Analyzed a 1,610-row lifestyle dataset (diet, exercise, and habits) to understand patterns linked to obesity. Built logistic regression models around physical activity, fast-food intake, and vegetable consumption, highlighting key lifestyle factors. Notably identified reverse causality effects and validated insights against the raw data.

Analyzed 10 seasons (2014–2024) of NBA schedule data to measure the impact of travel and game density on team performance. Built interactive R visualizations (ggplotly, geosphere) to highlight back-to-backs, 4-in-6 stretches, and excessive travel periods. Designed a regression model to estimate wins gained or lost due to schedule factors, identifying the most advantaged and disadvantaged teams. Delivered a professional brief comparing Oklahoma City’s 2024–25 draft schedule with Denver’s, outlining key stretches and recovery strategies.

In this project, we use multiple linear regression to model a car’s fuel efficiency (measured in miles per gallon, MPG) based on two key features: weight and horsepower. Using the built-in mtcars dataset in R, we explore how these variables relate to MPG, visualize the relationships with ggplot2 and plotly, and interpret the regression output. Our final model explains approximately 83% of the variation in MPG, showing that heavier and more powerful cars tend to have lower fuel efficiency.