![]() These ambitious, often inexperienced companies flock to South China in search of affordable ways to turn their designs into reality. The city, which prides itself on humble beginnings as a small fishing village, has grown into a hardware hub for millions of international start-ups. ![]() The World’s Manufacturing BaseĪs China’s leading electronics manufacturing hub, Shenzhen has used its favorable location and influx of creative spirit from other provinces to take its reputation a step further. These overwhelmingly positive results have inspired others to take to the ‘web crowd’ for support. If a project is well received, one may be fully funded within a matter of weeks and can trade in financial woes for the (enjoyable) hassle of processing pre-orders.Ĭrowdfunding platforms have given rise to some of today’s most prominent tech players: the companies behind Oculus Rift, Smart Things, Pebble and more. The platforms have captivated public interest by confirming what we’ve always hoped: that an idea can change the world.īy returning to the source of ideas – people – these online forums collect opinions about new inventions from end consumers. These are the questions frequently asked on the world’s two most popular crowdfunding websites, Kickstarter (KS) and Indiegogo (IGG). Furthermore, the ensemble learning model showed significant advantages in other evaluation indicators, indicating its potential in forecasting the financing amount of crowdfunding projects. © 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.How much are you willing to invest to bring a really cool idea to life? What if it was a personal underwater exploration device? How about a hologram tablet? A robotic butler? 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. 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. First, we collected project data of JingDong (JD) crowdfunding platform for preprocessing and analyzed the characteristics of successful projects. 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. However, many crowdfunding projects are faced with the risk of low success rates and failure to reach the financing target within the specified period. 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.
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