Spam message detection

Spam messages are a common issue in digital communication systems like email and SMS. These unwanted messages often include advertisements, phishing links, harmful content, and fraudulent offers that can cause financial and security risks. This paper presents a machine learning spam message detection system that classifies messages as spam or not spam. The system uses text preprocessing, feature extraction with Term Frequency Inverse Document Frequency, and classification through the Naive Bayes algorithm. A centralized server processes messages and provides real-time predictions via a web application. Performance improvement techniques enhance classification accuracy and lower false positives. The proposed system achieves high accuracy and reliable results while ensuring data privacy and scalability. Keywords: Spam Detection, Text Classification, Naive Bayes, TF-IDF, Machine Learning, Natural Language Processing, Email Filtering.