Predicting Entrepreneurial Tendencies in University Students of Bangladesh: A Machine Learning Approach

Machine learning offers a powerful approach to predicting entrepreneurial tendencies among university students, enabling targeted measures and better decision-making. This research explores a dataset of academic, demographic, and behavioral attributes to classify students into entrepreneurial categories using nine machine learning models. Random Forest emerged as the top-performing model, achieving the highest accuracy of 83%. For better interpretability, Local Interpretable Model-agnostic Explanations (LIME) was applied, revealing key features such as gender, CGPA, study hours, and program involvement that influenced predictions. By integrating predictive accuracy with explainability, the study provides transparent insights into entrepreneurial behavior, helping educators and policymakers in fostering entrepreneurship. Future research can enhance this work by integrating additional features and applying the model to broader student populations.