creditcard.csv

About DatasetDataset link - https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?select=creditcard.csvContextIt 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.ContentThe dataset contains transactions made by credit cards in September 2013 by European cardholders.<br>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-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.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.Update (03/05/2021)A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book.AcknowledgementsThe dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.<br>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 projectPlease 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, 2015Dal 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, PergamonDal 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,IEEEDal 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-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,ElsevierCarcillo, Fabrizio; Le Borgne, Yann-Aël; 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 PublishingBertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, 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, 2019Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical HandbookBertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, IInternational Journal of Data Science and Analytics

关于数据集
数据集链接：https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?select=creditcard.csv

背景
信用卡公司能够识别欺诈性信用卡交易，避免顾客为未购买的商品支付费用，这一点至关重要。

数据集内容
本数据集收录了2013年9月欧洲信用卡持卡人的信用卡交易记录。本次数据集涵盖两天内的全部交易，共计284807笔，其中欺诈交易492笔。该数据集存在严重的类别不平衡问题：正类（欺诈交易）仅占全部交易的0.172%。
本数据集仅包含经过主成分分析（PCA, Principal Component Analysis）转换后的数值型输入变量。受限于数据保密协议，我们无法披露原始特征及更多数据集背景信息。特征V1、V2……V28均为通过PCA得到的主成分分量，仅'Time'与'Amount'两项特征未经过PCA转换。特征'Time'表示每笔交易与数据集中首笔交易之间的秒数差；特征'Amount'为交易金额，该特征可用于示例依赖的代价敏感学习。特征'Class'为响应变量，当交易为欺诈时取值为1，正常交易则取值为0。
鉴于类别不平衡的比例特性，我们建议使用精确率-召回率曲线下面积（AUPRC, Area Under the Precision-Recall Curve）作为模型评估指标，混淆矩阵准确率在不平衡分类任务中并无实际参考意义。

更新（2021年3月5日）
针对信用卡欺诈检测的机器学习实践手册已同步推出交易数据模拟器，相关链接：https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html。我们诚挚邀请所有关注欺诈检测数据集的从业者，一并查阅该数据模拟器及手册中介绍的信用卡欺诈检测方法论。

致谢
本数据集由Worldline与布鲁塞尔自由大学（ULB, Université Libre de Bruxelles）机器学习组（MLG, Machine Learning Group，http://mlg.ulb.ac.be）在大数据挖掘与欺诈检测的合作研究中收集并整理。更多相关项目的过往与当前研究细节，可访问https://www.researchgate.net/project/Fraud-detection-5与DefeatFraud项目页面查阅。

请引用以下文献：
1. 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
2. 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
3. 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
4. Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)
5. 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, Information fusion,41, 182-194,2018,Elsevier
6. Carcillo, Fabrizio; Le Borgne, Yann-Aël; 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
7. Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, 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
8. Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019
9. Yann-Aël Le Borgne, Gianluca Bontempi Reproducible machine Learning for Credit Card Fraud Detection - Practical Handbook
10. Bertrand Lebichot, Gianmarco Paldino, Wissam Siblini, Liyun He, Frederic Oblé, Gianluca Bontempi Incremental learning strategies for credit cards fraud detection, International Journal of Data Science and Analytics

搜集汇总

背景与挑战

背景概述

该数据集包含欧洲持卡人在2013年9月的信用卡交易记录，共284,807笔交易，其中492笔为欺诈交易，数据极度不平衡。特征包括PCA转换后的V1-V28、交易时间和金额，目标变量为'Class'，适用于欺诈检测模型开发。