Development of a Machine Learning-Based Email Spam Filtering System
Computer Science — 2025, Undergraduate
email spammachine learningdata miningspam filtering
This study developed a Machine Learning-Based Email Spam Filtering System to distinguish spam messages from legitimate correspondence. The volume of unsolicited email continues to burden users and network resources. The system extracts text and domain features, trains a classifier on a labelled corpus, and routes incoming messages accordingly. Built with PHP and MySQL, the system was evaluated with a balanced dataset of spam and ham messages. Findings showed high precision and recall in spam detection. The study recommends deployment at mail servers with periodic retraining to adapt to evolving spam patterns.
For reference and learning only. Do not submit this work as your own.
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