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PYTHON · HEALTHCARE ANALYTICS

30-Day Hospital Readmission Prediction

A concise case study showing the business problem, analytical approach, and resulting value.

Recreated 30-day hospital readmission prediction dashboard
Portfolio visualization recreated with sample or illustrative data to demonstrate the project’s analytical approach.

THE PROBLEM

Healthcare organizations need ways to identify patients who may be at greater risk of readmission so that limited intervention resources can be prioritized.

THE APPROACH

Prepared a diabetes-related healthcare dataset, engineered utilization and risk features, and compared logistic regression, random forest, XGBoost, and support vector machine models.

THE OUTCOME

Produced a structured model comparison and translated predictive findings into possible operational and clinical intervention considerations.

TOOLS

PythonPandasScikit-learnXGBoostFeature Engineering
Confidentiality note: This case study highlights the business problem, analytical approach, and impact while protecting confidential company and client information. Any public visuals or demonstrations use recreated or sample data.