A Comparative Performance of Classification Algorithms in Predicting Alcohol Consumption Among Secondary School Students

Abstract

The increased consumption of alcohol among secondary school students has been a matter of concern these days. Alcoholism not only affects individual’s decision-making ability but also have a negative effect on academic performance. The early prediction of a student consuming alcohol can be helpful in preventing them from such risks and failures. This paper evaluates classification algorithms for prediction of certain risks of secondary school student due to alcohol consumption. The classification algorithms considered here are three individual classifiers including Naive Bayes Classifier, Random Tree, Simple Logistic and three ensemble classifiers: Random Forest, Bagging, and Adaboost. The dataset is taken from the UCI repository. The performance of these algorithms is evaluated using standard evaluation metrics such as Accuracy, Precision, Recall and F-Measure. The results suggested that Simple Logistic and Random Forest performed better than the other classifiers.