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Credit Card Fraud Detection
External Dataset
External Data Source
55 (lowest rank is 55)

Category & Restrictions

cyber crime


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, the original features and more background information about the data are not provided. The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group of ULB (Universit Libre de Bruxelles) on big data mining and fraud detection.    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.

Additional Details

ripper, transaction processing, data mining, inferlink corporation, universit libre de bruxelles, machine learning, dimension reduction, universities in belgium, external data source, 1276, engineering universities and colleges in belgium, credit card fraud, big data, credit card fraud detection, principal component analysis, universities and colleges in brussels