## Inspiration

Supreme brings spreadsheets into the modern era by enabling seamless interaction between spreadsheets and Python, D3, and other data science tools.

Excel is the most widely-used programming language in the world. While it doesn't look like other languages which developers use, its accessibility allows non-programmers to develop complex applications and models within spreadsheets. Its ease of use and discoverability is unparalleled and Supreme leverages that by building a full data science toolkit around Excel.

## How I built it

Supreme is built on top of the Beaker Notebook, which enables interactive computing with Python, JavaScript, and other languages. I built a plugin for Beaker Notebook which makes spreadsheets a full-fledged "language" on the platform, enabling data to seamlessly move between spreadsheets and programs.

## Use case

To demonstrate the power of this system, I did a sample analysis of FINRA short data which combines Supreme spreadsheets, Python, and D3.
## ✨ Inspiration

Driven by the goal of more accessible and transformative education, our group set out to find a viable solution. Stocks are very rarely taught in school and in 3rd world countries even less, though if used right, it can help many people go above the poverty line. We seek to help students and adults learn more about stocks and what drives companies to gain or lower their stock value and use that information to make more informed decisions.

## 🚀 What it does

Users are guided to a search bar where they can search a company stock for example "APPL" and almost instantly they can see the stock price over the last two years as a graph, with green and red dots spread out on the line graph. When they hover over the dots, the green dots explain why there is a general increasing trend in the stock and a news article to back it up, along with the price change from the previous day and what it is predicted to be from. An image shows up on the side of the graph showing the company image as well.

## 🔧 How we built it

When a user writes a stock name, it accesses yahooFinance API and gets the data on stock price from the last 3 years. It takes the data and converts to a JSON File on local host 5000. Then using flask it is converted to our own API that populates the ChartsJS API with the data on the stock. Using a Matlab Server, we then take that data to find areas of most significance (the absolute value of slope is over a certain threshold). Those data points are set as green if it is positive or red if it is negative. Those specific dates in our data points are fed back to Gemini and asks it why it is thinking the stock shifted as it did and the price changed on the day as well. The Gemini at the same time also takes another request for a phrase that easy for the json search API to find a photo for about that company and then shows it on the screen.

## 🤯 Challenges we ran into

Using the amount of API's we used and using them properly was VERY Hard especially making our own API and incorporating Flask. As well, getting Stock data to a MATLAB Server took a lot of time as it was all of our first time using it. Using POST and Fetch commands were new for us and took us a lot of time for us to get used too.

## 🏆 Accomplishments that we're proud of

Connecting a prompt to a well-crafted stocks portfolio.
learning MATLAB in a time crunch.
connecting all of our API's successfully
making a website that we believe has serious positive implications for this world

## 🧠 What we learned

MATLAB integration
Flask Integration
Gemini API

## 🚀What's next for StockSee

* Incorporating it on different mediums such as VR so users can see in real-time how stocks shift in from on them in an interactive way.
* Making a small questionnaire on different parts of the stocks to ask whether if it good to buy it at the time
* Use (MPT) and other common stock buying algorthimns to see how much money you would have made using it.
## Inspiration

We want to automate data science so that anyone can harness its power. There are many low-code drag & drop data science platforms such as Alteryx, but you still need some data science knowledge to use it.
We want to truly **fully automate data science** so that users can simply **tell Meshflow what they are interested in** (e.g. "why are my users leaving me?") and Meshflow will **create the entire data science workflow and perform the analysis** for you -- no code, and no drag & drop.

## What it does

We built a platform that automates the entire data science workflow for you. Tell Meshflow "show me which customers are churning. " and Meshflow will train a decision tree on your data to predict which customers are churning (i.e. leaving your company's product). Ask Meshflow "What do you think the stock price of Apple will be in one year?", and meshflow will gather historical stock prices, train a time series model, and make predictions.

## How we built it

We are not going to detail our exact algorithm, but on a high level, we allow large language models like GPT-3 to reason on a constrained framework so that it can produce robust data science flows. As for the frontend, we used react and in particular a library called react-flow that lets us create beautiful drag & drop flow charts. In the backend we use python and a bunch of large language models.

## Challenges we ran into

One thing super difficult is balancing the robustness of our algorithm and its flexibility. At the end we find a balance that lets Meshflow generates useful flows almost all the time while allowing for significant flexibility.

## Accomplishments that we're proud of

It works!

## What we learned

Hacking is fun

## What's next for Meshflow

Get users, test it, and launch it publicly.
On the technical side, we will improve our algorithm by aggregating data and improving the framework.