Analysis reveals machine learning models effectively improve pricing strategy in the automobile industry, indicating advanced methods may outperform traditional approaches.
Key Points
Random Forest achieved a remarkable R² score of 0.92, highlighting the model's predictive accuracy for car prices.
A comprehensive feature analysis pinpointed engine capacity, vehicle age, and year of manufacture as critical determinants in pricing.
The study implemented state-of-the-art machine learning techniques, including CNNs and thorough hyperparameter tuning, to refine predictions.
Incorporating Explainable AI methods like LIME and SHAP increased the interpretability of model outcomes for stakeholders.