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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: | Educational Grade Prediction System Using Machine Learning Algorithm |
| Authors Name: | Hritik Shirsath , Kalyani Darekar , Shweta Khilari , Shweta Mate , Prof. J. V. Borase |
| Unique Id: | IJSDR2305190 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | Predictive analytics applications are highly desired in higher education. Predictive Analytics used advanced analytics, including applications of machine learning, to deliver high-quality performance and valuable information at all academic levels. Researchers have developed many variations of machine learning approaches in education over the past decade. This system enables in-depth study of machine learning approaches for predicting students' final grades in the firstsemester course by improving prediction accuracy. The proposed system emphasizes two components. First, using a dataset of real student course performance, we tested six well-known machine learning algorithms including Decision Trees (J48), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF). Evaluate the accuracy of the approach's performance. Next, to avoid overfitting and misclassification results resulting from imbalanced multiple classifications, we provide a multiclass prediction model that can improve the unbalanced multiple classification prediction performance model for predicting student performance. |
| Keywords: | Predictive analytics applications are highly desired in higher education. Predictive Analytics used advanced analytics, including applications of machine learning, to deliver high-quality performance and valuable information at all academic levels. Researchers have developed many variations of machine learning approaches in education over the past decade. This system enables in-depth study of machine learning approaches for predicting students' final grades in the firstsemester course by improving prediction accuracy. The proposed system emphasizes two components. First, using a dataset of real student course performance, we tested six well-known machine learning algorithms including Decision Trees (J48), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF). Evaluate the accuracy of the approach's performance. Next, to avoid overfitting and misclassification results resulting from imbalanced multiple classifications, we provide a multiclass prediction model that can improve the unbalanced multiple classification prediction performance model for predicting student performance. |
| Cite Article: | "Educational Grade Prediction System Using Machine Learning Algorithm", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.1195 - 1204, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305190.pdf |
| Downloads: | 000337070 |
| Publication Details: | Published Paper ID: IJSDR2305190 Registration ID:206513 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 1195 - 1204 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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