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This project leverages unsupervised machine learning to uncover hidden patterns in patient health data. By clustering patients based on metrics such as heart rate, blood pressure, temperature, and oxygen saturation, it moves beyond static threshold based diagnostics to provide more dynamic, personalized insights into patient well being.