• P. Bhargav Reddy
• Mr. P. Muthyalu

Text messaging (SMS) has become a ubiquitous form of communication worldwide, leading to a rapid increase in spam messages. This rise in spam presents significant risks to both users and businesses, with traditional spam detection methods often proving insufficient to tackle the problem. In this paper, we propose a new system that uses advanced machine learning techniques, specifically multinomial Naive Bayes and Light GBM algorithms, to identify and classify spam messages. Our system distinguishes spam from legitimate messages and further categorizes spam into subcategories such as Financial, Healthcare, and Advertising/Promotional. Experiments show that this approach offers improved accuracy and efficiency compared to traditional methods. Additionally, the ability to understand spam patterns in greater detail provides users and businesses with the resources to better address and mitigate the risks associated with SMS spam

SMS Spam Detection and Classification System

"SMS Spam Detection and Classification System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.216-221, June-2024, Available :http://www.jetir.org/papers/JETIRGL06037.pdf

"SMS Spam Detection and Classification System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp216-221, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06037.pdf