Projects

Machine Learning and Natural Language Processing

Machine Learns the Sermon Topic: A supervised learning project to determine if the main topic of a speaker can be identified.
Machine Learns the Sermon Speaker: A supervised learning project to determine if the speaker of a sermon can be identified.

packages: sklearn, nltk, matplotlib, BeautifulSoup, Selenium

Does Church and State Correlate?: An EDA Project to see if correlations exists between World Events, Sermon Writers, and ancient scripture.
March Madness Gladness? (The Python Version): Is there a model that can predict the Final Four teams in the Men’s NCAA Basketball tournament (also known as March Madness)? This project tested many team related variables available to use to see if a model could be built (Using Python).
Bureau of Labor Statistics - Employment, Hours, and Earnings - A statistical exercise: A statistical look into the wonderful world of the Bureau of Labor Statistics.

packages: sklearn, nltk, sqlite3, matplotlib

March Madness Gladness? (The R Version): Is there a model that can predict the Final Four teams in the Men’s NCAA Basketball tournament (also known as March Madness)? This project tested many team related variables available to use to see if a model could be built (Using R).

packages: ggplot2, dplyr, readxl, psych

Predicting the Next Best NFL QB: Using supervised learning and statistical analysis, a model was created to predict the success of a football quarterback in the NFL based on their college statistics.
Predicting the Next NBA All-Star: Using supervised learning and statistical analysis, a model was created to predict NBA All stars in future years based on their previous 3 years in the league.
Predicting the Best Night to Increase Baseball Attendance: Using linear regression, a model was created to determine a (ficticious) marketing promotion strategy for increasing baseball game attendance.

packages: sportsipy, pandas, matplotlib, nba_api, sklearn

Where the GOAT’s Roam: A visualization analysis of where two of the NBA’s best scorers score in the twilight of their careers.

packages: sklearn, nba_api, pandas, matplotlib