## Inspiration

Our inspiration for Credit Search would be having one platform that helps you decide on which credit cards would best fit your requirements for your needs.

## What it does

It will help the user to decide the best credit card with their spending habits and preferences.

## How we built it

We planned out Credit Search by creating a rough sketch of the project and the functionality of it. During the planning, we include what software and technology such as HTML, CSS, and JavaScript for the front-end, python and flask for the back-end and MySQL for the database.

## Challenges we ran into

The challenges that we ran into would be creating a data set using MySQL. we were creating a database that is to retain the information of the cards and the owner information. After figuring out another solution, we ran into the issue with AWS because it is a technology we are not familiar with. We end up not using because we did not have enough time to figure out the issues with it.

## Accomplishments that we're proud of

We are proud of creating the pages for our website while using a new software called Flask.

## What we learned

We learned the application of flask while doing this project and the use of creating a table in MySQL.

## What's next for Credit Search

The next thing for Credit Search would be adding the data using AWS and creating a functionality that allows you to pay off all your credit card payment in one go instead of going through the company one by one.
## Inspiration

Motivated by the all too well-known feeling of being broke, we wanted to develop a web application that would help university students tackle on crippling student debt.

## What it does

*Bill Splitting*
For when you want to split the grocery bill your housemates or only want to order **half** the pizza and tracks when your friend has "forgotten" to pay you back afterwards.

*Budgetting*
Manage the millions of bills and subscriptions that you may have forgotten about, complete with category sorting and shared expense tracking.

*Goal Setting*
Always wanted to go on that exotic trip or save for that car you always wanted? Calculate how much and for how long you'll need before your dream comes true, or you could go buy another drink...

## How we built it

Using Django framework, we were able to develop a web application to put together the various features in mind and the addition of bootstrap for CSS flair.

## Challenges we ran into

From simple bugs, Django specific errors to database design decisions where we spent a little more time than we wanted to, but in the end, we managed to get something presentable enough and something that we are proud of.

## Accomplishments that we're proud of

Designing a database where both users of the payee and payor can see what incoming or outcoming debts have been paid or needs to be paid that can be confirmed by the payee whether they received the money or not.

## What we learned

More Django knowledge and ways to avoid syntax errors for any future projects.

## What's next for Broker Than Me

Utilizing any graphing API's to show the user in a more visual sense what their budget entails.
## 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.