# Context
Fraud detection in financial transactions is one of the most important problems in financial companies.
We have already had a competition on fraud detection at [the 5th 1056Lab Data Analytics Competition](https://www.kaggle.com/c/1056lab-flaud-detect) which is the first our competition at Kaggle in 2018.

# Content
The original dataset is in [Kaggle Datasets](https://www.kaggle.com/mlg-ulb/creditcardfraud).

This data is about fraud detection in credit card transactions. The data was made by credit cards in September 2013 by European cardholders.  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. The original features and more background information about the data is not provided.

The dataset contains 284,807 instances, 492 instances are fraudulent, the remaining 284,315 instances are genuine.


# Training Dat and Test Data
The training data set is the former 198,365 (70%) instances, 383 represents fraud transactions, 197,982 transactions are genuine.

The test data is the later 86,442 (30%) transactions.


# License
The original data is licensed by [Open Database License (ODbL) 1.0](https://opendatacommons.org/licenses/dbcl/1.0/).


# Notice
This is a private competition in [1056Lab](https://1056lab.org/), a data mining laboratory at [Chubu University](https://www3.chubu.ac.jp/main/english/).

#Reference
- [Machine Learning Group](http://mlg.ulb.ac.be) - ULB: [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud), Kaggle Datasets
- Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. [Calibrating Probability with Undersampling for Unbalanced Classification](https://www.researchgate.net/publication/283349138_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](https://www.researchgate.net/publication/260837261_Learned_lessons_in_credit_card_fraud_detection_from_a_practitioner_perspective), Expert systems with applications,41,10,4915-4928,2014, Pergamon
- Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. [Credit card fraud detection: a realistic modeling and a novel learning strategy](https://www.researchgate.net/publication/319867396_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. [Adaptive Machine learning for credit card fraud detection](http://di.ulb.ac.be/map/adalpozz/pdf/Dalpozzolo2015PhD.pdf), ULB MLG PhD thesis (supervised by G. Bontempi)
- 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](https://www.researchgate.net/publication/319616537_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; Bontempi, Gianluca. [Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), International Journal of Data Science and Analytics, 5,4,285-300,2018, Springer International Publishing
- Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi. [Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection](https://www.researchgate.net/publication/332180999_Deep-Learning_Domain_Adaptation_Techniques_for_Credit_Cards_Fraud_Detection), INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019
- Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi. [Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection](https://www.researchgate.net/publication/333143698_Combining_Unsupervised_and_Supervised_Learning_in_Credit_Card_Fraud_Detection), Information Sciences, 2019