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
- 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