Email Spam Dectetion Using Machine Learning

The proliferation of unsolicited spam emails poses significant challenges for email users and service providers. Efficient detection and filtering of spam are crucial to maintain communication integrity and reduce security risks. This paper presents a comparative study of three machine learning algorithms—Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression—applied to spam email detection. The methodology incorporates comprehensive data preprocessing, including tokenization, stop-word removal, and TF-IDF vectorization to transform textual data into meaningful features. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics on a benchmark email dataset. Experimental results demonstrate that SVM achieves superior accuracy, while Naïve Bayes offers computational efficiency. The study contributes to a better understanding of the trade-offs among these algorithms in spam classification tasks. Future work includes exploring deep learning approaches and incorporating real-time adaptive filtering.