This project explores the selling price of used cars by analyzing several influencing factors such as price bins, drive wheel types, engine size, and body style. Using Python, the project preprocesses the data and employs various visualization techniques, including scatter plots, heatmaps, and regression plots, to examine relationships and trends. The goal is to understand how these factors correlate with car prices, providing valuable insights into pricing patterns and market behavior.
The visualizations generated include a bar plot of price distributions across different bins, a boxplot comparing prices by drive wheel types, and a scatterplot showing the relationship between engine size and price. Additional visualizations include a heatmap that highlights patterns between drive wheels and body styles, and a regression plot that confirms a positive correlation between engine size and car price. These analyses help in identifying key factors that affect used car prices and assist in making informed pricing decisions.