This analysis uses a real-world health dataset to explore how different clinical and lifestyle factors influence diabetes risk. By examining variables such as BMI, fasting glucose, HbA1c, cholesterol, blood pressure, physical activity, smoking, and family history, we identify important patterns linked to diabetes. Through summary statistics, visualizations, group comparisons, and basic machine learning methods like clustering and KNN, the study highlights which factors differ most between high-risk and low-risk individuals. The results provide a clear, data-driven understanding of how metabolic and lifestyle indicators contribute to diabetes risk, helping support better health insights and decision-making.

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The Global Road Accident Analysis aims to explore and understand patterns, causes, and impacts of road accidents worldwide. By analyzing large datasets, the study highlights key factors like time of day, weather conditions, road types, vehicle involvement, and casualty rates. The goal is to uncover meaningful insights that can help in improving road safety policies, optimizing emergency response strategies, and ultimately reducing the number and severity of accidents globally. Through visualizations and statistical methods, this analysis provides a comprehensive view of accident trends across different regions and environments.

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