Crowdfunding performance prediction using feature-selection-based machine learning models

Background

Crowdfunding is increasingly favoured by entrepreneurs for online financing. Predicting crowdfunding success can provide valuable guidance for stakeholders. It is a new attempt to evaluate the relative performance of different machine learning algorithms for crowdfunding prediction.

Objectives

This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction.

Method

We crawled data from MoDian.com, a Chinese crowdfunding platform, and predicted the crowdfunding performance using four machine learning algorithms, which is a new exploration in this area. Most of the existing literature focuses on empirical analysis. This work solves the problem of predicting crowdfunding performance using a dataset with a minimal number of highly contributive features, which has higher accuracy compared to the regression analysis.

Results

The experiment results show that feature-selection-based machine learning models are effective and beneficial in crowdfunding prediction.

Conclusion

Feature selection can significantly improve the prediction performance of the machine learning models. KNN achieved the best prediction results with five features: number of backers, target amount, number of project likes, number of project comments, and sponsor fans. The prediction accuracy was improved by 16%, the precision was improved by 13.23%, the recall was improved by 22.66%, the F-score was improved by 18.48%, and the AUC was improved by 14.9%.