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Enhancing Academic Performance Prediction Through K-Means Clustering and Comparative Evaluation of Machine Learning Algorithms: A Case Study on Student Dataset

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Business Data Analytics (ICBDA 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2358))

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Abstract

Predicting academic performance is a crucial task in educational institutions, as it aids in identifying students who may require additional support or intervention. This research paper presents an innovative approach to academic performance prediction by combining K-Means clustering with an in-depth comparison of advanced machine learning algorithms. The study utilizes a comprehensive student dataset sourced from Kaggle, encompassing diverse attributes related to students’ backgrounds and academic history. The dataset is subjected to K-Means clustering, resulting in the identification of distinct student clusters. Subsequently, four prominent machine learning algorithms—K-Nearest Neighbors (KNN), Neural Network (NN), Random Forest (RF), and Support Vector Machine (SVM)—are rigorously evaluated for their predictive efficacy on academic performance. The experimental results showcase the utility of K-Means clustering in segmenting students into meaningful clusters based on shared characteristics. The comparative analysis of the machine learning algorithms reveals varying levels of accuracy, precision, recall, and F1-score in predicting academic performance across different clusters. The outcomes highlight the algorithm that exhibits superior performance in this specific context.

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Acknowledgment

Authors would like to thank Dr. Ranjit Patil, Principal, Dr. D. Y. Patil Arts, Science and Commerce College, Pimpri, Pune (MS) for helping to write this research paper and useful discussions. I also like to express my sincere thanks to Dr. Bharat Shinde, Principal, Mr. Gajanan Joshi, Head Department of Computer Science, Vidya Pratishthan’s Arts, Science and Commerce College, M.I.D.C., Baramati, Pune (MS) for giving me valuable guidelines and suggestions regarding this work. We would like to extend my sincere appreciation to Mr. Amol Mohite for their invaluable assistance in formatting this research paper.

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Authors and Affiliations

  1. Dr. A.P.J. Abdul Kalam University, Indore, Madhya Pradesh, India

    Gautam Appasaheb Kudale & Sandeep Singh Rajpoot

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  1. Gautam Appasaheb Kudale
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  2. Sandeep Singh Rajpoot
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Correspondence to Gautam Appasaheb Kudale .

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Editors and Affiliations

  1. Uttaranchal University, Dehradun, Uttarakhand, India

    Rajesh Singh

  2. Uttaranchal University, Dehradun, Uttarakhand, India

    Anita Gehlot

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Kudale, G.A., Rajpoot, S.S. (2025). Enhancing Academic Performance Prediction Through K-Means Clustering and Comparative Evaluation of Machine Learning Algorithms: A Case Study on Student Dataset. In: Singh, R., Gehlot, A. (eds) Business Data Analytics. ICBDA 2023. Communications in Computer and Information Science, vol 2358. Springer, Cham. https://doi.org/10.1007/978-3-031-80778-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-80778-7_20

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