Climate change is one of the most pressing global challenges of the 21st century, with carbon dioxide (CO2) emissions identified as the primary driver of global warming. Understanding what factors drive national-level emissions is critical for designing effective climate policy. Countries differ substantially in their per-capita emissions, and these differences are shaped by economic scale, energy systems, and fossil fuel dependence. This project analyses the Our World in Data (OWID) CO2 and Greenhouse Gas Emissions dataset to investigate the structural factors that explain variation in CO2 emissions per capita across countries from 1990 to 2024. We apply regression to quantify the relationship between emissions and their key drivers, and classification to group countries into low, medium, and high emission intensity categories.

The US Cancer Statistics Explorer is an interactive Shiny application that allows users to: - Explore estimated new cancer cases by state - Explore estimated cancer deaths by state - Compare cases and deaths visually - Calculate an estimated death rate for selected cancers The application was created to make national cancer statistics easier to explore through an interactive interface.

This project develops a predictive text model using Natural Language Processing techniques. The application predicts the next word based on user input and is deployed as a Shiny app. The project uses the SwiftKey dataset and demonstrates text mining, data analysis, and language modeling

Exploratory analysis of the SwiftKey text prediction dataset including blogs, news, and Twitter data. This report summarizes key findings and outlines the prediction model plan.

Exploratory analysis of the SwiftKey dataset for the Johns Hopkins Data Science Specialization Capstone Project.

Module 15 for GEOG 6680 at the University of Utah