The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.
It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. The feature 'pcaVector' is a Vector of the principal components obtained with PCA, the only features which have not been transformed with PCA are 'time' and 'amountRange'. Feature 'time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'amountRange' is the approximate amount of the transaction. The ranges are represented as an integer between 0 and 7 which correspond to the range (in dollars) 0-1, 1-5, 5-10, 10-20, 20-50, 50-100, 100-200 and 200+ respectively. Feature 'label' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification.
This dataset is a slightly modified version of the dataset collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on http://mlg.ulb.ac.be/BruFence and http://mlg.ulb.ac.be/ARTML
Please cite: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

