Analyzing Data in Decision Making for Educational Universities Using Machine Learning

In an educational field, the application of machine learning algorithms is currently the main focus for researchers and scientists. This document focuses on describing the usability and advancement of machine learning for increasing the accuracy of decision making for educational universities; popular machine learning methods are analyzed. The concert of each machine learning algorithms is assessed in requisites of prediction of time, accuracy, and the same has been documented. The various machine learning algorithms used in educational universities have been developed and tested in many regions of developed countries. Such tools are an immense boon for developing countries which can be leveraged even across high school level since assimilation of data is difficult due to the availability of limited resources. The rationale of this study is to establish the prediction of the rate of graduation of undergraduate programs in the university. The management of educational universities plays a vital role in making most pronouncements that have collision on the strategic level and the student, professors, graduates, and the whole institution's academic society. In the process of decision making, the directors depend on educative tools to support their various missions and many barriers including rigid government structure changes in rules, technological conditions. This may hinder the support required by university directors and managers. Therefore, this document seeks an outline of the popular usability of the machine learning algorithms in the educational university with the focus on techniques used for graduation rate prediction.