Paper Title
A Dataset: Impact of Education on Juvenile Delinquency

Abstract
This paper aims to propose a new dataset on Indian juvenile crime statistics and use data mining techniques to analyze the relationship between education and juvenile delinquency in India. The goal is to investigate the risk factors, such as family and parents, financial problems, education, and peer groups, that contribute to the increasing trend of criminal offenses committed by children and youth in India. The technique of association rule mining will be used to analyze the education risk factor in particular. This paper aims to draw attention to the need for increased vigilance and awareness of juvenile crimes in Indian states and union territories, by analyzing their crime statistics. It employs a number of traditional supervised machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest, for classification purposes. The goal is to identify patterns and correlations in the data that can inform policy decisions and crime prevention efforts.