Predicting Student’s Alcohol Drinking Habits Using Machine Learning Techniques

Abstract

Alcohol drinking among college student’s is common problem that can leads to low academic performance, health issues and risky behavior. When student’s in college, they often experience more freedom, which can result to increased consumption of alcohol. Therefore, this work explores the prediction of student’s alcohol drinking habits utilizing Machine Learning techniques. Data set is utilized in this work contains 25 parameters collected by various college students and using this dataset is employed for Machine Learning algorithms, such as Random Forest Classifier, Decision Tree, and Logistic Regression, are employed. This experiment aims to classify students into categories of alcoholic, non-alcoholic and maybe alcoholic based on various influencing factors. The results obtained are contrasted with various Machine Learning techniques.