Student Academic Performance analysis using Classification algorithms

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Student Academic Performance analysis using Classification algorithms
Author(s):
E. Iyappan, K. Rajapriya, A. Santhosh, T. Sasikumar, P. Vinitha, M.S. Sassirekha, Anbarasan Balakrishnan
Keywords:
Classification, Support Vector classification, Adaboost, Matplotlib, Seaborn, Logistic regression, Decision tree, Random forest, Stochastic classification
Abstract
In this paper, we performed analysis is to measure performance of the students and factors affecting their performance. The model can be used for early predicting student performance to help in improving student performance on the subject. Supervised algorithms like Decision tree, Adaboost, Random Forest, Stochastic classification and Logistic regression are used to predict the model.
Article Details
Unique Paper ID: 154170 Publication Volume & Issue: Volume 8, Issue 7 Page(s): 35 - 38
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