Anticipating Success or Failure: A Comprehensive Analysis of Entrepreneurship Factors using Machine Learning Predictive Models

Abstract

The research, a pioneering effort in Morocco, explores the intricate elements driving new startups' viability using AI models. It employs a range of advanced techniques such as decision trees, random forests, logistic regression, support vector machine (SMV), ensemble techniques, and neural networks. The study uncovers unique perspectives on the complex interplay between internal variables like human capital, strategic planning, and internal bureaucracy and external factors like government support, mentorship, and competition that shape entrepreneurship performance. The findings, which reveal a dual and unexpected influence of internal bureaucracy and a multifaceted contribution of human capital, are particularly relevant in the dynamic startup landscape. Mentorship and financial resources emerge as critical contributors to startups’ success. This review, the first of its kind in Morocco, offers special insights into the factors influencing entrepreneurial success. The discoveries have the potential to revolutionize our understanding of how organizations operate in Morocco and their significant implications for enterprising undertakings, providing a practical guide for startups in the region.