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@article{191444,
        author = {G. Subasri and A. Anandhi},
        title = {EMAIL SPAM DETECTION USING MACHINE LEARNING ALGORITHMS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6327-6330},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191444},
        abstract = {In our modern digital landscape, email stands as an essential pillar of communication. We rely on it for everything from personal chats and business deals to academic research and official records. However, this growth has been mirrored by a massive surge in spam. These unsolicited messages often hide advertisements, deceptive offers, phishing links, or malware. Beyond just being a nuisance that wastes time, they pose serious risks like identity theft and financial fraud. Because spammers are constantly evolving their tactics to slip past traditional rule-based filters, those older methods are no longer enough. This project introduces an Email Spam Detection System driven by Machine Learning. By analyzing text through TF-IDF feature extraction, the system classifies messages as "spam" or "ham" using Naive Bayes, Logistic Regression, and Support Vector Machines. Our results show that these learning-based approaches offer the high accuracy and reliability needed to protect users and improve their digital experience.},
        keywords = {Email Spam Detection, Machine Learning, Text Classification, TF-IDF, Naive Bayes, Logistic Regression, Support Vector Machine, Natural Language Processing, Spam Filtering, Information Security.},
        month = {January},
        }