摘要：Machine Learning has undergone a tremendous progress , which is evolutionary over the last decade. It is widely used to make predictions that lead to the most valuable decisions. Many experts in economics use models derived from Machine Learning as important assistance, and many companies would use Neural Network, a model in bankruptcy prediction, as their guide to prevent potential failure. However, although Neural Networks can process a tremendous amount of attribute factors, it results in overfitting frequently when more statistics is taken in. By using K-Nearest Neighbor and Random Forest, we can obtain better results from different perspectives. This paper testifies the optimal algorithm for bankruptcy calculation by comparing the results of the two methods.
