Using the data of the mobile phone coverage to give the users more information that enables them to choose a company propperly.
What it does
The script creates an interface which takes the adress of the user as a parameter, as well as a free number other places where the user goes usually. As an output, it returns the ranking of the performance of the companies as an average of connectivity in these places.
How we built it
We create an interface with tkinter that displays the inputs and the outputs, we use the geolocate package to determine longitude and latitud of the adress given. We use vectorization in order to compute statistics on csv documents that have about 250 Mb to attain the calculations in short time.
Challenges we ran into
As we are dealing with a huge amount of data, we cannot compute in really short time.
Accomplishments that we're proud of
Achieving a solution to a problem that has not been proposed before. Actually, giving a new perspective to a problem that has been extensively worked and determining how does this newly considered factor perform on different people.
What we learned
Creating an interface with Python, using the geolocate package, dealing with big amounts of data. We learnt that vectorized computations do go faster than the "for" approach.
What's next for User signal ranking based on daily activities.
Tracking people behaviour's with geolocalization by tracking their movement, and the integrating over their usual paths in order to recommend the optimal company.