Location Intelligence & Clustering System
Data-driven geospatial analysis system using clustering for intelligent location recommendations.
Problem
Selecting optimal locations based on multiple factors like venues, accessibility, and preferences is complex and subjective without structured data analysis.
Solution
Collected and processed geospatial and venue data
Engineered features representing location characteristics
Applied KMeans clustering to group similar neighborhoods
Generated insights and recommendations based on cluster patterns
Tech Stack
KMeansPandasScikit-learnGeospatial Analysis
Architecture
Data Collection → Data Cleaning → Feature Engineering → KMeans Clustering → Cluster Analysis → Recommendations
Challenges
Handling sparse and noisy real-world location data required careful preprocessing. Choosing the optimal number of clusters and ensuring meaningful segmentation was also a key challenge.
What I’d Improve Next
- • Incorporate dynamic user preferences into recommendation engine
- • Use advanced clustering (DBSCAN, hierarchical)
- • Integrate real-time data sources for adaptive insights