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@article{184180,
        author = {Vishakha Bhagwan Damodhar and Prof. Sonali Kiran Shewale},
        title = {COMPARATIVE STUDY OF NAÏVE BAYES AND SUPPORT VECTOR MACHINE FOR EMAIL AND SMS SPAM DETECTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {413-419},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184180},
        abstract = {The rapid rise in digital communication through emails and SMS has made spam detection a key focus in Natural Language Processing (NLP). Spam messages waste users' time and create security risks like phishing, identity theft, and malware attacks. This paper presents a study and implementation of spam classification using two supervised machine learning algorithms: Naïve Bayes and Support Vector Machines (SVM). A preprocessed dataset with labeled spam and ham messages was used to train and test the models. Experimental results show that SVM achieves higher accuracy and precision, while Naïve Bayes works faster and uses fewer computing resources. The findings suggest that a combined approach could leverage the strengths of both algorithms for real-time use in mobile and cloud-based platforms.},
        keywords = {Spam Classification, Naïve Bayes, SVM, NLP, Email Filtering, SMS Detection},
        month = {September},
        }