In this project, we will use a standard imbalanced machine learning dataset referred to as the “Credit Card Fraud Detection” dataset.

The data represents credit card transactions that occurred over two days in September 2013 by European cardholders.

The dataset is credited to the Machine Learning Group at the Free University of Brussels (Université Libre de Bruxelles) and a suite of publications by Andrea Dal Pozzolo, et al.

All details of the cardholders have been anonymized via a principal component analysis (PCA) transform. Instead, a total of 28 principal components of these anonymized features is provided. In addition, the time in seconds between transactions is provided, as is the purchase amount (presumably in Euros).

Each record is classified as normal (class “0”) or fraudulent (class “1” ) and the transactions are heavily skewed towards normal. Specifically, there are 492 fraudulent credit card transactions out of a total of 284,807 transactions, which is a total of about 0.172% of all transactions.