Author : R. kaviyarasi , t. balasubramanian
Keyword : Educational data mining; ensemble; stacking; logistic regression; extratreesclassifier; adaboost; randomforest
Subject : Science and technology
Article Type : Original article (research)
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Abstract : One of the hottest and most popular methods in applied Machine Learning is Ensemble methods. Ensemble combines predictions from different models to generate a final prediction with better performance than any other single model. The research focused on the implementation of Ensemble method for predicting student academic performance based on their personal characteristics, family background, infrastructural environment in the college and external environment, etc...Our study uses RandomForestClassifier, Logistic Regression, and ExtraTreesClassifier as the Base Learners and AdaBoost Classifier as the Meta Learner. This result helps in predicting the accuracy of students’ academic performance and also in identifying the poor performers, so that early measures prior to final semester examination can be deployed.
Article by : KAVIYARASI R
Article add date : 2020-12-16
How to cite : R. kaviyarasi , t. balasubramanian. (2020-December-16). Predictive analysis of academic performance of college students using ensemble stacking. retrieved from https://www.openacessjournal.com/abstract/499