Sarandonga

Inspiration

We came to a city that was new to us and thought, what about finding a place that suits our tastes in music and it's close to us? After some thought we were like, 'damn, we're hackers, let's implement it'.

What it does

The app recommends you a bar that suits your music tastes in 2 different ways: 1) Depending on your answers to a small survey. This survey is a small scrolling test with differrent music that has a genres associated to it and that only triggers once you register on the app or once you update your profile. 2) By a search that includes the genres you wanna hear tonight and your location. This search can be made on the main menu.

How we built it

JavaScript and NodeJS for backend, a REST API on React native for the frontend and MongoDB for the database. We also used Python to create our data-collection script. As we got our hands into an offical data from the Barcelona TownHall site about pubs, we decided to complete that dataset with the music genres are usually played in them. That's what we used the web scrappping from, to collect all the reviews we could find for those pubs and anaylising them by filtering the comments and using regex to adjust the results to what we were looking for.

Challenges we ran into

We didn't choose the correct technology at first so we had to start from scratch with only 15 hours left before the deadline

Accomplishments that we're proud of

The search engine, the swipper test and the finished backend that we managed to make. To be honest, mostly we're proud of the fact that we were capable of learning all this new technology in less than 15 hours.

What we learned

JavaScript, REST API, MongoDB

What's next for Sarandonga

Finish the implementation and add the last funtionalities we planned for it such as:

Being able to connect with other users with similar tastes to yours or that wanna go to the same bar/pub as you on the same day.
Adding some more restrictions to the search engine such as location and ages
Analytics for business owners that can give them information about the most searched genres, the ages of the users that tend to prefer that, information about the users that tend to favor his pub and information about how many times his pub was recommended.