Text mining techniques for machine learningbased SMS spam detection

In contemporary digital communication systems, spam communications have grown to be a significant issue that both inconveniences users and raises security concerns. Unwanted messages can have a detrimental effect on the user experience since they frequently contain dangerous content, phishing links, or ads. A machine learning-based intelligent spam detection system is suggested as a solution to this problem. The system uses learnt patterns from a labelled dataset to analyse textual communication data and categorise it as either authentic (ham) or spam. The suggested method converts unprocessed messages into intelligible numerical representations by applying text preparation techniques as tokenisation, stop-word removal, and feature extraction. The processed dataset is then used to train a machine learning model that can accurately identify spam communications. A web-based application that lets users enter messages and get real-time spam classification results incorporates the developed model. The algorithm successfully distinguishes between spam and non-spam messages and achieves high prediction accuracy, according to experimental results. The suggested system offers a scalable and effective method for automatic spam identification, and it can be improved in the future by adding sophisticated deep learning and natural language processing methods.