Open AccessPredictive analysis of academic performance of college students using ensemble stacking
Author(s) -
R. Kaviyarasi,
T. Balasubramanian
Publication year - 2020
Publication title -
kongunadu research journal
Language(s) - English
Resource type - Journals
ISSN - 2349-2694
DOI - 10.26524/krj.2020.28
Subject(s) - adaboost , ensemble learning , computer science , machine learning , artificial intelligence , logistic regression , classifier (uml) , ensemble forecasting
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