Informational tools are necessary at schools and colleges due to the sheer volume and diversity of data they handle. Numerous scholars has emphasized towards applying machine learning to retrieve information from the education database to enable students and educators in attaining greater results as a means of simplifying essential work. Selecting efficient tactics that might produce acceptable prediction performance is a challenging task for prediction models. In order to improve classification performance by addressing the misclassification issue, this study proposes a hybrid approach known as arbitrator miniature that combines factor analysis with the following nine machine learning techniques: Support vector machine, Random Forest, K Nearest Neighbor, Logistic Regression, Artificial neural network, Decision Tree, XG boost, Ada boost and Naïve Bayes. To evaluate the robustness of the suggested models, student datasets from a variety of academic fields at diploma-granting institutions in Karnataka, India, were used. In order to assess the proposed model using the datasets, assessment criteria such as classification accuracy and root mean square error were employed. This study’s findings revealed that proposed arbitrator miniature model might significantly improve classification performance. For the purpose of resolving prediction and classification issues, the proposed arbitrator miniature may be viewed as the best prediction models.
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