Optimizing SMS Spam Detection Using Machine Learning Models

Spam detection is an important problem in natural language processing. In this study, multiple machine learning models are evaluated for SMS spam detection using the SMS Spam Collection dataset. The dataset is preprocessed using text cleaning techniques and TF-IDF vectorization. Several models including Naïve Bayes, Logistic Regression, Random Forest, and Support Vector Machine (SVM) are trained and evaluated using accuracy, precision, recall, and F1-score. Experimental results show that SVM achieves the highest accuracy of 98%, making it the most effective model for spam detection in this work.