A Framework to Optimize Student Performance using Machine Learning
Abhijeet Joshi1, A. S. Kapse2

1Mr. Abhijeet Joshi, Department of Computer Science & Engineering, Anuradha Engineering College Chikhli (Maharashtra), India.

2Dr. Avinash S. Kapse, Associate Professor & Head of Department Computer Science & Engineering, Anuradha Engineering College Chikhli (Maharashtra), India.  

Manuscript received on 09 April 2024 | Revised Manuscript received on 03 May 2024 | Manuscript Accepted on 15 May 2024 | Manuscript published on 30 May 2024 | PP: 27-30 | Volume-13 Issue-1, May 2024 | Retrieval Number: 100.1/ijrte.A805213010524 | DOI: 10.35940/ijrte.A8052.13010524

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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: For scholars, mining data and extracting information from huge databases has emerged as an intriguing field of study. Since a few decades ago, the concept of using data mining techniques to extract information has been around. The dataset was originally intended to be partitioned and the inherent features examined using classification and clustering algorithms. They base their predictions on these characteristics. These forecasts have been made in the area of educational data mining for a variety of reasons, including to predict student success based on personal characteristics and help students find the right professors and courses. These goals have been drawn from the attrition and retention of students. These objectives are the focus of our research on student attrition and retention. Additionally, we have found exciting variables that aid in predicting students’ success, suggesting the most qualified instructors, and assisting them in course selection.

Keywords: Mining, Databases, Information, Dataset, Predictions, Performance.
Scope of the Article: Machine Learning