Comparing Global and Segment-Aware Machine Learning Models for Used Car Price Prediction

Background: Used car prices are influenced by model year, mileage, fuel type, transmission, engine size, engine power and market position. This study compares global and segment-aware machine learning approaches for used car price prediction using the CarDekho dataset from Kaggle. Methods: After 167 exact duplicate extras were removed, 15,244 records were evaluated. The primary analysis used ten sealed 80/20 outer splits. Segment topology was selected by three-fold cross-validation within each outer-training set, and fixed CatBoost/XGBoost expert profiles were combined through an XGBoost routing classifier. Global, hard-routing, soft-gating and oracle variants were compared. All target models used log1p prices and a three-fold out-of-fold Duan retransformation correction. Results: Across the full-market splits, global XGBoost achieved R²=0.8523±0.1120, RMSE=349,460±180,834 INR and MAE=104,138±5,274 INR. Soft gating achieved R²=0.8733±0.1007, RMSE=321,661±173,497 INR and MAE=99,196±5,479 INR. Paired Wilcoxon p values were 0.0039 for R² and RMSE and 0.0020 for MAE. In the seed-42 paired bootstrap, the R² and RMSE intervals included zero, whereas the MAE reduction remained supported. The secondary IQR-restricted analysis yielded almost identical mean R² and RMSE for global and soft-gated models, with a lower MAE for soft gating. Conclusion: Segment-aware modeling reduced average full-market error, but the size of the advantage varied across splits. The contribution is a controlled empirical comparison rather than a new learning algorithm.