Data
CreditCardFraudDetection

CreditCardFraudDetection

active ARFF Publicly available Visibility: public Uploaded 14-10-2019 by Andreas Mueller
0 likes downloaded by 3 people , 8 total downloads 0 issues 0 downvotes
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
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)numeric2 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

62 properties

284807
Number of instances (rows) of the dataset.
31
Number of attributes (columns) of the dataset.
0
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.
31
Number of numeric attributes.
0
Number of nominal attributes.
153.16
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
3061.36
Mean of means among attributes of the numeric type.
-2.24
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.73
First quartile of standard deviation of attributes of the numeric type.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
26.62
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.73
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
Percentage of instances belonging to the most frequent class.
1540.83
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.26
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-1.29
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
0.96
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
933.4
Maximum kurtosis among attributes of the numeric type.
-0
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
220.59
Third quartile of kurtosis among attributes of the numeric type.
94813.86
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.
0
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-8.52
Minimum skewness among attributes of the numeric type.
0
Percentage of nominal attributes.
0.68
Third quartile of skewness among attributes of the numeric type.
24
Maximum skewness among attributes of the numeric type.
0.04
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
1.33
Third quartile of standard deviation of attributes of the numeric type.
47488.15
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
2.58
First quartile of kurtosis among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
-0
First quartile of means among attributes of the numeric type.

0 tasks

Define a new task