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Diagnosing a Patient for Diabetes

Use data analytics to diagnose diabetes

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Design a mechanism to discern whether a patient was likely to have diabetes through common trends and patterns. This was done by first conducting EDA on the sample dataset to visualize face-value trends. Afterwards, a prediction model was implemented to decide whether a patient was likely to have diabetes. Many algorithms were tested and a champion model was chosen based on efficiency and accuracy. Note: it will be very helpful to view the official report located at the Project Website under 'Data Science Capstone'.

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