Machine Learning based efficient Bankruptcy Prediction Model

Bankruptcy is a severe financial problem for companies that can lead to significant losses for stakeholders, including employees, investors, and creditors. Early bankruptcy prediction is crucial to reduce default risk and avert financial losses. Using the Taiwan Company Bankruptcy Dataset (1999-2009), we investigate the challenge of making an accurate prediction of company failure in this research article. The dataset contains financial ratios and other company characteristics, such as liquidity ratios, solvency ratios, and profitability ratios. In order to predict the likelihood of bankruptcy, this article examines the application of several machine learning techniques, such as SVM, Naive Bayes, Decision Tree and Logistic Regression. This project's primary objective is to develop an efficient and accurate bankruptcy prediction model that financial institutions can use to identify companies that are likely to default on their financial obligations.