“SMS SPAM DETECTION USING MACHINE LEARNING”

Abstract

In today's digital era, where digitization is pervasive, SMS has emerged as a crucial form of communication. Unlike platforms such as Facebook and WhatsApp, SMS doesn't depend on an active internet connection. However, the prevalence of spam SMS poses a significant threat as it can deceive mobile users into divulging confidential information, leading to severe consequences. Recognizing the gravity of this issue, there is a pressing need to develop an effective spam filtration solution. The model would be trained to identify spam messages based on a variety of features, such as the keywords in the messages was sent. Machine learning algorithms can be used to train a model to identify spam messages, such as Naive Bayes classifiers serve as straightforward and resilient probabilistic classifiers, proving especially valuable in tasks related to text classification. The algorithm operates on the premise of assuming conditional independence among features given a class, providing a practical initial approximation for real-world scenarios.