The ML-Based Student Housing Solution aims to simplify the process of finding the best accommodation for students relocating to a new city. By leveraging the power of K-Means Clustering, this project classifies available accommodations based on student preferences regarding amenities, budget, and proximity to key locations. Utilizing Kaggle's 'Food Choices and Preferences of College Students' dataset, this project successfully categorized the population by optimizing K values and significantly reducing cluster variance. Additionally, the integration of the Foursquare API for geolocation data enables to plot these accommodations on a map using Folium, providing a user-friendly interface for students to find their ideal housing option.
- K-Means Clustering: Efficiently categorize accommodations to match student preferences.
- Dataset Utilization: Analysis of Kaggle's comprehensive dataset on college students' food choices and preferences to inform housing categorization.
- Geolocation Mapping: Integration of the Foursquare API for accurate accommodation locations, visualized through Folium maps.
This Project can be valuable for:
- Incoming students to help them find best accommodation on the basis of their preferences on amenities, budget, and proximity to the location
- Local businesses can use the demographic and geolocation data from this project for optimal site selection, reducing commute times, improving customer experiences, and fostering sustainable urban development
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Python 3.x
- Pip (Python package installer)
- Clone the repository:
git clone https://github.com/yourusername/ML-Based-Student-Housing-Solution.git