A Robust Ensemble-Based Framework for House Price Estimation: Integrating XG-Boost with SHAP and Web Deployment

Accurate house price prediction is crucial for stakeholders in the real estate industry, including buyers, sellers, agents, and investors. Traditional valuation methods often rely on manual appraisal or basic regression techniques, which may lack scalability and the ability to capture complex relationships among features. With the rise of machine learning, especially ensemble models like XGBoost, there is a growing opportunity to improve prediction accuracy and robustness. This study presents a comprehensive framework for optimal house price prediction using XG-Boost, applied to the King County housing dataset comprising over 21,000 records. The suggested objective is supported by carefully conducted data preprocessing, which will imply outliers’ elimination, feature engineering, and normalization. Other important derived features that were included were the age of the house and the renovation status of the house as well as the price per square foot. The XG-Boost regression model was trained and validated, and the R-squared ratio was found to be 0.87, indicating decent model performance. The model was also deployed as an interactive web application with Streamlit to allow users to store property details and get real-time predictions of the properties. Also, Shapley Additive Explanations (SHAP) values were applied to interpret the model output in a way that enhances explanations and builds trust among users. The system is not only accurate, but also keen on accountability, and usability. The proposed research will fill this missing gap and provide an actionable machine learning solution, deployable and explainable to real estate businesses to take advantage of the predictive analytics revolution. The results demonstrate the effectiveness of ensemble learning in conjunction with visualization and interpretability tools in the development of robust decision-support systems in the real-world housing markets.