Spam SMS Detection Using Machine Learning

– In today’s digital communication era, unsolicited and malicious text messages, commonly known as spam, pose a significant threat to user privacy and mobile security. This project aims to develop an intelligent and automated system for SMS spam detection using machine learning techniques, with a focus on the Support Vector Machine (SVM) algorithm. The objective is to classify incoming messages as either “spam” or “ham” (non-spam) with high accuracy and efficiency. The system is trained on a labeled SMS dataset containing a mixture of spam and ham messages. Text preprocessing techniques such as tokenization, lowercasing, stop-word removal, and TF-IDF vectorization are applied to convert raw text into numerical features suitable for machine learning. The SVM classifier, known for its robustness in high-dimensional spaces, is trained on the transformed dataset to find the optimal decision boundary between the two classes. The model’s performance was tested using widely accepted metrics such as accuracy, precision, recall, and F1-score to ensure reliable results. Experimental results demonstrate that SVM provides a reliable and effective method for spam detection with strong generalization capabilities. .