The Hotspot Detector project is a machine learning-based initiative aimed at identifying hotspots within a defined geographic region. Leveraging a variety of algorithms, including K-Means, Birch, Gaussian Mixture Model, Hierarchical Clustering, and Mean-Shift Clustering, the project employs sophisticated clustering techniques to pinpoint areas of interest. Validation of the algorithmic results is conducted using robust metrics such as the Silhouette Score and Heatmap.
This model can be utilized as per specific requirements, including:
- Crime Hotspots: Identifying areas with high crime rates to optimize the deployment of security services.
- Taxi Hotspots: Identifying locations with high demand for taxis but limited supply, enabling the strategic placement of taxi drivers.
The Hierarchical Clustering algorithm exhibited outstanding performance, achieving a silhouette score of 0.943. This indicates the model's exceptional ability to accurately identify and delineate hotspots within the specified area.