Integrating Multiple Machine Learning Algorithms for Bankruptcy Prediction: The Role of Voting Classifiers in Enhancing Predictive Performance

In financial management, it is an important chore to find businesses that might go bankrupt and so enable preventative measures to avert a financial crisis. In this work, the authors build an innovative model for bankruptcy prediction using a voting classifier, one of the ensemble learning techniques combining traits of several machine learning algorithms for superior predictive performance. Combining logistic regression, decision trees, and support vector machines in the approach generates a more robust prediction. The authors obtained an accuracy of $\mathbf{9 7 \%}$ with the voting classifier after rigorous training and testing on dataset having multiple financial variables. This tremendous degree of accuracy is meant to show how well ensemble methods capture complex patterns and connections inside financial data. The results reveal that the voting classifier not only raises the accuracy in prediction but also offers a dependable method through which likely bankruptcy may be found at an early stage, thereby enabling rapid and smart actions on the part of financial stakeholders.