This project explores patterns in vehicle insurance claims using a real-world dataset. Through data cleaning, visualization, and statistical exploration in R, I analyzed claim frequency and severity across variables such as vehicle usage, type, manufacturer, insured values, and premiums. Key insights include identifying high-risk vehicle categories, understanding the distribution of claim amounts (on a log scale), and comparing average premiums and claims by usage. The analysis demonstrates how exploratory data work can support risk assessment and underwriting decisions, aligning with core principles of property and casualty insurance. Tools used: R, dplyr, ggplot2, plotly, tidyr