Many of the people attending to Hackathons are not just hackers but also students and as such many of us have exams coming up next week. The idea of this project came up to decide if we had time to spend a weekend hacking in London instead of beeing studying for the upcoming exams.

What it does

Studdler analyzes a database in growing process wich contains information about grades obtained, difficulty, and different tipes of studying preparation for many different exams and many different students. Using this dataset and using machine learning it is able to generate the optimal studying schedule (personalized for every student) needed to obtain a wanted grade specified by the student.

How we built it

We developed the needed database using flask. We then aceeded to the stored dataset using python and analyzed it using personally developed machine learning algorithms inspired on various papers found on the web. We then theveloped a user-friendly interface with android and run a further inprovement to the data obtained via the previous method in order to personalize the results to the each user.

Challenges we ran into

Since we knew very little about android we run in some minor problems when developing the grafic interface but the part where we had the most problems was when incorporating the python algoritm to the android language.

Accomplishments that we're proud of

We are very proud of the machine learning algorithms we came up with and we think they can be used to optimize the studying time for busy students.

What we learned

We learned machine learning tecniques and got much more familiar with the android lenguage.

What's next for Studller

Increasing the database that we currently have would be a huge first step for improving Studdler's efficiency and a way to improve it would be commercializing the product and make use of the data provided by every user. Once obtained a bigger dataset we should also be able to improve our machine learning algorithms implementing neuronal nets methods and thus providing the user with a more personalized and efficient studying plan.

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