A MACHINE LEARNING APPROACH FOR UNDERSTANDING GPA WITH STUDENTS’ EXPERIENCE USING HYBRID ALGORITHM

Abstract

Foreseeing understudies' review has risen as a noteworthy zone of examination in training because of the craving to distinguish the fundamental factors that impact scholastic execution. Due to constrained accomplishment in foreseeing the Grade Point Average (GPA), the greater part of the earlier research has concentrated on anticipating grades in a particular arrangement of classes dependent on understudies' earlier exhibitions. The issues related with information driven models of GPA expectation are additionally opened up by a little example measure and a generally vast dimensionality of perceptions in an analysis. In this paper, we use the best in class machine learning systems to develop and approve a prescient model of GPA exclusively dependent on an arrangement of self-administrative learning practices decided in a moderately little example analyze. At last, the objective of level expectation in comparative examinations is to utilize the built models for the outline of mediation methodologies went for helping understudies in danger of scholarly disappointment. In such manner, we lay the numerical preparation for characterizing and identifying most likely accommodating mediations utilizing a probabilistic prescient model of GPA. We exhibit the use of this structure by characterizing fundamental intercessions and recognizing those mediations that are most likely supportive to understudies with a low GPA. The utilization of self-administrative practices is justified, in light of the fact that the proposed mediations can be effortlessly drilled by understudies.