House Hunter

Inspiration

Finding a place in a new city can be tough. You generally don't know what the average housing for a certain place you want to rent is. This is to help first time renters in the city to be accustomed to the house rentals.

What it does

To provide a solution to this problem, we devised a service that compares rental listings to the average of the housing market. We are able to determine this by comparing the listing price to a list of data of other rentals within the city.

How I built it

This is done using a machine learning algorithm to predict the average value of the rentals based on features of the property such as number of rooms, appliances, and location.

Challenges I ran into

• Due to time constraints, the machine learning algorithm currently takes only the number of rooms into consideration
• Designing the logo.
• Figuring out where to get datasets from.

Accomplishments that I'm proud of

• Used machine learning for the first time.
• Completed the phase that we set out initially to be accomplished.

What I learned

• React.JS
• Machine learning with python using sci-kit learn
• Prototyping with IO

What's next for House Hunter

• Machine learning on more criteria and not just housing
• Animations to display general averages of different instances of the website

Built With

• css
• figma
• html
• javascript
• material-ui
• python
• react.js
• scikit-learn