Campus Search

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  Output

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  Input

Inspiration

Rising Housing Costs and no rental properties for Madison students.

What it does

Provides personalized rental property recommendations near campus based on user preferences for budget, bedrooms, and proximity to key locations.

How we built it

Developed using web scraping for dataset creation, Django for backend, and machine learning with K-nearest neighbors for clustering recommendations.

Challenges we ran into

Faced difficulties in dataset assembly due to lack of existing data, selecting appropriate clustering strategies, and integrating real-time property updates.

Accomplishments that we're proud of

Successfully compiled a comprehensive dataset, devised an effective search and clustering method, and ensured recommendations are practical and relevant.

What we learned

Gained insights into data collection from scratch, the intricacies of machine learning for property recommendation, and the importance of real-time data.

What's next for Campus Search

Plans to incorporate real-time API data, compare against Fair Market Rates, and introduce a visual map interface to enhance user decision-making.

Provides personalized rental property recommendations near campus based on user preferences for budget, bedrooms, and proximity to key locations.

How we built it Developed using web scraping for dataset creation, Django for backend, and machine learning with K-nearest neighbors for clustering recommendations.

Built With

• css
• html
• javascript
• python