A comprehensive solution to the neighborhood-lifestyle matching problem
NeighborFit is a full-stack web application developed as part of an academic project to solve the complex problem of matching individuals and families with neighborhoods that align with their lifestyle preferences, budget constraints, and long-term goals.
Traditional neighborhood selection methods rely heavily on price and proximity to work, often overlooking crucial factors like community culture, walkability, safety perception, and lifestyle compatibility. This leads to suboptimal housing decisions and reduced quality of life satisfaction.
"A data-driven, multi-dimensional matching algorithm that considers lifestyle preferences, demographic alignment, amenity accessibility, and transportation patterns can significantly improve neighborhood selection outcomes and user satisfaction compared to traditional price-and-location-only approaches."
Implemented intelligent caching, batch processing, and rate limiting strategies to maximize free tier usage across multiple data sources.
Leveraged government open data portals, census information, and public datasets to build comprehensive neighborhood profiles without licensing costs.
Designed client-side processing and caching strategies to minimize server costs while maintaining performance and user experience quality.
This project was developed as part of a comprehensive assignment focusing on problem analysis, research methodology, technical implementation, and systems thinking. The goal was to demonstrate the ability to solve complex, real-world problems through systematic research, data analysis, and algorithmic thinking within significant resource constraints.