Prediction and Optimization of School-Enterprise Cooperation Projects Based on Decision Tree Algorithm

Abstract

This study investigates the prediction and optimization of school-enterprise cooperation projects through the application of the decision tree algorithm. Collaborative endeavours between academic institutions and industry partners represent a critical pathway for fostering innovation, talent development, and knowledge transfer. However, the success of these projects hinges on effective decision-making, resource allocation, and risk management. Leveraging advanced data-driven methodologies, our study aims to enhance project outcomes and streamline decision-making processes in the context of school-enterprise cooperation. Through a comprehensive review of the literature and empirical studies, we elucidate the key determinants influencing the success of cooperation projects and highlight the potential of decision tree modelling in this domain. The decision tree algorithm offers a systematic approach to analyze complex datasets, identify patterns, and generate actionable insights, thus facilitating predictive modelling and optimization efforts. The methodology involves collecting and preprocessing relevant project data, selecting influential features, constructing decision tree models, and evaluating their performance using metrics such as accuracy, precision, recall, and F1-score. Sensitivity analysis and scenario analysis further explore the robustness and practical implications of decision tree modelling in optimizing resource allocation, risk mitigation, and stakeholder engagement strategies. Our statistical results demonstrate the efficacy of decision tree modelling in predicting project outcomes with high accuracy and precision. The models exhibit robustness to variations in key parameters and offer valuable insights into the underlying dynamics of school-enterprise cooperation projects. By empowering stakeholders with actionable insights and facilitating informed decision-making processes, decision tree modelling holds significant promise as a transformative tool for enhancing the management and outcomes of collaborative initiatives.