This project is a machine learning-based system to detect fraudulent credit card transactions using the Random Forest classification algorithm. It is designed to analyze transaction data and ...

Fraud detection is a truly important problem to any e-commerce store, and companies put a lot of money to prevention because a single fraud can cost them a lot of money as well. One of the biggest ...

This project's primary objective is to detect credit card fraud in the real world. Recent growth has resulted in a significant increase in the number of credit card transactions. The objective is to ...

In this paper we mainly focus on credit card fraud detection in real world. Here the credit card fraud detection is based on fraudulent transactions. Generally credit card fraud activities can happen ...

The model, which combines Random Forest and XGBoost classifiers, achieved a recall rate of 95% and an AUC-PR of 97% in testing, outperforming multiple state-of-the-art fraud detection systems. The ...