We predict the role of fentanyl in opioid deaths. We are interested in how the opioid crisis is currently affecting America.


The opioid crisis has swept America and we were interested in the data behind the madness.

What it does:

We apply machine learning algorithms in order to predict if fentanyl will result in an overdose.

How we built it:

All of out back-end code is handled in Python. The front end of the application was created using HTML, CSS, Javascript and Jinja. The data from the back-end is sent to the front end via render calls in flask. The variable data is then output to HTML for the user to see.

Challenges we ran into:

We had difficulty choosing the right algorithm to make predictions. At first, we attempted an implementation of KNN, but the variance in the data, as well as a failed attempt at normalization made it quite difficult.

Accomplishments that we're proud of:

We are proud of bring able to implement a working machine learning algorithm. We are also happy to to create a webapp in python, which we have never done.

What we learned:

We learned how to use build web apps in python using flask, apply machine learning algorithms to data sets, and how to integrate Javascript into a python flask project.

What's next:

We need to get a hold of real-time data in order to predict upcoming overdoses.

Built with:

Python, Flask, Javascript, HTML, CSS, Pandas, Numpy, SciPy, Jinja

Prizes we're going for:

Call of Duty: Black OPS 4 (XBOX ONE)

$100 Amazon Gift Cards

Social Entrepreneurship Award

Hustle Award

Grand Prize

Jetbrains Pro Software

Misfit Shine 2

Fujifilm Instax Mini 26

Team Members

Brenno Ribeiro, Cameron LaFreniere, Emiton Alves, Matthew Silva
View on Github