Developed a Spam Message Predictor that classifies messages as spam or ham using machine learning techniques. Python was used for data preprocessing, analysis, and model training to ensure accurate predictions. The system supports bulk analysis by accepting Excel files in multiple formats, enabling efficient handling of large datasets. It also includes a real-time input feature where users can manually enter messages and instantly receive predictions. The frontend, built using HTML, CSS, and JavaScript, provides an interactive and user-friendly interface. This project demonstrates practical skills in machine learning, data handling, and integrating backend models with a responsive frontend for real-world applications.