Abstract: This paper investigates the performance of machine learning algorithms in the business domain, specifically on a dataset for measuring entrepreneurial intention. Understanding entrepreneurial intention is crucial due to the significant impact entrepreneurial activity has on the economy. Machine learning, increasingly used for prediction and classification, is replacing traditional statistical approaches. The objective of this paper is to analyze the utility of decision trees in predicting entrepreneurial intention. A predictive model is developed and tested on a sample of 119 business students from Croatia. The results demonstrate that decision trees provide rules that identify specific groups within the student sample and their respective probabilities of having entrepreneurial intentions, thereby offering practical insights for identifying and supporting potential entrepreneurs.

Keywords: Machine learning, decision tree, predictive model, business data, entrepreneurship, entrepreneurial intention

Pages: 451-454