Driver Delineation

Inspiration

Our team was inspired by StrataScratch's Datathon challenge, offering us the chance to conduct machine learning and data analysis/visualization for operational efficiency on real-world data for companies like Uber.

What it does

Our team has developed two distinct models that analyze the most useful factors to predict driver prospects for Uber.

How we built it

After preprocessing and cleaning up StrataScratch's dataset, we split up into two subteams that worked independently on two different models: one based on a Neural Network trained through technologies and libraries like PyTorch, and one based on a Decision Tree using scikit-learn.

Challenges we ran into

Training two models in the span of 12 hours was not easy, leading our group to face a variety of difficult to resolve challenges. These included deciding best practices in developing our software product using pair programming through services like VSCode liveshare, debugging issues with dependencies and resolving the various issues that come from the rigorous libraries used and time taken to train the models.

Accomplishments that we're proud of

Both models performed with impressive performance and accuracy, which were largely due to how successful both our subgroups worked within their respective teams.

What we learned

Our team learned how to quickly implement and test models, and additionally learned how to deal with larger datasets of data.

What's next for Driver Delineation

As we come to a close with this project, we hope to be able to fit more training data from different companies and different fields to our models, and be able to more accurately and efficiently determine which factors play a prominent role in prospective drivers.