Designing A Method for Alcohol Consumption Prediction Based on Clustering and Support Vector Machines

In this study, an implementation of several data mining techniques is presented, including decision trees, Support Vector Machines (SVM), Bayesian Networks and K-Nearest Neighbor and their comparison using different evaluation metrics such as True Positive Rate (TpRate), False Positive Rate (FpRate) and Recall, with the dataset “STUDENT ALCOHOL CONSUMPTION”, that provides information of alcohol consumption in teenagers in Portugal. High alcohol consumption rate in teenagers in society, high schoolers and college students, has become a social problem with alarming data showing they start consuming alcohol between 10 and 14 years and this obviously has a huge impact in their behavior, especially with situations such as binge drinking. At the end of the study, the results found show that Support Vector Machines (SVM) have a better accuracy rate than other techniques used and corroborate that the proposed method it is quite efficient and highly precise for detection of students consuming alcohol, improving the results obtained in previous similar studies.