Diabetes is a major global health concern, with rising prevalence and substantial impacts on individuals and healthcare systems. Early identification of at-risk individuals allows for preventive interventions that can significantly reduce disease burden.

In this competition, participants are challenged to develop machine learning models to predict whether an individual has diabetes based on their demographic information, medical history, and basic lab test results.

This binary classification task will help demonstrate the potential of predictive models in supporting early diabetes screening and risk assessment.