Enhancing SMS Classification with Ensemble Machine Learning Techniques

In today’s digital age, SMS classification is essential for protecting users from spam, phishing, and other malicious activities, ensuring a secure and efficient messaging environment. This paper outlines the building of an SMS classifier that use machine learning techniques to effectively differentiate between spam messages and non-spam messages. The classifier is trained using a labeled dataset of various SMS texts that have been classified as spam or legitimate. With the help of this dataset, the classifier gains the ability to identify traits and patterns that are frequently present in spam communications. The classifier’s efficacy is evaluated based on its capacity to precisely recognise and weed out unwanted spam communications, boosting messaging platforms’ security and optimising user experience in general. In order to identify the best method for SMS categorization, a variety of machine learning algorithms, such as Support Vector Machines, Multinomial Naive Bayes, and Random Forests are applied.TF-IDF, or term frequency-inverse document frequency, is a feature extraction method, used to convert text input into a numerical representation suitable for handling by the machine learning models. The outcomes showed that the classifier attains a high degree of accuracy and considerably lowers the amount of spam that ends up in the user’s message mailbox. This not only keeps the messaging environment clear of clutter, but it also stops any security risks like phishing and scams that could be brought on by spam messages. By integrating our SMS classifier into messaging apps, it is possible to enable real-time spam identification and filtering, improving user experience and fostering a safer environment for digital communication.