Recycle Rush

Inspiration

Instead of the high prevalence slacktivisim, which derives from a social pressure to look superficially good through simply "liking" or "sharing of post", what about something that gauges people based on their non-superficial actions.

What it does

There are two main parts to this application. There is the social part where users can log in, donate to charity events, and receive recognition for their actions from all other users. Then there is the Machine Learning Tool that identifies what items can be recycled and its classifcation.

How we built it

We used MangoDB to store the charity event data, and Node.js for the backend. Offloaded to a python flask server, we use a constitutional neural network to identify images and recommend how to recycle them.

Challenges we ran into

Communicating between servers. Trying to use UIPath to web-scrape data.

Accomplishments that we're proud of

Rapidly prototyping a machine learning model in one hour. Creating a robust back-end. And communication of servers.

What we learned

Flask protocol, UIPath, MongoDB, Sockets.

What's next for Recycle Rush

Utilizing Capital One's API for secure donations. Domain for public access. Encryption of Database. An IPO $3.5 billion minimum only to be bought out by MySpace.

Built With

• flask
• javascript
• machine-learning
• mongodb
• node.js
• python
• socket.io