Artificial Intelligence-Based for Email Spam Filtering

Chen, Lan Bin (2024) Artificial Intelligence-Based for Email Spam Filtering. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

We humans rely on electronic communication, such as email, in this modern era. However, email spamming has become a big issue in spreading unsolicited emails. This project targets the widespread problem of email spam by implementing machine learning techniques, with a particular emphasis on spam filtering using artificial intelligence (AI). The major purpose is to provide an efficient solution for the identification and filtration of spam emails, hence improving spam filtering accuracy. The project's scope includes the creation of an AI-powered application capable of effectively identifying and filtering spam emails. Preprocessing of email content, feature extraction using techniques such as Count Vectorization and TF-IDF Vectorization, and the deployment of machine learning models such as Support Vector Machines, Naive Bayes, and K-Nearest Neighbour classifiers are among the key functionalities. The results show that the AI-driven spam filtering system achieves a noteworthy accuracy of 98.65% in recognizing spam emails. The project's conclusion emphasises the importance of employing AI in spam filtering to provide accurate and dependable findings. Finally, the research successfully addresses the ongoing problem of email spam through an innovative use of machine learning. The results contribute to the evolution of spam filtering techniques, providing a reliable reference for future email security research and development