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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | MULTICLASS PREDECTION MODEL FOR STUDENTS GRADE PREDECTION USING MACHINE LEARNING |
| Authors Name: | G.JAYANTH SATYA , SHAIK MULLA ALMAS , D.PAVAN KUMAR , MOHAMMED ASRAR , M.YASHWANTH, N.SAI SUBHASH |
| Unique Id: | IJSDR2403117 |
| Published In: | Volume 9 Issue 3, March-2024 |
| Abstract: | Today, there is a growing demand for predictive analytics applications in higher education institutions. These applications utilize advanced analytics, including machine learning, to extract valuable insights and improve performance across all levels of education. Student grades are a crucial performance indicator that educators use to track academic progress. Over the past decade, various machine learning techniques have been proposed for educational purposes. However, challenges persist in dealing with imbalanced datasets to enhance the accuracy of predicting student grades. This study offers a detailed analysis of machine learning techniques to predict final student grades in first-semester courses, aiming to boost predictive accuracy. The paper focuses on two main modules. Firstly, it compares the performance of six popular machine learning techniques - Decision Tree (J48), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbor (kNN ), Logistic Regression (LR), and Random Forest (RF) - using a dataset of 1282 real student course grades. Secondly, a multiclass prediction model is proposed to address overfitting and misclassification issues in imbalanced multi-class scenarios, employing oversampling techniques like Synthetic Minority Oversampling Technique (SMOTE) along with feature selection methods. The results demonstrate that integrating the proposed model with RF leads to a significant improvement, achieving the highest f-measure of 99.5%. This model shows promising results in enhancing prediction performance for imbalanced multi-class student grade prediction. |
| Keywords: | SVM, NB, KNN, LR, RF. |
| Cite Article: | "MULTICLASS PREDECTION MODEL FOR STUDENTS GRADE PREDECTION USING MACHINE LEARNING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 3, page no.832 - 836, March-2024, Available :http://www.ijsdr.org/papers/IJSDR2403117.pdf |
| Downloads: | 000337363 |
| Publication Details: | Published Paper ID: IJSDR2403117 Registration ID:210531 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: 832 - 836 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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