SMS Spam Detection using Naive Bayes and Test Processing

The mobile user growth is happening at a rapid pace. This leads to an increased trend in mobile messaging, which in turn strengthens the existence of SMS spam. Unlike messaging apps like WhatsApp or Facebook Messenger, SMS does not require an active internet connection. Unsolicited and sometimes dangerous, spam messages pose a significant problem within mobile communications; their primary intent is the dissemination of electronic content aimed at commercial promotion or monetary prize. For this reason, now known as principle protection of ensuring true integrity within telecom communication channels, providing solutions for SMS spam is very important. The inadequacy of current email filtering techniques would be attributed to several problems like the lack of complete folders for storing all your SMS spams and having less message structure formality and features available.The proposed method includes multiple components such as dataset integration, data preprocessing, exploratory data analysis, and feature selection. Furthermore, various machine learning models, including Naive Bayes and Support Vector Machine (SVM), are evaluated for constructing an efficient classifier. The primary objective of SMS spam detection is to safeguard users from the risks associated with spam messages.