Tired of missing flights and losing luggage while traveling, we came up with a real world solution for airlines to optimize their loading times and for checking and storing bags in their aircraft.

What it does

Our application, uses machine learning algorithms to scan baggage and automatically assign a spot for it. Our algorithm dynamically balances the aircraft, so the aircraft spends less time on the runway and avoid possible delays due to baggage.

How we built it

We built our application using react, HTML and CSS for the front-end. We used python for the back-end programming.

Challenges we ran into

One of the biggest challenges we ran into was having Microsoft API differentiate suitcases. Currently Microsoft cognitive search API only differentiates facial images. We overcame this challenge by training our own machine learning model.

Accomplishments that we're proud of

We are proud of creating a real world solution, that utilizes new technologies and methods.

What we learned

We learned how to collaborate in a fast paced team environment. Also we learned how to bring our raw ideas to real life from brainstorming to designing and developing.

What's next for FLYIO

Moving on forward were planning on training our machine learning model with larger data sets so it can successfully differentiate all bags

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