A machine learning model was developed to predict future HbA1c levels and assess diabetes risk with the goal of increasing awareness and supporting early intervention. The initiative focused on understanding data patterns and reducing diabetes prevalence through predictive analysis. The process involved problem definition, exploratory data analysis, and statistical evaluation of numerical and categorical variables. Data was cleaned and prepared to ensure accuracy, followed by model building using suitable machine learning algorithms. The solution analyzed distributions, relationships, and feature behavior to generate reliable predictions, supporting proactive health monitoring and informed decision‑making related to diabetes management.

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