## Inspiration

The idea of Fizz inspired us and motivated us to work on a simple yet intuitive budgeting solution that can be used by students especially teens.

## What it does

Fizzy is a simple yet seamless way for teens to have a track of their expenses and visualize them through graphs and charts. Fizzy also provides recommendations for users in managing their expenses. We provide users alerts when their expenses cross a given threshold limit. Further, Fizzy can help the user in decision making and managing the budget by providing an overview about how money is spent and how it can be saved.

## How we built it

We built Fizzy as a cross platform Flutter application. We used Firebase Firestore to save user data and retrieve it in real time. We also used a number of plugins and libraries which include charts for data visualization and tensorflow lite for deep learning.

## Challenges we ran into

* We lost touch with Flutter because it's been months since we worked on it. Today, we attempted to gain back the touch and it took us quite a while.
* Data viz consumed us remarkable amount of time.

## Accomplishments that we're proud of

* Coming up with a fully functional project within the time limits.
* Team work.

## What's next for Fizzy

* Researching the potential of such ideas in Fintech and know the shortcomings.
* Re construct the idea based on the inferences and try deploying it in the market.
## Inspiration

As students, we have found that there are very few high-quality resources on investing for those who are interested but don't have enough resources. Furthermore, we have found that investing and saving money can be a stressful experience. We hope to change this for those who want to save better with the help of our app, hopefully making it fun in the process!

## What it does

Our app first asks a new client a brief questionnaire about themselves. Then, using their banking history, it generates 3 "demons", aka bad spending habits, to kill. Then, after the client chooses a habit to work on, it brings them to a dashboard where they can monitor their weekly progress on a task. Once the week is over, the app declares whether the client successfully beat the mission - if they did, they get rewarded with points which they can exchange for RBC Loyalty points!

## How we built it

We built the frontend using React + Tailwind, using Routes to display our different pages. We used Cohere for our AI services, both for generating personalized weekly goals and creating a more in-depth report. We used Firebase for authentication + cloud database to keep track of users. For our data of users and transactions, as well as making/managing loyalty points, we used the RBC API.

## Challenges we ran into

Piecing the APIs together was probably our most difficult challenge. Besides learning the different APIs in general, integrating the different technologies got quite tricky when we are trying to do multiple things at the same time!

Besides API integration, definitely working without any sleep though was the hardest part!

## Accomplishments that we're proud of

Definitely our biggest accomplishment was working so well together as a team. Despite only meeting each other the day before, we got along extremely well and were able to come up with some great ideas and execute under a lot of pressure (and sleep deprivation!) The biggest reward from this hackathon are the new friends we've found in each other :)

## What we learned

I think each of us learned very different things: this was Homey and Alex's first hackathon, where they learned how to work under a small time constraint (and did extremely well!). Paige learned tons about React, frontend development, and working in a team. Vassily learned lots about his own strengths and weakness (surprisingly reliable at git, apparently, although he might have too much of a sweet tooth).

## What's next for Savvy Saver

Demos! After that, we'll just have to see :)
In the spirit of the team of Stanford researchers who, three years ago, pioneered the use of computer vision in helping diagnose melanoma, I worked on a way to make such computer aided diagnostics more available to the world. Google's Firebase platform provides a great way to make deep learning models such as this one scalable to the point of being usable on mobile devices.