부산대학교 도서관

The pre-owned car market has experienced a significant growth within the automotive industry, fueled by factors such as affordability, improved vehicle reliability, accurate valuation of pre-owned cars holds pivotal importance for both buyers and sellers, directly influencing pricing decisions. This research paper seeks to develop a model to predict the correct value of pre-owned cars by employing the potent Random Forest algorithm, renowned for its efficiency in handling intricate regression problems. The study leverages a comprehensive dataset encompassing diverse vehicle attributes to train and assess the performance of the model. The output derived from this study will empower stakeholders in the automotive industry to make proper decisions when buying or selling pre-owned vehicles.