Abstract
The enormous issue of drop-out students or resigned is regarding scholarly accomplishment. The academic organization wants to catch up the prompting framework while counselors be supposed to direct the arranging educational programs to their advisors. The data mining methods are very much helpful to provide the qualitative education in the academic institutions and to analyze the student performance quickly. Different classification models which can be applied in academic data mining are focused in this paper. The student problems can be identified by applying different classification models. The goal of this paper is to enhance the performance of student and at that point anticipating the appropriated scholarly accomplishment in each major. To look at the investigation, we utilized 1200 students’ data. Two measures like accuracy and error rate are assessed the framework. This method produces the 95.5% accuracy and 4.5% error rate.
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Ramakrishna Sajja, V., Jhansi Lakshmi, P., Bhupal Naik, D.S., Kalluri, H.K. (2021). Student Performance Monitoring System Using Decision Tree Classifier. In: Bhattacharyya, D., Thirupathi Rao, N. (eds) Machine Intelligence and Soft Computing. Advances in Intelligent Systems and Computing, vol 1280. Springer, Singapore. https://doi.org/10.1007/978-981-15-9516-5_33
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DOI: https://doi.org/10.1007/978-981-15-9516-5_33
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