Analysis and Application of College Enrollment Data Mining Based on Decision Tree Algorithm

The decision tree algorithm-based analysis and application of college enrollment data mining are essential for streamlining admission processes, making student enrollment forecast predictions, and enhancing institutional decisionmaking. Conventional methods typically struggle with largescale, imbalanced datasets and thus compromise on suboptimal accuracy and interpretability. To mitigate these shortcomings, the present research uses a decision tree algorithm to analyze enrollment patterns and suggests augmented methods like Random Forest and XGBoost to enhance predictive outcomes. The research utilizes the “College Enrollment Demographics 2021, which includes historical admission records, demographic data, and academic performance metrics. The proposed system demonstrates improved accuracy (98.3%), precision, and recall compared to conventional models, offering better insights into enrollment trends and student retention. Novelty emerges from the integration of ensemble learning techniques, which enhance model robustness and interpretability while reducing overfitting. Performance metrics confirm the system's effectiveness in supporting data-driven enrollment strategies.