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In this paper, a study was conducted on the accuracy of forecasting real estate prices using various neural network architectures. The results showed that the two-layer model with 32 neurons in each layer achieved the highest prediction accuracy (0.98), demonstrating the ability to capture complex data dependencies most effectively and avoid overfitting. This study highlights the importance of choosing the optimal neural network architecture and configuring hyperparameters to achieve high prediction accuracy. The optimal configuration must balance the complexity of the model and its ability to generalize.

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Analysis of Forecasting Accuracy Depending on Various Hyperparameters of Neural Network Training

  • 会议论文

  • 发表日期：

    2025年

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  • Vladislav Kukartsev,
  • Ksenia Degtyareva

摘要

In this paper, a study was conducted on the accuracy of forecasting real estate prices using various neural network architectures. The results showed that the two-layer model with 32 neurons in each layer achieved the highest prediction accuracy (0.98), demonstrating the ability to capture complex data dependencies most effectively and avoid overfitting. This study highlights the importance of choosing the optimal neural network architecture and configuring hyperparameters to achieve high prediction accuracy. The optimal configuration must balance the complexity of the model and its ability to generalize.