## What Inspired Us

A good customer experience leaves a lasting impression across every stage of their journey. This is exemplified in the airline and travel industry. To give credit and show appreciation to the hardworking employees of JetBlue, we chose to scrape and analyze customer feedback on review and social media sites to both highlight their impact on customers and provide currently untracked, valuable data to build a more personalized brand that outshines its market competitors.

## What Our Project does

Our customer feedback analytics dashboard, BlueVisuals, provides JetBlue with highly visual presentations, summaries, and highlights of customers' thoughts and opinions on social media and review sites. Visuals such as word clouds and word-frequency charts highlight critical areas of focus where the customers reported having either positive or negative experiences, suggesting either areas of improvement or strengths. The users can read individual comments to review the exact situation of the customers or skim through to get a general sense of their social media interactions with their customers. Through this dashboard, we hope that the users are able to draw solid conclusions and pursue action based on those said conclusions.

Humans of JetBlue is a side product resulting from such conclusions users (such as ourselves) may draw from the dashboard that showcases the efforts and dedication of individuals working at JetBlue and their positive impacts on customers. This product highlights our inspiration for building the main dashboard and is a tool we would recommend to JetBlue.

## How we designed and built BlueVisuals and Humans of JetBlue

After establishing the goals of our project, we focused on data collection via web scraping and building the data processing pipeline using Python and Google Cloud's NLP API. After understanding our data, we drew up a website and corresponding visualizations. Then, we implemented the front end using React.

Finally, we drew conclusions from our dashboard and designed 'Humans of JetBlue' as an example usage of BlueVisuals.

## What's next for BlueVisuals and Humans of JetBlue

* collecting more data to get a more representative survey of consumer sentiment online
* building a back-end database to support data processing, storage, and organization
* expanding employee-centric

## Challenges we ran into

* Polishing scraped data and extracting important information.
* Finalizing direction and purpose of the project
* Sleeping on the floor.

## Accomplishments that we're proud of

* effectively processed, organized, and built visualizations for text data
* picking up new skills (JS, matplotlib, GCloud NLP API)
* working as a team to manage loads of work under time constraints

## What we learned

* value of teamwork in a coding environment
* technical skills
## 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.