{"url":"/dataset/vehicle-claims","name":"Vehicle Claims","full_name":null,"description_markdown":"The code to create the dataset is available [here](https://github.com/ajaychawda58/UADAD/blob/main/Code/Notebooks/create_dataset.ipynb).\r\nThe dataset used in the paper is available on [github](https://github.com/ajaychawda58/UADAD/tree/main/data/vehicle_claims)\r\n\r\n- `Maker` - *Categorical* - The brand of the vehicle.\r\n- `GenModel` - *Categorical* - The model of the vehicle.\r\n- `Color` - *Categorical* - Colour of the vehicle.\r\n- `Reg_Year` - *Categorical* - Year of Registration.\r\n- `Body_Type` - *Categorical* - Eg. SUV, Convertible.\r\n- `Runned_Miles` - *Numerical* - Distance covered by the vehicle.\r\n- `Engin_Size` - *Categorical* - Size of engine.\r\n- `GearBox` - *Categorical* - Automatic, Manual.\r\n- `FuelType` - *Categorical* - Petrol, Diesel.\r\n- `Price` -  *Numerical* - Price of vehicle.\r\n- `Seat_num` - *Numerical* - Number of seats.\r\n- `Door_num` -  *Numerical* - Number of Doors.\r\n- `issue` - *Categorical* - Type of damage.\r\n- `issue_id` - *Categorical* - Specific damage.\r\n- `repair_complexity` - *Categorical* - Difficulty to repair the vehicle.\r\n- `repair_hours` -  *Numerical* - Time required to finish the job.\r\n- `repair_cost` - *Numerical* - Cost of repair.\r\n\r\nOther attributes are not used for evaluation in this work. \r\n`breakdown_date` and `repair_date` were added with the idea of inserting anomalies based on the number of days required to repair the vehicle.","description_withheld":null,"homepage":"","introduced_date":"2022-10-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/unsupervised-anomaly-detection-for-auditing","title":"Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings","first_author":"Ajay Chawda","url":null},"license":null,"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Fraud Detection","url":"/task/fraud-detection","datasets_with_task":"/datasets/task/fraud-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Vehicle Claims"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-vehicle","task":"Unsupervised Anomaly Detection","dataset_variant":"Vehicle Claims","rows":9,"metrics":["AUC"],"first_row_in_archive_order":{"model":"SOM","paper":"/paper/unsupervised-anomaly-detection-for-auditing","metrics":{"AUC":"65.43"},"code_links":[{"title":"ajaychawda58/uadad","url":"https://github.com/ajaychawda58/uadad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-vehicle-claims","task":"Anomaly Detection","dataset_variant":"Vehicle Claims","rows":2,"metrics":["AUC"],"first_row_in_archive_order":{"model":"Random Forest","paper":"/paper/unsupervised-anomaly-detection-for-auditing","metrics":{"AUC":"98.65"},"code_links":[{"title":"ajaychawda58/uadad","url":"https://github.com/ajaychawda58/uadad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unsupervised-anomaly-detection-for-auditing","title":"Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings","date":"2022-10-25","rows_on_this_dataset":11,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}