From Data to Decisions: Enhancing Crowdfunding Strategies AI-Driven and Business Intelligence Techniques

Abstract

In the ever-evolving landscape of crowdfunding, the ability to predict project success is paramount for investors and stakeholders. In this paper, we employ advanced machine learning techniques to analyze crowdfunding data and identify key predictors of project outcomes. Leveraging a comprehensive dataset sourced from Kickstarter, we preprocess the data, apply cutting-edge machine learning models, and evaluate performance using a range of metrics. Notably, the Random Forest model emerges as the top performer, achieving the highest performance across all metrics, including an impressive AUC of 99.90%, accuracy of 99.86%, precision of 99.60%, recall of 100%, and F1-Score of 99.80%. Additionally, we introduce novel Key Performance Indicators (KPIs) to provide deeper insights into crowdfunding project dynamics. Our analysis reveals actionable insights into project success factors, empowering stakeholders to make informed decisions and maximize investment returns. Through rigorous experimentation and visualization, including dashboards, we demonstrate the efficacy 0 four a pproach in predicting crowdfunding project outcomes with high accuracy and precision. This paper not only contributes to the growing body of research in crowdfunding analytics but also offers practical implications for investors and entrepreneurs navigating the dynamic crowdfunding landscape.