Ensemble ML Model for Automobile Price Prediction Using Image Dataset

The study explores applying Ensemble Machine Learning (ML) model to predict car prices using a dataset of car images. It aims to improve accuracy and efficiency in the automotive market. By leveraging machine learning models and ensemble techniques, the study achieves a remarkable 99.99% accuracy in price prediction. Through data examination, preprocessing, model building, and testing, the research demonstrates the effectiveness of ensemble methods like bagging and stacking in improving predictive performance. The findings highlight the significance of data-driven solutions in overcoming traditional valuation challenges, offering transparency, fairness, and precision in car pricing. The study contributes to advancing predictive modeling in the automotive industry, emphasizing the importance of accurate price predictions for informed decision-making. Future research directions include exploring larger datasets, addressing missing data issues, and refining predictive capabilities for enhanced model robustness and performance.