We were given large data sets by Finra, the data included info about Brokers who had been banned from stock trading. However we noticed that many become financial advisors. How do we know we can trust them?

What it does

Rates and determines if a financial advisor is good or bad depending on financial and legal data that well have run through our machine learning model.

How I built it

Its with google app engine, its a flask web app that uses Google Cloud Storage as its blob store and big query to parse the data along with machine learning apis like SCIKIT-Learn and NLP

Challenges I ran into

Understanding the relationship between the data sets provided for the ex-brokers and understand the relationships between the schemas for the court data. In addition, understanding the implementation of the machine learning libraries and the Google Cloud NLP API

Accomplishments that I'm proud of

Finishing the project, and learning more about machine learning and correlations

What I learned

Google Cloud and machine learning techniques.

What's next for Trusade

Expanding the data set to update in real time.

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