Combatting SMS Spam: A Machine Learning Approach for Accurate and Scalable Detection

SMS spam, being an increasing number, causes tremendous challenges toward user security, privacy, and efficiency of communication. In this paper, a machine learning-based SMS spam detection system is proposed to identify the messages as spam or legitimate. Here, a balanced dataset of 5573 labeled SMS messages from Kaggle have been used with steps of tokenization, normalization, and feature extraction from the data using a Vectorizer. It has been enhanced using multiple Classifiers to have a system with high-probability predictions as well as higher classification accuracy. The model attained 98.4% accuracy with an error rate of 1.6% and went on to surpass several other approaches. In addition, the system uses a real-time detection mechanism, returning instant feedback about the classified messages. It is adaptive and scalable, giving the system a strong foundation in combating the constantly evolving spamming techniques through learning and integration of feedback. The work demonstrated here shows that machine learning can be very effective in fighting SMS spam and also gives insight into the future improvement areas such as advanced feature extraction, multilingual support, and real-time model updates.