Study on the Analysis and Prediction of Drug Addiction Among University Students of Bangladesh Using Machine Learning

Abuse of illicit substances among students at educational institutions is rapidly reaching crisis proportions in terms of the number of addicts. Substance abuse of all kinds is becoming an increasingly widespread problem. The goal of this research is to determine whether or not it is possible to make a prediction regarding the abuse of intoxicating substances that is frequent among university students in Bangladesh. The visualization of the data from this study indicated parameters or factors connected to the propensity to take drugs. In this particular study, machine learning is used to derive projections and predictions regarding the use of illicit drugs. In this study, Questionnaires were used to gather the data. For the purpose of carrying out this study, the sample consisted of a total of 468 different pupils. The use of Google Forms allowed for the collection of information as a questionnaire. The majority of those who participated were young adults (between the ages of 24 and 29), and the majority of those young adults were male students. At least 30% of the sample for this study admitted to experimenting with drugs for recreational purposes. According to their responses, the most significant factors that lead to student drug use include spending the night at the home of an addicted friend, being subjected to the destructive influence of peers, and smoking cigarettes. The neural network (Multilayer perceptron) algorithm gives an accuracy of 93% which is the highest, furthermore, the random forest algorithm gives the second-best accuracy.