Student Grade Prediction
Vruddhi Mehta1, Rajasi Adurkar2, Kriti Srivastava3

1Vruddhi Mehta*, Computer Department, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
2Rajasi Adurkar, Computer Department, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
3Kriti Srivastava, Computer Department, Dwarkadas J. Sanghvi College of Engineering, Mumbai, India.
Manuscript received on August 19, 2020. | Revised Manuscript received on September 01, 2020. | Manuscript published on September 10, 2020. | PP: 311-316 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78260991120 | DOI: 10.35940/ijitee.K7826.0991120
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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. 
Keywords: Analysis, Education, Grade Prediction, Machine learning, Regression.