Optimizing SMS Spam Detection: An In-Depth Analysis of Machine Learning Approaches

Since mobile messaging has become more popular, there is a rising worry in the digital communication environment about SMS spam detection because of the surge in unwanted and potentially hazardous communications. Beyond conventional rule-based approaches, machine learning (ML) provides a potent way to automate the detection and filtering of spam in SMS (Short Message Service) systems. By converting text data into numerical representations, these techniques enable models to identify trends in both spam and ham (non-spam) communications. SMS spam datasets are used to train the models, and measures including precision, recall, and F1-score are used to assess the results.The outcomes show that machine learning models are capable of identifying spam. Because they work well in text classification tasks, Naive Bayes and Logistic Regression in particular perform well. According to the study's findings, machine learning provides a reliable, scalable, and adaptable method for detecting SMS spam that can handle big datasets and changing spam strategies. Advanced NLP (Natural Language Processing) techniques is integrated into SMS systems to greatly enhance the performance of ML models. Future research will include multilingual spam filtering and real-time detection.