Researchers used machine learning to create a decision tree model that predicts college graduates' readiness for entrepreneurship. They surveyed 700 students, aged 17-25, and collected valid data from 604 participants. The decision tree model, divided into pruned and unpruned versions, generated 180 and 121 decision rules, respectively. The pruned model showed high accuracy above 99% in training mode and 82.87% in split mode, while the unpruned model had accuracy of 90.18% in training mode and 80.38% in split mode. The pruned decision tree model outperformed the unpruned model.