Predicting houses price by deep learning neural network

Research Article
Open access

Predicting houses price by deep learning neural network

Zidong Xu 1*
  • 1 Stony brook institute of Anhui university    
  • *corresponding author xu-zidong.co@foxmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/13/20230724
ACE Vol.13
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-017-2
ISBN (Online): 978-1-83558-018-9

Abstract

Deep learning is widely used in various fields, the article proposed a deep learning method to predict house prices through different characteristics of real estate, establish a prediction model, and carry out simulation experiments. First, extracting data from property transactions records, it is difficult to directly input the raw-data into the deep learning model, and there may be overfitting in the model training, so data will be pretreated. Second, Fully-Connected Neural Network is used to model different features’ influences to price. The sample data will be randomly divided into a training set and a validation set, of which 70% of the samples are used for building and training the model, and the remaining 30% are used for model accuracy verification. Experimental results show that the model can achieve a high accuracy in predicting houses price. The model can be used as a reference for the evaluation of housing prices.

Keywords:

machine learning, artificial neural network, predicting, weight values, back propagation neural network

Xu,Z. (2023). Predicting houses price by deep learning neural network. Applied and Computational Engineering,13,153-159.
Export citation

References

[1]. Silver, David, et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature, vol. 529, no. 7587, 2016, pp. 484–489., https://doi.org/10.1038/nature16961.

[2]. Song, Sen, et al. “Competitive Hebbian Learning through Spike-Timing-Dependent Synaptic Plasticity.” Nature Neuroscience, vol. 3, no. 9, Sept. 2000, pp. 919–926., https://doi.org/10.1038/78829.

[3]. Silver, David, et al. “Mastering the Game of Go without Human Knowledge.” Nature, vol. 550, no. 7676, 2017, pp. 354–359., https://doi.org/10.1038/nature24270.

[4]. Rumelhart, David E., et al. “Learning Representations by Back-Propagating Errors.” Nature, vol. 323, no. 6088, 1986, pp. 533–536., https://doi.org/10.1038/323533a0.

[5]. Goodfellow, Ian, et al. Deep Learning. MITP, 2018.

[6]. Arora, Monika, and Vineet Kansal. “Character Level Embedding with Deep Convolutional Neural Network for Text Normalization of Unstructured Data for Twitter Sentiment Analysis.” Social Network Analysis and Mining, vol. 9, no. 1, 2019, https://doi.org/10.1007/s13278-019-0557-y.

[7]. Zhong, Botao, et al. “Convolutional Neural Network: Deep Learning-Based Classification of Building Quality Problems.” Advanced Engineering Informatics, vol. 40, 2019, pp. 46–57., https://doi.org/10.1016/j.aei.2019.02.009.

[8]. Li, Mu, et al. “Efficient Mini-Batch Training for Stochastic Optimization.” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, https://doi.org/10.1145/2623330.2623612.

[9]. Olson, Matthew, et al. “Modern Neural Networks Generalize on Small Data Sets.” Advances in Neural Information Processing Systems, 1 Jan. 1970, https://papers.nips.cc/paper/7620-modern-neural-networks-generalize-on-small-data-sets.

[10]. Tamura, S., and M. Tateishi. “Capabilities of a Four-Layered Feedforward Neural Network: Four Layers versus Three.” IEEE Transactions on Neural Networks, vol. 8, no. 2, Mar. 1997, pp. 251–255., https://doi.org/10.1109/72.557662.

Cite this article

Xu,Z. (2023). Predicting houses price by deep learning neural network. Applied and Computational Engineering,13,153-159.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-017-2(Print) / 978-1-83558-018-9(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.13
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Silver, David, et al. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature, vol. 529, no. 7587, 2016, pp. 484–489., https://doi.org/10.1038/nature16961.

[2]. Song, Sen, et al. “Competitive Hebbian Learning through Spike-Timing-Dependent Synaptic Plasticity.” Nature Neuroscience, vol. 3, no. 9, Sept. 2000, pp. 919–926., https://doi.org/10.1038/78829.

[3]. Silver, David, et al. “Mastering the Game of Go without Human Knowledge.” Nature, vol. 550, no. 7676, 2017, pp. 354–359., https://doi.org/10.1038/nature24270.

[4]. Rumelhart, David E., et al. “Learning Representations by Back-Propagating Errors.” Nature, vol. 323, no. 6088, 1986, pp. 533–536., https://doi.org/10.1038/323533a0.

[5]. Goodfellow, Ian, et al. Deep Learning. MITP, 2018.

[6]. Arora, Monika, and Vineet Kansal. “Character Level Embedding with Deep Convolutional Neural Network for Text Normalization of Unstructured Data for Twitter Sentiment Analysis.” Social Network Analysis and Mining, vol. 9, no. 1, 2019, https://doi.org/10.1007/s13278-019-0557-y.

[7]. Zhong, Botao, et al. “Convolutional Neural Network: Deep Learning-Based Classification of Building Quality Problems.” Advanced Engineering Informatics, vol. 40, 2019, pp. 46–57., https://doi.org/10.1016/j.aei.2019.02.009.

[8]. Li, Mu, et al. “Efficient Mini-Batch Training for Stochastic Optimization.” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, https://doi.org/10.1145/2623330.2623612.

[9]. Olson, Matthew, et al. “Modern Neural Networks Generalize on Small Data Sets.” Advances in Neural Information Processing Systems, 1 Jan. 1970, https://papers.nips.cc/paper/7620-modern-neural-networks-generalize-on-small-data-sets.

[10]. Tamura, S., and M. Tateishi. “Capabilities of a Four-Layered Feedforward Neural Network: Four Layers versus Three.” IEEE Transactions on Neural Networks, vol. 8, no. 2, Mar. 1997, pp. 251–255., https://doi.org/10.1109/72.557662.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.