1Assistant Professor, Department of Computer Science, University College for Women (A), VCIWU (Veeranari Chakali Ilamma Women's University), Hyderabad, Telangana. Email: shekarreddy08@gmail.com
2Professor and HOD, Department of CSE (AI&ML), Malla Reddy College of Engineering and Technology & MRTC, MRV, Maisammaguda, Hyderabad, Telangana, India. Email: dranagaraju.cse@gmail.com
3Associate Professor, Department of CSE, School of Engineering, Anurag University, Hyderabad, Telangana State. Email: shyamprasadcse@anurag.edu.in
The exponential growth of mobile communication has led to a surge in unsolicited and fraudulent text messages, commonly referred to as spam. These messages not only inconvenience users but can also pose security threats such as phishing attacks. This project focuses on developing an efficient SMS spam detection system using machine learning techniques to automatically classify messages as "spam" or "ham" (non-spam). A dataset of labelled SMS messages is preprocessed through techniques such as tokenization, stopword removal, and vectorization (TF-IDF). Multiple algorithms, including Naïve Bayes, Support Vector Machine (SVM), Decision Tree, and XGBoost, are evaluated, with XGBoost achieving the highest accuracy. The system aims to provide a lightweight, fast, and reliable solution that can be integrated into messaging platforms to enhance user security and reduce spam intrusion.
Keywords: NA
How to cite this article: Shekar Reddy T, Nagaraju A, Shyam Prasad T. SMS Spam Detection Using XGBoost Algorithm in Machine Learning. Int J Drug Deliv Technol. 2026;16(15s): 945-955. DOI: 10.25258/ijddt.16.15s.106
Source of support: Nil.
Conflict of interest: None