Predicting Student Academic Performance Using Learning Vector Quantization and Probabilistic Neural Network

• Author(s): Omkar Deshmukh; Yash Mishra; Sweta Nigam
• Paper ID: 1715384
• Page: 1825-1832
• Published Date: 23-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026

Abstract

Early prediction of student performance is an important task in the field of education, as it helps teachers identify the performance of the students and provide them with the required academic support. This paper proposes a framework based on Artificial Neural Network (ANN) techniques, namely Learning Vector Quantization (LVQ) and Probabilistic Neural Network (PNN), for the classification of the students based on the behavioral, academic, and demographic features of the students. The performance of the students is determined by using the historical data of the students. The proposed framework is based on the Streamlit platform, which is used for the visualization of the performance of the students.

Keywords

Student Performance Prediction, Machine Learning, Data Preprocessing, Feature Engineering, Predictive Modeling, Classification.

Citations

IRE Journals:
Omkar Deshmukh, Yash Mishra, Sweta Nigam "Predicting Student Academic Performance Using Learning Vector Quantization and Probabilistic Neural Network" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 1825-1832 https://doi.org/10.64388/IREV9I9-1715384

IEEE:
Omkar Deshmukh, Yash Mishra, Sweta Nigam "Predicting Student Academic Performance Using Learning Vector Quantization and Probabilistic Neural Network" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715384