Saudi Used Car Price Prediction with Machine Learning
A machine learning regression model trained on Saudi used car market features to deliver accurate price predictions.
Across used car listing data in Saudi Arabia, pricing is often influenced by individual guesswork rather than clear standards. This project brings a data-centered perspective, applying machine learning regression techniques to learn real pricing behavior and estimate fair car values.
The work captures all essential stages, from data cleaning and categorical encoding to exploratory analysis and model benchmarking, highlighting the strongest factors that shape a vehicle’s price. Model performance is validated using key error metrics, with top-performing methods such as XGBoost selected for inference and reuse.
With structured model artifacts and a scalable prediction pipeline, this work supports integration into online marketplaces, price recommendation systems, dealership tools, and potential deployment environments aimed at improving market transparency.
Saudi Used Car Price Prediction offers both analytical insight and practical price predictions to support more confident decision-making in the used car market.