Credit card fraud detection uses algorithms and machine learning to identify unauthorized transactions. It involves data collection, preprocessing, and models such as logistic regression and random forests. Techniques include rule-based systems, anomaly detection, and real-time monitoring to prevent fraud. The dataset is sourced from Kaggle.

Building a model that uses a dataset containing transaction data to detect fraudulent transactions.