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CreditCardFraudDetection

CreditCardFraudDetection

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Source: Unknown - Date unknown 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 Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content 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. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Inspiration Identify fraudulent credit card transactions. 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. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universite Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: 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 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; 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 Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Ael; 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-Ael; 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 Bertrand Lebichot, Yann-Ael Le Borgne, Liyun He, Frederic Oble, Gianluca Bontempi 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-Ael Le Borgne, Olivier Caelen, Frederic Oble, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019

31 features

Class (target)nominal2 unique values
0 missing
Timenumeric124592 unique values
0 missing
V1numeric275663 unique values
0 missing
V2numeric275663 unique values
0 missing
V3numeric275663 unique values
0 missing
V4numeric275663 unique values
0 missing
V5numeric275663 unique values
0 missing
V6numeric275663 unique values
0 missing
V7numeric275663 unique values
0 missing
V8numeric275663 unique values
0 missing
V9numeric275663 unique values
0 missing
V10numeric275663 unique values
0 missing
V11numeric275663 unique values
0 missing
V12numeric275663 unique values
0 missing
V13numeric275663 unique values
0 missing
V14numeric275663 unique values
0 missing
V15numeric275663 unique values
0 missing
V16numeric275663 unique values
0 missing
V17numeric275663 unique values
0 missing
V18numeric275663 unique values
0 missing
V19numeric275663 unique values
0 missing
V20numeric275663 unique values
0 missing
V21numeric275663 unique values
0 missing
V22numeric275663 unique values
0 missing
V23numeric275663 unique values
0 missing
V24numeric275663 unique values
0 missing
V25numeric275663 unique values
0 missing
V26numeric275663 unique values
0 missing
V27numeric275663 unique values
0 missing
V28numeric275663 unique values
0 missing
Amountnumeric32767 unique values
0 missing

19 properties

284807
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
30
Number of numeric attributes.
1
Number of nominal attributes.
3.23
Percentage of binary attributes.
0
Percentage of instances having missing values.
1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
96.77
Percentage of numeric attributes.
99.83
Percentage of instances belonging to the most frequent class.
3.23
Percentage of nominal attributes.
284315
Number of instances belonging to the most frequent class.
0.17
Percentage of instances belonging to the least frequent class.
492
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

9 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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