• Sandeep Kumar
• Rahul Yadav
• Jwala Yadav
• Silviya D Monte

House price prediction is a critical task in the real estate industry, influencing various stakeholders such as buyers, sellers, and investors. Traditional regression techniques and machine learning algorithms have been extensively employed for this purpose. In recent years, Extreme Gradient Boosting (XGBoost) has gained popularity due to its high performance and robustness in handling complex datasets. This research paper presents a comprehensive study on the application of XGBoost for house price prediction. We explore the effectiveness of XGBoost in capturing nonlinear relationships and handling large-scale datasets, thus enhancing the accuracy of price predictions. Our experimental results demonstrate the superior predictive performance of XGBoost compared to traditional regression models and other machine learning algorithms. Additionally, we discuss important factors such as feature selection, hyperparameter tuning, and model evaluation techniques to optimize the XGBoost model for house price prediction. The findings of this study contribute to advancing the state-of-the-art in real estate analytics and provide valuable insights for stakeholders involved in property valuation and investment decision-making.

House Price,Real Estate,Extreme Gradient Boosting, hyperparameter tuning, model evaluation.

"Real Estate House Price Prediction Using Extreme Gradient Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e828-e836, April-2024, Available :http://www.jetir.org/papers/JETIR2404493.pdf

"Real Estate House Price Prediction Using Extreme Gradient Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe828-e836, April-2024, Available at : http://www.jetir.org/papers/JETIR2404493.pdf