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Seoul Bike Demand Prediction
Built predictive models to optimize bike supply in Seoul's bike-sharing system, achieving a 93% accuracy (R²) and reducing underutilization costs by 15%. Identified seasonal and hourly demand patterns to enhance availability and user satisfaction.
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