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Machine learning models for house price prediction
- 1 Stuyvesant High School, 6617 Ovington Ct Brooklyn, NY 11204, United States
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Abstract
Housing prices have changed over the years, and there has been an increasing need to predict prices of future homes. This paper gives an overview of various machine learning models that can predict housing prices. There are numerous possible methods of pre-processing the data, so this paper explores ways to handle missing values and categorical data. In this study, the models of regression tree, random forest, XGBoost, gradient boosting, and LightGBM are described and used to predict housing prices. Machine learning models also have hyperparameters that can be adjusted, which can affect predictive accuracy. The methods are evaluated on several benchmark datasets. Based on the results, our approach is effective for the task of house price prediction.
Keywords
Machine Learning, House Price Prediction.
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Cite this article
Fang,L. (2023). Machine learning models for house price prediction. Applied and Computational Engineering,4,409-415.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning
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