A Comprehensive Assessment on Predictive Analytics using Data Mining Techniques

Educational Data Mining is a fast emerging multidisciplinary field that reveals unexpected patterns in educational organizations. Considering the issues and demanding situations faced through instructional establishments in studying people overall performance and enhancing their instructional management, the diverse automated studying strategies have been examined. In this work, more Research contribution were reviewed and evaluated in revealing alternative ways for evaluating students' educational attainment to improve quality and support students in attaining remarkable achievements in their academics. The most typical applications of educational data mining encompass: enhancing the study process, intensifying course completion, assisting students in curriculum selection, student profiling, classifying factors that predispose to dropout, student targeting, curriculum planning, predicting student performance, and as a support for decision-making at the time of student enrolment. Predictive analytics is a machine learning and data mining method that is commonly utilized in educational data mining, to foresee expected future trends, events, and behaviors based on prior data. As a result of data mining algorithms, academic establishments will be able to make people academic outcomes accessible in aims to enhance the quality of learning they deliver.