Abstract
As a pillar industry of the economy, real estate has a significant impact on social and economic development. Therefore, accurate prediction of house prices has always been a focus of attention. This study is based on the Kaggle House Prices dataset and constructs a relatively reliable house price prediction model through data cleaning, feature engineering, and machine learning algorithms. Firstly, the data was preprocessed to remove outliers and missing values. Then, feature engineering and principal component analysis were performed to extract more meaningful data features. Finally, the stacking model was used to train the data, and a high-accuracy house price prediction model was established. The research results of this study can help homebuyers make more informed decisions, assist investors in making more favorable investment decisions, aid governments in formulating more effective policies and plans, and help the real estate industry develop more targeted marketing strategies, among others.
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Zhou, Y. (2024). Stacking-Based Model for House Price Prediction. In: Li, X., Yuan, C., Kent, J. (eds) Proceedings of the 7th International Conference on Economic Management and Green Development. ICEMGD 2023. Applied Economics and Policy Studies. Springer, Singapore. https://doi.org/10.1007/978-981-97-0523-8_88
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DOI: https://doi.org/10.1007/978-981-97-0523-8_88
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