DSpace Repository

Comparison of Different Machine Learning Algorithm for Detecting Bankruptcy

Show simple item record

dc.contributor.author Keya, Maria Sultana
dc.contributor.author Akter, Himu
dc.contributor.author Sozib, Md. Atiqur Rahman
dc.date.accessioned 2021-09-01T07:12:09Z
dc.date.available 2021-09-01T07:12:09Z
dc.date.issued 2021-01-15
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/6062
dc.description.abstract There have been severe experiments from academics and merchandisers concerning models for Predicting bankruptcy. The paper propounds an extensive rethink of work done during 5 years in the petition of intellectual strategy to accomplish bankruptcy prediction problems. Several machine learning directions are being used in this paper for Predicting bankruptcy. Some algorithms: AdaBoost, Decision tree, J48, Bagging, Random Forest are used in this paper. By traditional models, machine learning models offer enhancing bankruptcy prediction accuracy. Different types of models are tested using several evaluation metrics. The five years Bagging accuracy range is 95% within 97% among another model. Here we include k-fold cross-validation(k=10) to measure our accuracy. Bagging accuracy is high in this paper. A confusion matrix is used to recount the perfection of a classification model that gives true values for knowing. en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Machine learning en_US
dc.subject Bankruptcy en_US
dc.title Comparison of Different Machine Learning Algorithm for Detecting Bankruptcy en_US
dc.type Other en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


My Account