Where should we live?

Inspiration

Leaving my familiar environment for an internship at a different country last year with people from my university, we struggled to find a neighbourhood that suited us all, in terms of commuting, prices, and our interests. We had to manually look at every neighbourhood, what the commuting was like, as well as if it was in our price range. This year, seeing a similar problem in the horizon for when we graduate, we decided to organise a team of 3, working continuously for 24 hours, to avoid that 4-hour manual work. engineers

What it does

WhereShouldWeLive asks a user to supply the work locations of their future flatmates, and by intelligent optimisations finds a collections of points that seem a good match to the group. Then, the user is able to look into the neighbourhoods that interest them the most, and see further information, such as statistics, and even flats!

How we built it

We had some previous knowledge that Tfl, CityMapper and Google provide transport planning, as well as the fact that we could get some information on flat prices. We thus decided to build a Python backend (which we were all familiar with), with a React front-end to build a more meaningful product.

Challenges I ran into

We had iterate through API's as we were not sure which one was giving us the information we wanted in the best way. Furthermore, running out of available requests was a bottleneck as we had to create new accounts and look for alternative solutions.