Inspiration
It all started at EagleHack, where we set out to build something impactful with machine learning—despite having limited time. Our team had a rough idea of what we wanted: a tool that could analyze user-submitted data and return meaningful results. The challenge? Finding an efficient way to build a machine learning model quickly and deploy it in a user-friendly way.
Finding the Right Model
Instead of training a model from scratch—an unrealistic goal given the time constraints—we scoured online repositories and found a pre-trained ML model that fit our needs. It wasn’t perfect, but it provided a solid foundation. After some testing and modifications, we had a working model that could process input data and generate predictions.
Building the Website
Once we had the ML model in place, we needed a way for users to interact with it. That’s when we came up with the idea of a simple web app where users could submit their data and receive instant results. Using Flask for the backend and a lightweight frontend, we quickly spun up a prototype. The website took inputs, processed them with our ML model, and displayed the results in real-time.
The Final Sprint
With the deadline approaching, we fine-tuned the system, handled edge cases, and made sure the site was easy to use. In the final moments of the hackathon, we deployed the project, ran some last-minute tests, and prepared for the demo.
The Outcome
We had built something functional in limited time, proving that with the right approach, even a hackathon-sized project can be both ambitious and achievable.
