Relevant factors and classification of student alcohol consumption

Abstract

Educational data mining is the process of applying data mining tools and techniques to analyze data for educational purpose. This paper carries out educational data mining to study the student alcohol consumption through a public dataset which includes student attributes and their grades. The decision tree algorithm and the random forest algorithm are applied to perform classification and to analyze the variable importance. The regression model is then employed to illustrate the relationship between alcohol consumption level and the students' final grades. Our analysis provides knowledge on the relationship between student characteristics and alcohol consumption. The study also compares performance of the decision tree algorithm and the random forest algorithm.