We're all CMU students which means we have a lot of work to do and not very much time to do it in. Keeping track of all of our assignments is hard. They’re all due at different times and figuring out which assignment to do and when is next to impossible. You have to be able to prioritize which assignments to work on and when.
What it does
Quel helps you organize the time spent on your homework and other activities by evenly distributing the time. It also uses machine learning to predict how long an assignment might take based on previous data taken from former students.
How we built it
We built the backend using the Flask web microframework, Keras machine learning library with a TensorFlow backend, and the RethinkDB database.
We built the frontend using React, Redux, Semantic UI, and Yuanchu’s tears.
Challenges we ran into
As it turns out, mixing Flask and Tensorflow is a dangerous concoction!
Accomplishments that we're proud of
The time distributions are going to make a real impact on our schedules. We also got the machine learning model to work!
What we learned
We learned how to use Keras and TensorFlow, and how to design a full stack application. Angela learned how to Hackathon. Emmanuel learned that he should get more sleep. Harrison learned about optimization algorithms. Yuanchu learned how much he "loves" front-end development.
What's next for Quel
Integration with Google calendar, full deployment, time-of-day productivity optimization, accounting for office hours, retaining fixed study sessions, progress tracking, and more!