Real-Time Phishing Detection and User Education Using Machine Learning

Phishing emails trick users into giving away sensitive information by pretending to be from trusted sources. This paper introduces a real-time phishing detection system that also teaches users how to spot phishing attempts. The system uses natural language processing (NLP) techniques such as sentiment analysis and TF-IDF vectorization and a Random Forest classifier to accurately identify phishing emails. When a suspicious email is detected, the system immediately alerts the user through a graphical interface and provides helpful safety tips. We explain how the dataset was collected, processed, and used to train the model. Results show that the system effectively separates phishing emails from legitimate ones. By combining real-time detection with user education, our approach helps reduce the chances of phishing attacks and increases user awareness.