Regression model for a drug-related crime reduction

Abstract

Globally, drug-related offenses present a significant challenge, necessitating the development of effective prevention strategies. This abstract describes a regression model designed to address the complex dynamics of drug-related crimes. Using data from parents, faculty, and youth at a university, including demographic profiles and other drug-related information, the model identifies key factors contributing to the potential prevention of drug-related crimes. Through regression analysis, the model quantifies the relationships between these variables and provides insights into the causes of drug-related criminal behavior based on respondents' observations. The model identifies the most influential predictors of reducing drug-related crimes through careful preprocessing and feature selection, enabling a targeted approach to crime prevention and intervention strategies. The results show that each approach within the drug prevention model is significant. Notably, the findings indicate that parental involvement has the greatest impact on reducing drug criminality. Teachers contribute by focusing on the effects of drugs through seminars and integrating this information into their subjects. The community can also promote sports-related activities to divert youth interest. It is anticipated that these efforts will be effective because parents are already actively advising and educating their children.