Unveiling Crowdfunding Futures: Analyzing Campaign Outcomes through Distributed Models and Big Data Perspectives

Abstract

Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often involving advanced studies on past campaigns to extract insights that aiding companies in optimizing their crowdfunding project types and launch methodologies. Such analyses are often beyond the scope of basic data analysis techniques and frequently demand advanced machine learning tools, such as distributed computing, due to the large volume of data involved. This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures. By harnessing the power of distributed computing, it unravels intricate patterns and trends within crowdfunding data, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors. Through this multifaceted approach, a deeper understanding of the economic dynamics underlying crowdfunding ecosystems can be attained, fostering informed decision-making and sustainable growth within the startup landscape.