一种基于机器学习的决策支持系统，用于预测菲律宾某大学本科新生入学人数
A Machine Learning Based DSS in Predicting Undergraduate Freshmen Enrolment in a Philippine University
AI总结：
本文针对菲律宾私立高校新生入学预测难题，基于招生数据构建逻辑回归预测模型，并开发了包含数据可视化和个体预测功能的决策支持系统。
摘要：
菲律宾教育格局的突然变化，包括K至12项目的实施，使高等教育机构在吸引新生申请者方面一直面临困难，同时也难以预测即将入学的学生人数。私立高等教育机构的招生目标直接影响其成功因素。本研究回顾了影响申请者在菲律宾某大学录取状态的新生申请者的各种特征。所使用的数据集来自该大学招生办公室，通过向所有潜在申请者分发的在线表格获取。利用逻辑回归，开发了一个预测模型，以根据学生和机构的特征确定已录取学生是否会寻求在该机构入学的可能性。逻辑回归模型被用作开发决策支持系统的算法。Weka被用于特征选择和构建逻辑回归模型。该决策支持系统使用R Studio和R Shiny进行编码和设计，包括数据可视化和个体预测。
英文摘要：
The sudden change in the landscape of Philippine education, including the implementation of K to 12 program, Higher Education institutions, have been struggling in attracting freshmen applicants coupled with difficulties in projecting incoming enrollees. Private HEIs Enrolment target directly impacts success factors of Higher Education Institutions. A review of the various characteristics of freshman applicants influencing their admission status at a Philippine university were included in this study. The dataset used was obtained from the Admissions Office of the University via an online form which was circulated to all prospective applicants. Using Logistic Regression, a predictive model was developed to determine the likelihood that an enrolled student would seek enrolment in the institution or not based on both students and institution's characteristics. The LR Model was used as the algorithm in the development of the Decision Support System. Weka was utilized on selection of features and building the LR model. The DSS was coded and designed using R Studio and R Shiny which includes data visualization and individual prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
