• Applied K-means, neural networks to pinpoint parking hotspots, boosting city planning efficiency by 25%. • Leveraged spatial analysis and regression models on parking data, providing insights for over 15 businesses. • Utilized SVM and spatial data to forecast parking rate surges, enhancing dynamic pricing accuracy by 30%
Parking Demand Prediction
Identifying Parking Demand Hotspots and Predicting Rate Surge Using Machine Learning in Seattle
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