Predicting Alcohol Consumption in Students Using Data Mining Tool

Abstract

The alcoholism is a serious problem that
affecting both the individual and the society. Alcohol
drinking has several short term and future health effects.
This paper examines the student alcohol consumption
using a publicly available dataset that includes student
characteristics and grades. The process using data
mining tools and techniques to analyze data for
educational purposes is known as educational data mining
(EDM). EDM assists academic programs in identifying
and revealing hidden patterns in data. These patterns can
be used to learn which students consume alcohol and
what the impact it has on their academic performance.
Our paper contributes to our knowledge of the
relationship between student characteristics and alcohol
consumption. On a student dataset, this paper compares
the performance of the k- Nearest Neighbour (KNN),
J48, and Random Forest algorithms.