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@article{198217,
        author = {Nehal Raj and Rishi Raman and Rukesh Manivannan Vanniya Reddy and Shrikant Khandagale},
        title = {Spam Email Detection Using Machine Learning Classification Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {10183-10190},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198217},
        abstract = {Electronic mail remains the dominant vector for unsolicited and malicious digital communication. Traditional rule-based filters struggle to adapt to the continuously evolving tactics employed by spammers, motivating data-driven approaches. This paper presents a rigorous comparative analysis of five supervised machine learning classifiers—Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting (GB)—for the binary classification of spam and legitimate (ham) email messages. Feature extraction is performed using Term Frequency–Inverse Document Frequency (TF-IDF) vectorisation augmented with a curated set of structural metadata features. Experiments are conducted on the publicly available SpamAssassin and Enron email datasets containing a combined total of 33,716 messages. The SVM classifier with a Radial Basis Function (RBF) kernel achieves the highest F1-score of 0.9891 with a false-positive rate of 0.47%, whereas Naive Bayes exhibits the lowest computational overhead, completing training in under 1.2 seconds. Mathematical formulations of all algorithms, together with a detailed error analysis, are presented to guide practitioners in selecting an appropriate model for real-world deployment.},
        keywords = {Email Spam Detection, Machine Learning, TF-IDF, Naive Bayes, Support Vector Machine, Random Forest, Gradient Boosting, Text Classification, Natural Language Processing.},
        month = {April},
        }