Development of a predictive Tool of Startup Success

Abstract

Startups play a crucial role in driving economic growth, innovation, and job creation. However, the uncertain future of these projects raises concerns about their success rates. Statistics indicates that approximately 90% of startups fail, highlighting the challenges they face, such as financial resources, team dynamics, and market demand. To address these challenges, machine learning and artificial intelligence have gained interest in predicting startup success. This study aims to develop a predictive model using diverse machine learning techniques to classify startups as successful or not. It explores binary and multi-class classification approaches and evaluates various algorithms to determine their effectiveness. By contributing to the understanding of success drivers and providing insights for decision-makers, investors, and entrepreneurs, this research aims to advance the startup ecosystem in Algeria. Our analysis revealed that funding plays a significant role in determining success, with the amount of funding, its timing, and duration being highly influential factors. Additionally, the country in which a startup operates also influences its chances of success. These insights contribute to understanding success drivers and provide valuable guidance for decision-makers, investors, and entrepreneurs in advancing the startup ecosystem.