Explore the Datasetįirst, download and unzip the dataset and save it in your current working directory with the name “ creditcard.csv“. Next, let’s take a closer look at the data. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Some publications use the ROC area under curve metric, although the website for the dataset recommends using the precision-recall area under curve metric, given the severe class imbalance. Calibrating Probability with Undersampling for Unbalanced Classification, 2015. The dataset is highly unbalanced, where the positive class (frauds) account for 0.172% of all transactions … It contains a subset of online transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. Specifically, there are 492 fraudulent credit card transactions out of a total of 284,807 transactions, which is a total of about 0.172% of all transactions. In addition, the time in seconds between transactions is provided, as is the purchase amount (presumably in Euros).Įach record is classified as normal (class “0”) or fraudulent (class “1” ) and the transactions are heavily skewed towards normal. Instead, a total of 28 principal components of these anonymized features is provided. ![]() The dataset is credited to the Machine Learning Group at the Free University of Brussels (Université Libre de Bruxelles) and a suite of publications by Andrea Dal Pozzolo, et al.Īll details of the cardholders have been anonymized via a principal component analysis (PCA) transform. The data represents credit card transactions that occurred over two days in September 2013 by European cardholders. In this project, we will use a standard imbalanced machine learning dataset referred to as the “ Credit Card Fraud Detection” dataset. This tutorial is divided into five parts they are: Photo by Andrea Schaffer, some rights reserved. How to Predict the Probability of Fraudulent Credit Card Transactions
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