## Inspiration

We were inspired by the numerous Facebook posts, Slack messages, WeChat messages, emails, and even Google Sheets that students at Stanford create in order to coordinate Ubers/Lyfts to the airport as holiday breaks approach. This was mainly for two reasons, one being the safety of sharing a ride with other trusted Stanford students (often at late/early hours), and the other being cost reduction. We quickly realized that this idea of coordinating rides could also be used not just for ride sharing to the airport, but simply transportation to anywhere!

## What it does

Students can access our website with their .edu accounts and add "trips" that they would like to be matched with other users for. Our site will create these pairings using a matching algorithm and automatically connect students with their matches through email and a live chatroom in the site.

## How we built it

We utilized Wix Code to build the site and took advantage of many features including Wix Users, Members, Forms, Databases, etc. We also integrated SendGrid API for automatic email notifications for matches.

## Challenges we ran into

## Accomplishments that we're proud of

Most of us are new to Wix Code, JavaScript, and web development, and we are proud of ourselves for being able to build this project from scratch in a short amount of time.

## What we learned

## What's next for Runway
# cheqout

A project for QHacks 2018 by [Angelo Lu](https://github.com/angelolu/), [Zhijian Wang](https://github.com/EvW1998/), [Hayden Pfeiffer](https://github.com/PfeifferH/) and [Ian Wang](https://github.com/ianw3214/). Speeding up checkouts at supermarkets by fitting existing carts with tech enabling payments.

### Targeted Clients: Established supermarket chains

Big box supermarkets with established physical locations, large inventories and high traffic, such as Walmart and Loblaw-branded stores.

### Problems Identified: Slow checkouts

Checkout times can be slow because they are dependent on available checkout lanes and influenced by factors such as the number of items of the people in front of you and speed of the cashier and the person in scanning and paying. Stores with consistently slow checkouts are at risk of losing customers as people look for faster options such as other nearby stores or online shopping.

### Possible Solutions

1. **Additional checkout lanes**
This solution is limited by the physical layout of the store. Additional checkout lanes require additional staff.
2. **Self-checkout kiosks**
This solution is also limited by the physical layout. Lines can still develop as people must wait for others to scan and pay, which may take a long time.
3. **Handheld self-scanners**
In supermarkets, products such as produce, and bulk foods are weighed, which cannot be processed easily with this solution.
4. **Sensor-fusion based solution replacing checkouts (Ex. Amazon Go)**
In large chain supermarkets with many established locations, implementation of all the cameras, scales and custom shelving for the system requires massive renovations and is assumed to be impractical, especially considering the number of products and stores.

### Proposed Solution: “cheqout”, Smart Shopping Carts

The solution, titled “cheqout” involves fitting existing shopping carts with a tray, covering the main basket of the cart, and a touchscreen and camera, on the cart handle.
A customer would take out a cart upon entry and scan each items’ barcode before placing it in the cart. The touchscreen would display the current total of the cart in real time. Once the customer is ready to pay, they can simply tap “Check Out” on the touchscreen and scan a loyalty card (virtual or physical) with an associated payment method or tap their credit or debit card directly. Alternatively, the customer can proceed to a payment kiosk or traditional checkout if they do not have an account, are paying in cash or want a physical receipt, without having to wait for each item to be scanned again.
On the way out, there would be an employee with a handheld reader displaying what has been paid in the cart to do a quick visual inspection.
This solution trusts that most users will not steal products, however, a scale integrated in the tray will continuously monitor the weight of products in the cart and the changes in weight associated with an item’s addition/removal and prompt accordingly. The scale will also be used to weigh produce.
This solution allows for the sale of weighed and barcoded items while still decreasing checkout line congestion. It is scalable to meet the requirements of each individual store and does not require the hiring of many additional personnel.

### Challenges

* Time: This project is being built for QHacks, a 36-hour hackathon
* Technical: The scale and touch screen remain uncooperative, prompting temporary fixes for the purpose of demonstration

### Possible Future Features/Value-Added Features

* Information about traffic within the store
By implementing indoor location tracking, analysts can visualize where customers frequent and tailor/adjust product placement accordingly
* Information about product selection
The system can record a customer’s decision making process and uncertainties based on how long they spend in one spot and if they add, remove or swap items from their cart
## Inspiration

A common skill one needs in business management is the ability to know how the customer feels and reacts to a system of services provided by the business in question. Thus, having computers in this day and age make it an essential tool for analyzing these important sources of customer feedback. Automatically making a machine gather uncoerced customer "feedback" data can easily indicate how the last few interactions were for the customer. Making this tool accessible was our inspiration behind this project.

## What it does

This web application gathers data from Twitter, Reddit, Kayak, TripAdvisor and Influenster at the moment with room to expand into many more social review websites. The data it gathers from these websites are represented as graphs, ratios and other symbolic representations that help the user easily conclude how the company is perceived by its customers and even compare it to how customers perceive other airline companies as well.

## How we built it

We built it using languages and packages we were familiar with, along with packages we did not know existed before yHacks 2019. An extremely careful design process was laid out well before we started working on the implementation of the webApp and we believe that is the reason behind its simplicity for the user. We prioritized making the implementation as simple as possible such that any user can easily understand the observations of the data.

## Challenges we ran into

Importing and utilizing some packages did not play well with our implementation process, thus we had to make sure we covered our design checklist via working around the issues we ran into. This includes building data scrapers, data representers and other packages from scratch. This issue increasing became prominent the more we pressed on making the webApp user-friendly as more functions and code had to be shoveled in the back-end.

## Accomplishments that we're proud of

The data scrapers and representative models for collected data are accomplishments we're most proud of as they are simple yet extremely effective when it comes to analyzing customer feedback. In particular, getting data from giant resources of customer reactions such as TripAdvisor, Reddit and Twitter make the application highly relevant and effective. This practical idea and ease of access development we implemented for the user is what we are most proud of.

## What we learned

We learned a lot more about several of the infinite number of packages available online. There is so much information out on the internet that these 2 continuous days of coding and research have not even scratched the surface in terms of all the implementable ideas out there. Our implementation is just a representation of what a final sentiment analyzer could look like. Given there are many more areas to grow upon, we learned about customer feedback analysis and entrepreneur skills along the way.

## What's next for feelBlue

Adding more sources of data such as FaceBook, Instagram and other large social media websites will help increase the pool of data to perform sentiment analysis. This implementation can even help high-level managers of JetBlue decide which area of service they can improve upon! Given enough traction and information, feelBlue could even be used as a universal sentiment analyzer for multiple subjects alongside JetBlue Airlines! The goals are endless!