University admission process: a prescriptive analytics approach

Students typically do not have practical tools to help them choose their target universities to apply. This work proposes a comprehensive analytics framework as a decision support tool that assists students in their admission process. As an essential element of the developed framework, a prediction procedure is developed to precisely determine the student's chance of admission to each university using various machine learning methods. It is concluded that random forest combined with kernel principal component analysis outperforms other prediction models. Besides, an online survey is built to elicit the utility of the student regarding each university. A mathematical programming model is then proposed to determine the best universities to apply among the candidates considering the probable limitations; the most important is the student's budget. The model is also extended to consider multiple objectives for making decisions. Last, a case study is provided to show the practicality of the developed decision support tool.