Predicting Academic Achievement for Students Using Machine Learning Technique

Abstract

Abstract: Predicting the academic achievement of pupils is a significant component that needs to be taken into mind anytime concerns involving higher education or more in-depth schooling, particularly the connections between the two, are brought up for discussion. Students are able to choose the classes with future study plans that will be most useful to them with the assistance of the capability to predict their achievement. This is made possible by the availability of this skill. It gives teachers and administrators the opportunity to monitor pupils, which in turn enables them to provide more support for students, combine training programs for the highest potential results, and anticipate how successfully students will complete their education. One of the many benefits of student forecasting is that it leads to a decline in the number of official warning signals for school expulsions that are caused by inefficiency. This is only one of the many benefits of student forecasting. Students are able to see their own futures if they take the time to select their courses thoughtfully and come up with study methods that make the most of their unique sets of abilities and areas of interest. As it had values of 0.888 for accuracy, precision, recall, & f1 accordingly for each of those categories, the Support Vector Classifier was the most beneficial tool for this inquiry. This was due to the fact that it was able to correctly classify the data. These values are proof that the data were categorized with a high degree of accuracy. Throughout the course of this inquiry, several different methods of machine learning, such as ensemble, logistic regression, random forest, AdaBoost, & XG Boost, were applied