Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast
- 1 School of Engineering, Xi'an University Of Technology, Xi'an, China
* Author to whom correspondence should be addressed.
Abstract
This study used a total of eight machine learning algorithms to forecast property prices, it not only provides a robust comparison of the predictive power of different algorithms but also significantly advances our understanding of the factors that influence property prices. In this paper, four traditional machine learning algorithms and four neural network models are selected for comparative study and analysis, of which the neural network models include fully connected neural networks (FCNN), convolutional fully connected neural networks (FCNN+CNN), generative adversarial fully connected networks (FCNN+GANs) and generative adversarial convolutional fully connected neural networks (FCNN+GANs+CNN). This study applied to a Kaggle's sample. The results reveal that the models based on FCNN+CNN and FCNN+GANs+CNN perform relatively well in house price prediction, with both obtaining an explanatory power of R² as high as 0. 96 and 0. 97, respectively and significantly outperforming traditional machine learning algorithms. It is worth mentioning that the FCNN+CNN model is slightly stronger in terms of error minimization, but both perform better in terms of stability and generalization capabilities. The conclusion is that neural network models generally have better results than traditional algorithms in house price prediction, and the neural network model of CNN composite has significantly better prediction performance.
Keywords
Property Price Prediction, Deep Neural Networks, Machine Learning Algorithm Comparison, CNN.
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Cite this article
Zhang,J. (2024). Application and Performance Comparison of Compound Neural Network Model based on CNN Feature Extraction in House Price Forecast. Applied and Computational Engineering,96,210-217.
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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