I conducted a data analysis project on the autos.csv dataset from eBay Kleinanzeigen to explore factors influencing used car prices. Using Python with Pandas, Matplotlib, and Seaborn, I cleaned the dataset by removing unrealistic price and mileage values, handled missing data, and focused on key features. The analysis revealed that Volkswagen, BMW, and Mercedes-Benz are the most common brands, with luxury brands like BMW, Audi, and Mercedes showing higher resale values. Mileage showed a strong negative correlation with price, while newer cars retained more value than older ones. Additionally, diesel vehicles tended to have higher resale prices compared to petrol cars. This project highlights my skills in data cleaning, exploratory data analysis, and visualization while deriving actionable business insights from real-world data.