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Credit card fraud data

This directory contains a credit card purchase dataset with anonymized parameters. 48 hours of purchases by European cardholders are recorded. There are 284,807 purchases in total, and 492 of them are fraudulent.

Fields in creditcard.csv

The creditcard.csv file contains 31 columns of data. These columns are:

  • Time - The integer number of seconds after the first purchase in the file.
  • V1 - V28 - The 28 anonymized PCA transformed purchase parameters.
  • Amount - The purchase amount in Euros.
  • Class - A flag indicating a valid (0) or fraudulent (1) purchase.

Source and license

This data was obtained from Kaggle. It is provided here for demonstrative use without any warranty as to the accuracy, reliability, or completeness of the data. Credit for the dataset goes to:

Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Oblé, Frederic; and Bontempi, Gianluca. Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing

Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE

Dal Pozzolo, Andrea; Caelen, Olivier; Johnson, Reid A.; and Bontempi, Gianluca. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015

Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon

Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)

Lebichot, Bertrand; Le Borgne, Yann-Aël; He, Liyun; Oblé, Frederic; and Bontempi, Gianluca. Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019

Le Borgne, Yann-Aël; and Bontempi, Gianluca. Machine Learning for Credit Card Fraud Detection - Practical Handbook