This document looks at network analysis and tries to answer the following question, "Do you like to work with your coworker?" This data came from the sociogram-employees-un.csv that originated from the R-Exercises website and also drew on techniques that were mentioned in chapter 20 of the reading.

This document looks at sentiment analysis, and in particular, compares speeches from the 2020 Election. These speeches compared the sentiment for Joe Biden and Donald Trump.

This document looks at market basket analysis for the groceries.csv. In addition to this, it draws on topics mentioned in chapter 16.

This document looks at the clustering of houses for the Mount_Pleasant_Real_Estate.csv and the HosuePrices.csv. In addition to this, it also looks at topics mentioned in chapter 15.

This document looks at the creation of decision trees through the use of CART for the LoaData.csv and looks at how a loan's credit grade is classified.

This document looks at topics covered in chapters 10 and 12. In particular, it looks at naive bayesian analysis and discriminant analysis

This document looks at the FuelEconomy.csv and uses the k nearest neighbor algorithm (KNN), to accurately classify fuel type in a training set. This document also used topics and techniques that were used in chapter 9.

This document looks at the EmployeeSatisfaction.csv and uses topics and techniques learned in chapter 8. In particular, it looks at sensitivity and specificity and binomial classification.

This document looks at DirectMarketing.csv and checking to see how parsimony and false discovery rates impact models. This document also uses techniques learned in chapter 5.

This document looks at the LoanData.csv and uses topics and techniques learned in chapter 7.

This document looks at topics and techniques learned in chapter 3.

This document looks at housing prices and uses techniques learned in Chapter 2.

This document looks at data that was found on https://dasl.datadescription.com /datafile/lobsters-2016. Specifically, this document examines if there is between Pounds in Millions and Year, and if there is one between Value in Millions and Year too.

This document looks at data taken from Box Office Mojo. Specifically, it looks to see if there is a relationship between Average Ticket Price and Total Gross, and if there is one between Average Ticket Price and Tickets Sold too.

This document describes how a word cloud was created from tweets related to climate change.