An Intelligent SMS Spam Detection System Using Ensemble Machine Learning Techniques

The rapid growth of mobile communication has resulted in a significant increase in unsolicited and fraudulent spam messages. These messages not only inconvenience users but also pose serious security and privacy risks. Manual filtering of spam messages is inefficient due to the large volume of data generated daily. This paper proposes an intelligent SMS spam detection system using ensemble machine learning techniques. Text preprocessing is applied to clean and normalize SMS content, followed by feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Multiple machine learning classifiers, including Naive Bayes, Logistic Regression, and Support Vector Machine, are trained and combined using a voting-based ensemble approach. Experimental results on the SMS Spam Collection dataset demonstrate that the ensemble model outperforms individual classifiers by achieving higher accuracy and reduced false positives. The proposed system provides an effective and reliable solution for automated spam detection.