Do You Want to Foresee Your Future? The Best Model Predicting the Success of Kickstarter Campaigns

Abstract

Many creators find crowdfunding websites one of the best ways to get assistance for their campaigns. Kickstarter, as one representative crowdfunding website, provides a great platform for their brightest dreams. However, not everyone successfully reaches their funding goals. In this paper, we figure out what machine learning model and factors can best predict success probability in a Kickstarter campaign. Through comparing 6 different machine learning models, we find that the best performing model is the Random Forest model, with robust forecast accuracy of 87.85%, which is 10% higher than existing studies. Factor importance analysis indicates that the number of backers, whether picked up by editors, and the edit time of campaign are the top three most important factors in determining the success rate of crowd-funding projects. This suggests, to launch a successful project, the number of backers, whether picked up by editors, and the edit time of campaign should be weighted more than other factors. Our research shed light on both crowd-funding project determinants and machine leaning down-stream applications.