Prediction of alcohol consumption among Portuguese secondary school students: A data mining approach

Abstract

This paper is set to perform a comparative experiment on prediction of alcohol consumption among secondary school students. Data set used in this project contained 34 attribute was gathered from two Portuguese secondary schools in the year 2005–2006. Four classification algorithms are proposed and implemented, which include the Decision Tree, k-Nearest Neighbour (k-NN), Random Forest and Naïve Bayes. These methods were trained and tested using 10-fold cross validation. The results showed that the Decision Tree algorithm produced highest values for accuracy, recall and precision compared to other classification algorithms. Besides, it is observed that Naïve Bayes methods combined with Interquartile normalization provides a promising alternative classification technique in the area.