losses, email traffic problems, a shortage of memory space, and limits the system’s computing power. This paper proposes an artificial intelligence (AI) model that trains, tests, and validates, email datasets using machine learning classification, regression, and clustering algorithms. The performance metric was the root mean squared error. The error value achieved was 0.02349, which indicated the effectiveness of the proposed AI model in filtering email spam. A web application was built to test the robustness, performance, accuracy, and reliability of the system. The results revealed an excellent performance at a minimal system error level of 0.0004.