A Framework Based SVM for Bankruptcy Prediction

Bankruptcy prediction is a field within finance and business that aims to assess the likelihood of industrial companies facing financial distress. The impact of bankruptcy can lead to colossal losses for the company, employees, consumers and the entire country. Various methods and models are used to analyze financial data and other relevant information to make predictions. Indeed, this study aims to utilize a framework that makes it possible to analyze unbalanced. After this first treatment, the proposed framework invests the performance of support vector machines as a machine learning algorithm for prediction tasks. The performance of the proposed framework is compared with other techniques such as Random Forest (RF), Decision Tree (DT) and K-nearest Neighbors (KNN).