Beyond failure rates: unveiling startup success factors with machine learning

Startups are crucial in driving economic growth, innovation, and job creation. However, the uncertain future of these projects raises concerns about their success rates. Statistics indicate that approximately 90% of startups fail, highlighting challenges such as financial resources, team dynamics, and market demand. To address these challenges, Machine Learning (ML) has gained interest in predicting startup success by analyzing key features. This study aims to develop a predictive model to classify startups as successful. It explores binary and multi-class classification approaches and evaluates various algorithms to determine their effectiveness. This research aims to advance the startup ecosystem by contributing to understanding success drivers and providing insights for decision-makers. A key finding from our analysis is the significant role funding plays in predicting startup success. Specifically, the total amount of funding received by startups emerged as the most essential feature in our models. Furthermore, we found that variables related to the timing and duration of funding were also influential predictors. The time to receive the first funding, the funding lifecycle, the time between the first and the last financing, and the number of funding rounds showed noteworthy associations with the target variable. In Addition, we observed that the country in which a startup operates plays a role in its chances of success, possibly due to varying levels of support, resources, or market dynamics across different countries. Furthermore, the risks in startup operations, such as market demand and competition, are heavily influenced by the specific industry a startup enters.