Spam Detection using machine learning
Abstract
The rapid growth of digital communication has led to a significant increase in spam messages, which pose major challenges for users and organizations. This paper presents an efficient machine learning approach for detecting spam messages. The system employs supervised learning algorithms to classify messages as either spam or non-spam (ham). Among the various algorithms tested, Support Vector Machine (SVM) was chosen for its higher accuracy. The proposed model is trained on a labelled dataset and evaluated using several performance metrics, including accuracy, precision, recall, and F1-score. The results demonstrate that the model is effective in identifying spam messages and can be implemented in real-world applications.