Abstract: Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that Supervision Risk Score, Percent Days Employed, and Education Level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliationemerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners.
Keywords: Juvenile recidivism, criminal justice system, broader societal safety. juvenile justice practitioners.
Title: Data-Driven Insights into Juvenile Recidivism: Leveraging Machine Learning for Rehabilitation Strategies
Author: Saiakhil Chilaka
International Journal of Social Science and Humanities Research
ISSN 2348-3156 (Print), ISSN 2348-3164 (online)
Vol. 12, Issue 4, October 2024 - December 2024
Page No: 41-44
Research Publish Journals
Website: www.researchpublish.com
Published Date: 15-October-2024
