Optimizing Used Car Valuation with AI: A Predictive Modeling Approach

The used car marketplace matures into a vital part of the automotive industry by supplying affordable transportation solutions to buyers plus enabling sellers to sell their former vehicles. Multiple factors including vehicle age and mileage as well as fuel type and market volatility produce challenges for exact used car price forecasting. This research presents a complete machine learning model approach for used vehicle pricing estimation through Linear Regression alongside Decision Trees and Random Forest and K-nearest neighbors (KNN) and XGBoost and Stacking models. The research used more than 6000 samples which received thorough pre-processing treatment through both feature engineering techniques and missing value management. The prediction accuracy in this research was improved through a hyperparameter fine-tuning process. The RMSE score of XGBoost reached 0.240 delivering outstanding results. The achieved result shows the capability of accurate and dependable price estimation. The applied advanced machine learning techniques demonstrate their strength through latest research that helps develop pricing strategies specifically for the used car market.