Comparison of Different Machine Learning Algorithms for Detecting Bankruptcy

Abstract

There has 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 research 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 include kfold cross-validation (k=10) to measure our accuracy. Bagging accuracy is high in this paper. Confusion matrix is used to recount the perfection of a classification model that gives true values for knowing.