Credit Card Fraud Detection Using AdaBoost and Majority Voting Techniques

This project offers a sophisticated fraud detection model designed to detect fraudulent transactions in credit card data by utilizing AdaBoost and majority voting. The AdaBoost algorithm improves detection by producing several weak classifiers that target difficult data points, while the majority voting approach combines predictions from different models to increase dependability. Analysis of important characteristics such as transaction timing, location, and amount assists in the identification of possible fraud tendencies. The robust framework of the approach allows for real-time deployment, which promises more security and less financial loss. The application span of this method is increased by its high degree of adaptability and ability to optimize detection without requiring a lot of computing power.