End-to-End Explainable Stacked Ensemble Framework for Used Bike Price Prediction Using Real-World Marketplace Data

The significant research contribution of this study is the development of an explainable stacked ensemble framework for used-bike price prediction using real-world marketplace data from Bangladesh. The proposed framework integrates XGBoost, LightGBM, CatBoost, and HistGradientBoosting as base learners with Ridge regression as the meta-learner, supported by a comprehensive feature-engineering strategy that produces 28 predictors. Using 6,697 cleaned listings from Bikroy.com, the proposed model achieved an R^2 of 0.8759, RMSE of 0.2456, and MAE of 0.1712, outperforming all individually evaluated models. Furthermore, SHAP-based analysis provides interpretability by identifying engine capacity, vehicle age, and the brand–engine capacity interaction as major pricing factors. Finally, the complete framework was deployed as a 36 MB Android application using ONNX models, enabling on-device price prediction without cloud computation.