This study explores the many factors that contribute to the success of startups, examining a wide range of elements that can influence their outcomes. By analyzing over 4,000 cases, the research uses advanced statistical and machine learning techniques like Decision Trees, Random Forests, and Gradient Boosting to assess how well different startup traits can predict success. These traits include the industry, location, how long the company has been around, the number of previous ventures the founders have launched, and their educational backgrounds. The results reveal important predictors of success, shedding light on how these factors shape outcomes. With highly accurate predictive models, the study provides valuable insights for those involved in the startup world, offering a solid foundation for making informed decisions. Beyond contributing to the academic conversation on startup success, this research gives entrepreneurs and stakeholders practical, data-driven strategies to navigate the competitive startup environment.
Nadimpalli et al. (Fri,) studied this question.