Bankruptcy prediction is a crucial task in the determination of an organization’s economic condition, that is, whether it can meet its financial obligations or not. It is extensively researched because it includes a crucial impact on staff, customers, management, stockholders, bank disposition assessments, and profitableness. In recent years, Artificial Intelligence and Machine Learning techniques have been widely studied for bankruptcy prediction and Decision-making problems. When it comes to Machine Learning, Artificial Neural Networks perform really well and are extensively used for bankruptcy prediction since they have proven to be a good predictor in financial applications. various machine learning models are integrated into one called the ensemble technique. It lessens the bias and variance of the ml model. This improves prediction power. The proposed model operated on quantitative and qualitative datasets. This ensemble model finds key ratios and factors of Bankruptcy prediction. LR, decision tree, and Naive Bayes models were compared with the proposed model’s results. Model performance was evaluated on the validation set. Accuracy was taken as a metric for the model’s performance evaluation purpose. Logistic Regression has given 100% accuracy on the Qualitative Bankruptcy Data Set dataset, resulting in the Ensemble model also performing well.
J. Arumugam, S. Raja Sekar, V. Prasanna Venkatesan
Pondicherry University, India
Machine Learning, Ensemble Model, Bankruptcy Prediction, Qualitative Bankruptcy Data, Ensemble Blending
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 14 , Issue: 1 , Pages: 3147 - 3153 )
Date of Publication :
October 2023
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