audit-data
OpenML dataset with id 42931
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22045580/audit-data.arff
Upload date: 25 May 2021
Dataset Characteristics
Number of classes: 0
Number of features: 37 (numeric: 36, symbolic: 0 and in total binary: 0 )
Number of instances: 1,552
Number of instances with missing values: 1,552
Number of missing values: 19,402
Author: Nishtha Hooda, CSED, TIET, Patiala
Source: UCI - 2018
Please cite: [ https://doi.org/10.1080/08839514.2018.1451032 Hooda, Nishtha, Seema Bawa, and Prashant Singh Rana. 'Fraudulent Firm Classification: A Case Study of an External Audit.' Applied Artificial Intelligence 32.1 (2018): 48-64.]
The goal of the research is to help the auditors by building a classification model that can predict the fraudulent firm on the basis the present and historical risk factors. The information about the sectors and the counts of firms are listed respectively as Irrigation (114), Public Health (77), Buildings and Roads (82), Forest (70), Corporate (47), Animal Husbandry (95), Communication (1), Electrical (4), Land (5), Science and Technology (3), Tourism (1), Fisheries (41), Industries (37), Agriculture (200). The original dataset was separated into a trial and audit dataset. In this dataset these are concatenated into 1 dataset. Two features (trial and audit) have been added to indicate whether the data was originally from the trial or audit dataset.
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