The dataset used in this project contains over 6 million simulated data transactions with a tiny percent (0.129%) representing fraud transactions. The goal is to work with an imbalanced dataset to detect anomalies (fraud transactions) while minimizing Type I and II errors.
This project contains a PDF slide presentation in addition to the Jypter notebook.