ANALYSING AND DETECTING MONEYLAUNDERING ACCOUNTS

This project focuses on detecting suspicious transactions involved in money laundering using machine learning algorithms like SVM, AdaBoost, Naive Bayes, Logistic Regression, and Random Forest. A synthetic transaction dataset is used to train and evaluate models for classifying user behavior. The system includes user registration, login, data upload, and prediction viewing, built with Flask for the backend and HTML/CSS/JavaScript for the frontend.