Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ajith, Shivani; Joshi, Hemlata Title A name given to the resource A study of failure prediction of Indian banks using various machine learning algorithms - An examination of predictive accuracy Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived AIP Conference Proceedings;Volume;3191;Issue;1;Article No.;40007; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1063/5.0248387" target="_blank" rel="noreferrer noopener">https://doi.org/10.1063/5.0248387</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218678853?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218678853?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ajith S., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India; Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource Banks play a key role in strengthening the economy; hence their survival is very important. It is necessary to evaluate the failure probability of banks correctly based on the factors associated with it. Over half of the assets in the financial sector in India are held by the banking sector, which holds a strong position. Phased implementation of financial sector reforms has resulted in an exciting moment of rapid transformation for Indian banks. The study here focuses on the establishment of machine learning approach to compute and compare the extent of bankruptcy based on the accuracy measure-Support Vector Machine classification, Random Forest, Logistic Regression, Nae Bayes classification using the data of 250 Indian banks having qualitative variables from 2015 to 2020. The feature selection in this paper is based on correlation and relief algorithms. The explanatory features of the dataset are drawn by implementing a two-step feature selection technique and the selected features are fed and further used for prediction using the Random Forest technique, Logistic Regression, Support Vector Machine, and Nae Bayes classification techniques. The results reveal, that the support vector machine shows a score of 99.8% forecasting the highest accuracy. This research serves as a foundation for the decisions made by a variety of stakeholders, including analysts, policymakers, shareholders, and bank management, and it facilitates the comparison of the qualitative ratios of bankruptcy. The goal is to develop a prediction system that will allow the firms and businesses to be categorized according to the level of risk. 2025 Author(s). Subject The topic of the resource Bankruptcy; Feature selection; Logistic Regression; Machine learning; Support Vector Machines Publisher An entity responsible for making the resource available American Institute of Physics Relation A related resource ISSN: 0094243X; ISBN: 978-073545110-0; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online