From Concept to Market: Ensemble Predictive Model for Research Project Crowdfunding Readiness - Andreea Cristina Ionica; Stanislav Cseminschi; Monica Leba (Systems)
This study introduces an ensemble model that integrates random forest, gradient boosting, and logistic regression to predict the success of crowdfunding campaigns. Our research develops a novel set of metrics that assess the developmental stage of research projects, facilitating the transition from concept to market-ready product. Utilizing data from multiple sources, including Kaggle’s dataset of Kickstarter and Indiegogo projects and a proprietary dataset tailored to our study, the model’s performance was evaluated against traditional implementations of random forest and gradient boosting. The results demonstrate the ensemble model’s superior performance, achieving an accuracy of 98.94% and an F1 score of 98.81%, significantly outperforming the individual models, showing the best accuracies of around 91% for random forest and lower scores for gradient boosting. This enhancement in predictive power allows for optimized resource allocation and strategic planning in project development, thereby increasing the likelihood of crowdfunding success. This approach streamlines the process of bringing innovative ideas to final products, while at the same time offering a methodologically advanced tool for stakeholders to enhance their campaign strategies effectively.