Predicting the Sale Price of Pre-Owned Vehicles with the Ensemble ML Model

Abstract

Car price forecasting is a popular study topic because it requires a lot of work and knowledge. Used car pricing forecasting is a major auto industry concern. Machine learning can accurately predict used automobile prices based on many characteristics. Many distinct qualities are considered for accurate predictions. The suggested model uses a dataset that contains vehicle brand and model, year of production, mileage, condition, and other factors that affect used car prices. This study used linear regression, GBT regression, and random forest regression to estimate secondhand car prices. Then, algorithm performance was compared to find which method better fit the data set. Thus, these methods outperform others.