  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.
