{"url":"/dataset/kaggle-credit-card-fraud-dataset","name":"Kaggle-Credit Card Fraud Dataset","full_name":null,"description_markdown":"The dataset contains transactions made by credit cards in September 2013 by European cardholders.  \r\nThis 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.  \r\n   \r\nIt 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-dependent cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\r\n  \r\nGiven 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.","description_withheld":null,"homepage":"https://www.kaggle.com/mlg-ulb/creditcardfraud/","introduced_date":"2021-04-01","introduced_date_note":null,"introduced_by":null,"license":{"name":"Database: Open Database, Contents: Database Contents","url":"https://opendatacommons.org/licenses/dbcl/1-0/"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Fraud Detection","url":"/task/fraud-detection","datasets_with_task":"/datasets/task/fraud-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Kaggle-Credit Card Fraud Dataset"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fraud-detection-on-kaggle-credit-card-fraud","task":"Fraud Detection","dataset_variant":"Kaggle-Credit Card Fraud Dataset","rows":2,"metrics":["AUC","Accuracy","Average Precision"],"first_row_in_archive_order":{"model":"DevNet","paper":"/paper/deep-anomaly-detection-with-deviation","metrics":{"AUC":"0.98","Average Precision":"0.69"},"code_links":[{"title":"xuhongzuo/DeepOD","url":"https://github.com/xuhongzuo/DeepOD"},{"title":"GuansongPang/deviation-network","url":"https://github.com/GuansongPang/deviation-network"},{"title":"Ryosaeba8/Anomaly_detection","url":"https://github.com/Ryosaeba8/Anomaly_detection"},{"title":"robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log","url":"https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log"},{"title":"robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs","url":"https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs"},{"title":"robeespi/Weakly-Supervised-Malware-Detection","url":"https://github.com/robeespi/Weakly-Supervised-Malware-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-kaggle-credit-card-fraud","task":"Anomaly Detection","dataset_variant":"Kaggle-Credit Card Fraud Dataset","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"DIF","paper":"/paper/deep-isolation-forest-for-anomaly-detection","metrics":{"AUC":"0.953"},"code_links":[{"title":"xuhongzuo/DeepOD","url":"https://github.com/xuhongzuo/DeepOD"},{"title":"xuhongzuo/deep-iforest","url":"https://github.com/xuhongzuo/deep-iforest"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-isolation-forest-for-anomaly-detection","title":"Deep Isolation Forest for Anomaly Detection","date":"2022-06-14","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":1,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/xbnet-an-extremely-boosted-neural-network","title":"XBNet : An Extremely Boosted Neural Network","date":"2021-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-anomaly-detection-with-deviation","title":"Deep Anomaly Detection with Deviation Networks","date":"2019-11-19","rows_on_this_dataset":1,"code_links":6,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":14,"samples_ran":1,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}