## Inspiration

With the world in a technology age, it is easy to lose track of human emotions when developing applications to make the world a better place. Searching for restaurants using multiple filters and reading reviews is often times inefficient, leading the customer to give up searching and settle for something familiar. With a more personal approach, we hope to connect people to restaurants that they will love.

## What it does

Using Indico’s machine learning API for text analysis, we are able to create personality profiles for individuals and recommend them restaurants from people of a similar personality.

## How we built it

Backend: We started with drafting the architecture of the application, then defining the languages, frameworks and API's to be used within the project. We then proceeded on the day of the hackathon to create a set of mock data from the Yelp dataset. The dataset is then imported into MongoDB and managed through Mlab. In order to query the data, we used Node.js and Mongoose to communicate with the database.

Frontend: The front end is built off of the semantic ui framework. We used default layouts to start and then built on top of them as new functionality was required. The landing page was developed from something a member had done in the past using modernizer and bootstrap slideshow functionality to rotate through background images. Lastly we used ejs as our templating language as it integrates with express very easily.

## Challenges we ran into

1. We realized that the datasets we've compiled was not diverse enough to show a wide range of possible results.
2. The team had an overall big learning curve throughout the weekend as we all were picking up some new languages along the way.
3. There was an access limit to the resources that we were using towards testing efforts for our application, which we never predicted.

## Accomplishments that we're proud of

1. Learning new web technologies, frameworks and APIs that are available and hot in the market at the moment!
2. Using the time before the hackathon to brainstorm and discussing a little more in depth of each team member's task.
3. Collaboratively working together using version control through Git!
4. Asking for help and guidance when needed, which leads to a better understanding of how to implement certain features.

## What we learned

Node.js, Mongoose, Mlab, Heroku, No SQL Databases, API integration, Machine Learning & Sentiment Analysis!

## What's next for EatMotion

We hope that with our web app and with continued effort, we may be able to predict restaurant preferences for people with a higher degree of accuracy than before.
Our desire to build this easy-to use customer interface was inspired by our own personal past experiences. A lot of times, we move to different cities and places and hesitate to talk to new people. Meet and Eat have emerged from the desire to mingle around with new people in a group at places we are fond to explore.
Our main learning during this journey was the importance of getting to know and eventually leveraging each other's unique backgrounds and skills. Throughout this journey, we used cutting-edge web technologies to power our application. This includes HTML5, CSS3, Javascript and Swift. There were many important business challenges that we faced such as "How to best differentiate our application from other solutions currently existing on the market" and "How to create a User Interface and Experience that is not only simple but also intuitive to work with". We worked out various designs in the wireframing stages, which we are very proud of as a group (given this was our first hackathon). We are looking forward to hear your feedback.
## 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.