This section shows and details a sentiment analysis of the scraped presidential acceptance speech data. It also coincides with my personal blog on gritperformance.home.blog. I referenced this Kaggle lesson for help: https://www.kaggle.com/rtatman/tutorial-sentiment-analysis-in-r.

For our final project in Data Mining 320, we were given the Treatment Episode Data Set Admission from 2018. My group and I used this data and classified states based on the legislation of cannabis. Our goal was to look for any effects or outcomes that cannabis legislation had on patients admitted to rehab. We were able to use any measure we wanted or felt appropriate but choose a Naive Bayes approach.

This code examines transactional grocery store data. I attempt to examine which products are bought together and get a general understanding of market basket analysis.

This project uses PITCHf/x data to examine the Whiff tendencies of the 2017 Houston Astros. Starting on May 19th we found discrepancies on fastball pitch types. The logistic regressions predicted between 77 and 91 percent of outcomes and we determined that the Astros were 1.24-1.30 times more likely to swing and miss on fastballs away from home than at home