This project presents a comprehensive analysis of the built-in mtcars dataset in R, focusing on fuel efficiency (miles per gallon, or MPG). The workflow includes descriptive statistics, exploratory visualizations, and the application of machine learning models—specifically linear regression and random forest—to predict MPG based on vehicle attributes such as weight, horsepower, transmission type, and engine specifications. The report demonstrates that random forest outperforms linear regression in predictive accuracy and highlights key variables that influence fuel consumption.