Student Grade Prediction

Abstract

Education is a dominating strand in accomplishing indelible economic progress. It is complex and nuanced. Grades outline the shape of our institutional system. It is the most powerful bargaining chip, at once cherished and dreaded by most students, the unyielding mallet of teachers and parents compressed into a single letter. However, the grading system is not an efficient way to gauge intelligence. The domains of Data Mining (DM) and Business Intelligence (BI) aim at deriving impactful insights from unprocessed data and propose techniques that can encourage a change in the education system. Our work plans to analyze students in secondary year of education using Business Intelligence and Data Mining techniques. These algorithms assist in finding patterns. It covers a broad scope of statistics, machine learning, and database systems. Past evaluations are influential in their performance. Insightful research shows that there are some other pertinent features (for example, department, age, romantic relations, outings, and goals). The methodology uses seven different algorithms and compares them to find the most suitable one. Visualizations help understand each factor thoroughly. As a result of this research, we can also analyze the reason behind a student’s achievements. Each student faces several hurdles. The system should not focus only on improving student’s grades but should also be concerned with the other aspects affecting their scores. The paper presents the research of the factors affecting the student’s grades the most.