I am a recent graduate of the Masters of Data Science program at the University of San Francisco. Prior to joining USF, I was an undergraduate Research Assistant in a Solar Forecasting Lab at The Texas Sustainable Energy Research Institute. Some of my interests include Computer Vision, Machine Learning, Time Series and Geospatial Analysis applied to various domains including renewable energy, utilties, and IoT sensing.
I am proficient in Python 3.7+, Apache Spark, and SQL
For the development of my projects I utilize Git, vscode and AWS
I use the Mac OS
Integrated API calls into our codebase as well as set up a PostgreSQL database to store all user signup information. I used Google Analytics to monitor and analyze network data of our website to determine if we were meeting certain goals. Other Duties: Front-End design using Bootstrap4, Back-End support using Flask.Go to Website
Analyzed the feature importance of an XGBoost Model to determine whether or not a relationship existed between number of opioids in a geographical region and the suicide rate in that region.Check it out
Compared various traditional time series models to determine which forecasted the best median home price. A Trend-Exponential Smoothing model performed the best amongst an analysis of 17 models.Check it out
Through data visualization we analyzed Social Media Ad sentiment and frequencies leading up to both the primary and general election dates of the 2016 US Presidential Election.Check it out