Advancing Email Spam Classification using Machine Learning and Deep Learning Techniques

Abstract

Email communication has become integral to various industries, but the pervasive issue of spam emails poses significant challenges for service providers. This research proposes a study leveraging Machine Learning (ML) and Deep Learning (DL) techniques to effectively classify spam emails. Methods such as Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Artificial Neural Networks (ANNs) are employed to construct robust models for accurate spam detection. By amalgamating these techniques, the aim is to enhance efficiency and precision in spam detection, aiding email and IoT service providers in mitigating the detrimental effects of spam. Evaluation of the proposed models revealed promising outcomes. LR, RF, and NB achieved an impressive accuracy of 97% and an F1-Score of 97.5%, showcasing their efficacy in accurately identifying spam emails. The ANN model demonstrated slightly superior performance, with 98% accuracy and 97.5% F1-score, suggesting potential improvements in accuracy and robustness in spam filtering systems. These findings underscore the viability of both traditional ML algorithms and DL approaches in addressing the challenges of email spam classification, paving the way for more effective spam detection mechanisms in electronic communication platforms.