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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objectives<\/jats:title><jats:p>This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The experiment results show that feature\u2010selection\u2010based machine learning models are effective and beneficial in crowdfunding prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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\u2010score was improved by 18.48%, and the AUC was improved by 14.9%.<\/jats:p><\/jats:sec>","DOI":"10.1111\/exsy.13646","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T23:58:54Z","timestamp":1719532734000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Crowdfunding performance prediction using feature\u2010selection\u2010based machine learning models"],"prefix":"10.1111","volume":"41","author":[{"given":"Yuanyue","family":"Feng","sequence":"first","affiliation":[{"name":"College of Management Shenzhen University  Shenzhen China"}]},{"given":"Yuhong","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University  Shenzhen China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6835-4433","authenticated-orcid":false,"given":"Nianjiao","family":"Peng","sequence":"additional","affiliation":[{"name":"School of International Education Guangdong University of Finance  Guangzhou China"}]},{"given":"Ben","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University  Shenzhen China"}]}],"member":"311","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"crossref","unstructured":"Ahmad F. 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