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Identifying Factors That Lead To Injury In The Nfl, Matthew Toner
Identifying Factors That Lead To Injury In The Nfl, Matthew Toner
Honors Projects in Data Science
This study hypothesizes that injury-causing factors can be identified through training machine learning models with NFL injury data. The machine learning process entailed web scraping, pre-processing, cleaning, modeling, and analyzing NFL injury data to identify these factors. The features used to model injuries included the following: games played, games started, weight, height, age, year, years of experience, starting position, and team. The four models used to model NFL injuries were Logistic Regression, Decision Trees, Random Forests, and Gradient Boosted Trees. The model with the best performance was the Gradient Boosted Trees model, with an F1 score of 0.508. In addition, …
Twitter's Role In An Increasingly Polarized Political Climate; A Look Into The 2020 Us Elections, Leanne Kendall
Twitter's Role In An Increasingly Polarized Political Climate; A Look Into The 2020 Us Elections, Leanne Kendall
Honors Projects in Data Science
Amidst politically strained times, one might wonder what has cause such an exaggerated gap between the views of democrats and republicans. For years, research has suggested the US’s voting population is becoming increasingly politically polarized, with one of the causes being social media. This study's purpose is to understand more about the role that social media plays in the polarization of parties in the US. The study is comprised of the analysis of over 3,000,000 tweets from 9/22/2020 through 11/10/2020 that mention or are written by senate and presidential candidates. Natural language processing, network graphing, and sentiment analyses were utilized …
Cancel Culture: Who Or What Will Be Next?, Christine Trumper
Cancel Culture: Who Or What Will Be Next?, Christine Trumper
Honors Projects in Data Science
This paper utilizes Data Science and Applied Statistic techniques, to perform an analytical dive into Cancel Culture as it is referenced and used on Twitter. The research focuses on analyzing how Cancel Culture has affected the sentiment of Twitter, specifically how it impacts prominent topics in the media that have occurred between February 2021 to September 2021. The development of a topic and sentiment analysis will be based on 1,302,844 Tweets collected using Twitter’s API. Cancel Culture became popularized on social media in the past few years and there is little concrete information regarding its process and the demographics it …
Analyzing Yankees And Red Sox Sentiment Over The Course Of A Season, Connor Koch
Analyzing Yankees And Red Sox Sentiment Over The Course Of A Season, Connor Koch
Honors Projects in Data Science
This paper investigates data collected on twitter which references the Yankees or Red Sox during the 2020 Major League Baseball (MLB) season. The objective is to analyze the sentiment of tweets referencing the Yankees and Red Sox over the course of the season. In addition, an investigation of the networks within the data and the topics that were prevalent will be conducted. The 2020 MLB season was started late because of the COVID-19 pandemic and was a season like no other. The expectation of a dataset revolving around baseball is that the topics discussed would be about baseball. The findings …