A Method for SMS Spam Message Detection Using Machine Learning

 In recent years, it has become increasingly common for individuals to connect with their relatives and friends, read the most recent news, and discuss various social topics by using online social platforms such as Twitter and Facebook. As a consequence of this, anything that is considered spam can quickly spread among them. The identification of spam is widely regarded as one of the most significant problems involved in text analysis. Previous studies on the detection of spam concentrated primarily on English-language content and paid little attention to other languages. The information gathered by the University of California; Irvine served as the basis for the development of our spam detection technology (UCI). In this research study, the effectiveness of various supervised machine learning algorithms, such as the J48, KNN, and DT, in identifying spam and ham communications is investigated. SMS spam is becoming more widespread as the number of individuals who use the internet continues to rise and an increasing number of businesses disclose their customers' personal information. E-mail spam filtering is the progenitor of SMS spam filtering, which inherits a significant number of its features. We evaluate the proposed method based on accuracy, recall, and precision. Experiments showed that DT has obtained higher accuracy compared to other machine learning classifiers.