AI-Based Email Spam and Scam Detection

Email remains an important communication channel for education, business, and personal use. However, spam,phishing, fraudulent offers, and scam messages can expose users to financial loss, identity theft, and malware. Thispaper presents a proposed AI-Based Email Spam and Scam Detection system that classifies email text as likelylegitimate or suspicious by using text preprocessing and machine-learning techniques. The system can tokenizeand normalize email content, transform text into numerical features using methods such as Term Frequency–Inverse Document Frequency (TF-IDF), and apply a classifier such as Multinomial Naive Bayes or LogisticRegression. The predicted category is displayed with a simple explanation or warning to help users make saferdecisions. The proposed design includes data preparation, model training, evaluation, and a user interface. Thisdocument is a student research-paper draft: implementation details, dataset information, measured performance,and references must be verified and updated to match the student's actual work before submission