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.