It all started with our interest in Envel's challenge to come up with an interesting savings algorithm, as well as in working with Google Cloud's NLP API.

What it does

Our web-app aims to determine what percentage of the user's net income should go into their savings by analyzing their mood through several processes, including open-ended questions, random check with Twitter API, and weather mood measure. Ultimately, the user should know what amount they should save: the more emotionally negative they are, the more they can spend (the less they need to save) on something that will make them happy.

How we built it

We built our app using Flask, MongoDB, Dark Sky API, Google Cloud's NLP API, and Twitter API.

Challenges we ran into

We mostly had problems with coming up with an appropriate algorithm for finding what percentage of the user's net income should go into their savings.

Accomplishments that we're proud of

We are proud of coming up with such an interesting, interdisciplinary idea/algorithm, and of being able to work with an NLP.

What we learned

Besides learning how to use an NLP, we learned how to combine what we have learned in the past to build a multidisciplinary project.

What's next for MoodBank

We look forward to coming up with more endgoals for the project, including more options for a more varied population (as we know not everyone feels happier when they spend instead of saving.

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