Abstract
As an emerging internet financing model with high efficiency, low cost, diversified returns, and a small investment, crowdfunding is sought after by entrepreneurs and investors. However, many crowdfunding projects are faced with the risk of low success rates and failure to reach the financing target within the specified period. Therefore, the prediction of crowdfunding project financing results and multi-model comparison are important ways to improve the project success rates and reduce market risk. First, we collected project data of JingDong (JD) crowdfunding platform for preprocessing and analyzed the characteristics of successful projects. Then, we use ensemble learning and traditional machine learning models to predict the daily amount of crowdfunding with grid search to obtain the optimal hyperparameters of each model. Several evaluation metrics are then employed to assess the performance of the model.The experimental results demonstrate that the Extra Tree Regression (ETR) ensemble model achieves the best prediction performance, with a coefficient of determination(\(R^{2}\)) of 90.1%, when forecasting the daily crowdfunding fundraising. Furthermore, the ensemble learning model showed significant advantages in other evaluation indicators, indicating its potential in forecasting the financing amount of crowdfunding projects.
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This work was supported by Natural Science Foundation of Guangdong Province, China (2020A1515010761).
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Min, H., Wu, K., Tan, M., Lin, J., Zheng, Y., Zhan, C. (2022). Ensemble Learning for Crowdfunding Dynamics: JingDong Crowdfunding Projects. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_28
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DOI: https://doi.org/10.1007/978-981-19-6135-9_28
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