A Contrastive Study of Machine Learning on Funding Evaluation Prediction

Winning a granting is critical in helping young and innovative firms to reduce financial burden, yet successfully get funding is not easy. Granting applicants are eager to find out the funding evaluator's decision pattern and fully prepare for the fund application. In such condition, supervised machine learning models seem to be a suitable tool. Based on nearly 5000 Beijing Innofund applicants, we find that supervised machine learning models, like support vector machines (SVM), K-nearest neighbors (KNN), decision tree, logistic regression, and Artificial Neutral Network (ANN) can produce both accurate and reasonably understandable funding prediction results with their average accuracy rate over 80%. Yet, the comparison results also reveal that the SVM model produces the most accurate forecasts in terms of average accuracy rate (86%) and F-score (82%). The findings indicate that SVM is an effective and reliable classification algorithm that can perform tasks well with small datasize. Based on the selected attributes and their weights, the funding applicants can get ready for the grants, by making up for the disadvantages and enhancing the advantages.