Project Name: Financial Forecasters
Using financial data from a variety of sources our group acquired useful and relevant features for Bitcoin trading. With these features a combination of Classification, Regression, and Clustering models were utilized to attempt a successful trading strategy for day-to-day trading of Bitcoin. This involved extensive feature engineering and analysis on the Time Series data as well as sentiment NLP analysis on relevant tweets. Although unsuccessful in producing a model that could be used for real-world trading, we were able to gain insight into useful features that can continue to be tuned for possible future use.
I have a curiosity for the entire data pipeline and am always eager to learn new things, trying to see where they may connect to my learned experiences. I believe the way forward for data will require similar habits of synchronization.
seeing how the disparate technologies and methodologies will synchronize in the coming years to create a more fluid and dynamic interaction with data.
San Antonio, TX or Remote Opportunities
Curious, determined, reliable
Collaborative, analytical, storytelling
