The cool dudes at Finra dressed up as superheroes. Thinking about how to design and manage such large sets of data was also something the team wanted to do.

What it does

Determining who's potentially part of the financial "bad guys" using a given dataset.

How we built it

Parsed a large amount of data with Python, storing it into a MongoDB database. This data was also parsed in Python.

Challenges we ran into

The Wi-Fi was spotty so it was difficult to properly set up our environment. Additionally, since the dataset was so large it took a substantial amount of time to process, which made testing somewhat difficult. Finally, trying to decide what data was relevant to our model was not very easy.

Accomplishments that we're proud of

We're proud that we got to learn about parsing and dealing with large datasets and building an efficient graph using Python.

What we learned

Lots of Python, and that we can drink a lot of RedBull.

What's next for Suspicious Factors

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