The paper "An Optimal House Price Prediction Algorithm: XGBoost" by Hemlata Sharma, Hitesh Harsora, and Bayode Ogunleye focuses on developing an accurate house price prediction model using machine learning (ML) techniques. The authors address the house price prediction problem as a regression task and compare various ML algorithms, including XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression, using the Ames City housing dataset in Iowa, USA. The study aims to identify the key factors influencing housing costs and determine the most effective model for accurate predictions. Key findings include: - **Model Performance**: XGBoost outperforms other models in terms of R-squared, cross-validation score, mean squared error (MSE), and root mean squared error (RMSE). It achieves the highest R-squared value of 0.93 and the lowest MSE of 0.001. - **Hyperparameter Tuning**: Hyperparameter tuning significantly improves model performance, particularly for the random forest and XGBoost models. GridSearchCV is used to optimize hyperparameters, enhancing the model's accuracy. - **Feature Importance**: Feature importance analysis using XGBoost identifies "Overall Qual" (overall quality of the house), "Gr Liv Area" (ground floor living area), "Garage Cars" (garage capacity), and "Total Bsmt SF" (total basement square footage) as the most influential features in predicting house prices. The study concludes that XGBoost is the optimal model for house price prediction, providing valuable insights for real estate professionals, investors, and potential homebuyers. The findings highlight the importance of hyperparameter tuning and feature selection in achieving accurate and interpretable predictions. Future research could focus on expanding data volume and incorporating spatial interpolation to enhance model accuracy.The paper "An Optimal House Price Prediction Algorithm: XGBoost" by Hemlata Sharma, Hitesh Harsora, and Bayode Ogunleye focuses on developing an accurate house price prediction model using machine learning (ML) techniques. The authors address the house price prediction problem as a regression task and compare various ML algorithms, including XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression, using the Ames City housing dataset in Iowa, USA. The study aims to identify the key factors influencing housing costs and determine the most effective model for accurate predictions. Key findings include: - **Model Performance**: XGBoost outperforms other models in terms of R-squared, cross-validation score, mean squared error (MSE), and root mean squared error (RMSE). It achieves the highest R-squared value of 0.93 and the lowest MSE of 0.001. - **Hyperparameter Tuning**: Hyperparameter tuning significantly improves model performance, particularly for the random forest and XGBoost models. GridSearchCV is used to optimize hyperparameters, enhancing the model's accuracy. - **Feature Importance**: Feature importance analysis using XGBoost identifies "Overall Qual" (overall quality of the house), "Gr Liv Area" (ground floor living area), "Garage Cars" (garage capacity), and "Total Bsmt SF" (total basement square footage) as the most influential features in predicting house prices. The study concludes that XGBoost is the optimal model for house price prediction, providing valuable insights for real estate professionals, investors, and potential homebuyers. The findings highlight the importance of hyperparameter tuning and feature selection in achieving accurate and interpretable predictions. Future research could focus on expanding data volume and incorporating spatial interpolation to enhance model accuracy.