Examining The Impact Of Entrepreneurial Characteristics On Startup Success Using Machine Learning Techniques

Abstract

The process of foretelling a startup's success or failure is known as startup prediction, involving the forecast of a company's trajectory. Successful prediction requires a blend of analytical expertise, industry knowhow, and a dash of intuition to decipher signals indicating a venture's potential for sustained growth and innovation. The task is formidable due to the multitude of factors influencing a startup's fate, which can swiftly change in the dynamic startup ecosystem. Despite these challenges, the growing availability of data has propelled the popularity of data-driven approaches, including machine learning, for startup prediction. This research delves into the impact of entrepreneurial attributes on predicting startup success. Entrepreneurial attributes encompass the personal traits and skills inherent to entrepreneurs. Specifically, the study examines the influence of certain entrepreneurial attributes—age, gender, education, and network—on startup success prediction. Crunchbase, a platform offering insights into startup companies, venture capital firms, and private equity firms, provides the data for this study. Various machine learning classification techniques are employed for analysis and compared for performance. The outcomes aim to furnish entrepreneurs, investors, and policymakers with valuable insights on identifying and cultivating attributes crucial for startup success. Furthermore, this study contributes to the existing literature on startup prediction by underscoring the significance of entrepreneurs' personal characteristics in the prediction process.