A recent study uses machine learning to predict which convicts are prone to reoffend. Analyzing 13,000 cases, a Decision Tree model identified prior convictions (especially suspended sentences), education level, and age at first offense as top factors influencing recidivism. The model achieved 67% accuracy, revealing that convicts with multiple prior sentences or early first offenses are more likely to reoffend. Law enforcement can leverage this model for parole decisions, probation strategies, and targeted rehabilitation programs.

Check the full study here: https://www.mdpi.com/2078-2489/14/3/161

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