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 Articles Article Faculty Publications -Articles 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 Machine Learning Classifiers for Credit Risk Analysis Subject The topic of the resource Credit risk; Decision tree; K-Nearest Neighbour; Machine learning; Naive Bayes classifier Description An account of the resource The modern world is a place of global commerce. Since globalization became popular, entrepreneurs of small and medium enterprises to large ones have looked up to banks, which have existed in various forms since antiquity, as their pillars of support. The risk of granting loans in various forms has significantly increased as a consequence of this, the businesses face financing difficulties. Credit Risk Analysis is a major aspect of approving the loan application that is done by analyzing different types of data. The goal is to minimize the risk of approving the loan for the Individuals or businesses who might not pay back on time. This research paper addresses this challenge by applying various machine learning classifiers to the German credit risk dataset. By evaluating and comparing the accuracy of these models to identify the most effective classifier for credit risk analysis. Furthermore, it proposes a contributory approach that combines the strengths of multiple classifiers to enhance the decision-making process for loan approvals. By leveraging ensemble learning techniques, such as the Voting Ensemble model, the aim is to improve the accuracy and reliability of credit risk analysis. Additionally, it explores tailored feature engineering techniques that focus on selecting and engineering informative features specific to credit risk analysis. 2024 Sudiksha et al., licensed to EAI. Creator An entity primarily responsible for making the resource Sudiksha; Nanjundan P.; George J.P. Source A related resource from which the described resource is derived EAI Endorsed Transactions on Internet of Things, Vol-10 Publisher An entity responsible for making the resource available European Alliance for Innovation Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4108/eetiot.5376" target="_blank" rel="noreferrer noopener">https://doi.org/10.4108/eetiot.5376</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187884959&amp;doi=10.4108%2Feetiot.5376&amp;partnerID=40&amp;md5=b342327412a0caadb68881e3df17b106" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187884959&amp;doi=10.4108%2feetiot.5376&amp;partnerID=40&amp;md5=b342327412a0caadb68881e3df17b106</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 24141399 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sudiksha, Department of Data Science, Christ University, Pune Lavasa Campus, India; Nanjundan P., Department of Data Science, Christ University, Pune Lavasa Campus, India; George J.P., Department of Data Science, Christ University, Pune Lavasa Campus, India