## 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

Data analytics can be **extremely** time-consuming. We strove to create a tool utilizing modern AI technology to generate analysis such as trend recognition on user-uploaded datasets.The inspiration behind our product stemmed from the growing complexity and volume of data in today's digital age. As businesses and organizations grapple with increasingly massive datasets, the need for efficient, accurate, and rapid data analysis became evident. We even saw this within one of our sponsor's work, CapitalOne, in which they have volumes of financial transaction data, which is very difficult to manually, or even programmatically parse.

We recognized the frustration many professionals faced when dealing with cumbersome manual data analysis processes. By combining **advanced machine learning algorithms** with **user-friendly design**, we aimed to empower users from various domains to effortlessly extract valuable insights from their data.

## What it does

On our website, a user can upload their data, generally in the form of a .csv file, which will then be sent to our backend processes. These backend processes utilize Docker and MLBot to train a LLM which performs the proper data analyses.

## How we built it

Front-end was very simple. We created the platform using Next.js and React.js and hosted on Vercel.

The back-end was created using Python, in which we employed use of technologies such as Docker and MLBot to perform data analyses as well as return charts, which were then processed on the front-end using ApexCharts.js.

## Challenges we ran into

* It was some of our first times working in live time with multiple people on the same project. This advanced our understand of how Git's features worked.
* There was difficulty getting the Docker server to be publicly available to our front-end, since we had our server locally hosted on the back-end.
* Even once it was publicly available, it was difficult to figure out how to actually connect it to the front-end.

## Accomplishments that we're proud of

* We were able to create a full-fledged, functional product within the allotted time we were given.
* We utilized our knowledge of how APIs worked to incorporate multiple of them into our project.
* We worked positively as a team even though we had not met each other before.

## What we learned

* Learning how to incorporate multiple APIs into one product with Next.
* Learned a new tech-stack
* Learned how to work simultaneously on the same product with multiple people.

## What's next for DataDaddy

### Short Term

* Add a more diverse applicability to different types of datasets and statistical analyses.
* Add more compatibility with SQL/NoSQL commands from Natural Language.
* Attend more hackathons :)
### Long Term
* Minimize the amount of work workers need to do for their data analyses, almost creating a pipeline from data to results.
* Have the product be able to interpret what type of data it has (e.g. financial, physical, etc.) to perform the most appropriate analyses.
## Inspiration

There should be an effective way to evaluate company value by examining the individual values of those that make up the company.

## What it does

Simplifies the research process of examining a company by showing it in a dynamic web design that is free-flowing and easy to follow.

## How we built it

It was originally built using a web scraper that scraped from LinkedIn which was written in python. The web visualizer was built using javascript and the VisJS library to have a dynamic view and aesthetically pleasing physics. In order to have a clean display, web components were used.

## Challenges we ran into

Gathering and scraping the data was a big obstacle, had to pattern match using LinkedIn's data

## Accomplishments that we're proud of

It works!!!

## What we learned

Learning to use various libraries and how to setup a website

## What's next for Yeevaluation

Finetuning and reimplementing dynamic node graph, history. Revamping project, considering it was only made in 24 hours.