Machine Learning-Based House Rent Prediction Using Stacking Integration Method

Kainuo Wang; Huiyi Zhao; Jingzhong Li

doi:doi:10.11648/j.ajmse.20230802.12

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Machine Learning-Based House Rent Prediction Using Stacking Integration Method

Kainuo Wang, Huiyi Zhao, Jingzhong Li

Published in American Journal of Management Science and Engineering (Volume 8, Issue 2)

Received: 7 March 2023 Accepted: 10 April 2023 Published: 18 April 2023

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Abstract

With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.

Published in	American Journal of Management Science and Engineering (Volume 8, Issue 2)
DOI	10.11648/j.ajmse.20230802.12
Page(s)	50-55
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Stacking Integration, Ensemble Model, Machine Learning, House Rent, Prediction

References

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APA Style

Kainuo Wang, Huiyi Zhao, Jingzhong Li. (2023). Machine Learning-Based House Rent Prediction Using Stacking Integration Method. American Journal of Management Science and Engineering, 8(2), 50-55. https://doi.org/10.11648/j.ajmse.20230802.12

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ACS Style

Kainuo Wang; Huiyi Zhao; Jingzhong Li. Machine Learning-Based House Rent Prediction Using Stacking Integration Method. Am. J. Manag. Sci. Eng. 2023, 8(2), 50-55. doi: 10.11648/j.ajmse.20230802.12

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AMA Style

Kainuo Wang, Huiyi Zhao, Jingzhong Li. Machine Learning-Based House Rent Prediction Using Stacking Integration Method. Am J Manag Sci Eng. 2023;8(2):50-55. doi: 10.11648/j.ajmse.20230802.12

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@article{10.11648/j.ajmse.20230802.12,
  author = {Kainuo Wang and Huiyi Zhao and Jingzhong Li},
  title = {Machine Learning-Based House Rent Prediction Using Stacking Integration Method},
  journal = {American Journal of Management Science and Engineering},
  volume = {8},
  number = {2},
  pages = {50-55},
  doi = {10.11648/j.ajmse.20230802.12},
  url = {https://doi.org/10.11648/j.ajmse.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20230802.12},
  abstract = {With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.},
 year = {2023}
}

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TY  - JOUR
T1  - Machine Learning-Based House Rent Prediction Using Stacking Integration Method
AU  - Kainuo Wang
AU  - Huiyi Zhao
AU  - Jingzhong Li
Y1  - 2023/04/18
PY  - 2023
N1  - https://doi.org/10.11648/j.ajmse.20230802.12
DO  - 10.11648/j.ajmse.20230802.12
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 50
EP  - 55
PB  - Science Publishing Group
SN  - 2575-1379
UR  - https://doi.org/10.11648/j.ajmse.20230802.12
AB  - With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.
VL  - 8
IS  - 2
ER  -

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Author Information

Kainuo Wang

School of Resource and Environmental Science, Wuhan University, Wuhan, China
Huiyi Zhao

School of Urban Design, Wuhan University, Wuhan, China
Jingzhong Li

School of Resource and Environmental Science, Wuhan University, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Kainuo Wang, Huiyi Zhao, Jingzhong Li. (2023). Machine Learning-Based House Rent Prediction Using Stacking Integration Method. American Journal of Management Science and Engineering, 8(2), 50-55. https://doi.org/10.11648/j.ajmse.20230802.12

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ACS Style

Kainuo Wang; Huiyi Zhao; Jingzhong Li. Machine Learning-Based House Rent Prediction Using Stacking Integration Method. Am. J. Manag. Sci. Eng. 2023, 8(2), 50-55. doi: 10.11648/j.ajmse.20230802.12

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AMA Style

Kainuo Wang, Huiyi Zhao, Jingzhong Li. Machine Learning-Based House Rent Prediction Using Stacking Integration Method. Am J Manag Sci Eng. 2023;8(2):50-55. doi: 10.11648/j.ajmse.20230802.12

Copy | Download

@article{10.11648/j.ajmse.20230802.12,
  author = {Kainuo Wang and Huiyi Zhao and Jingzhong Li},
  title = {Machine Learning-Based House Rent Prediction Using Stacking Integration Method},
  journal = {American Journal of Management Science and Engineering},
  volume = {8},
  number = {2},
  pages = {50-55},
  doi = {10.11648/j.ajmse.20230802.12},
  url = {https://doi.org/10.11648/j.ajmse.20230802.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmse.20230802.12},
  abstract = {With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Machine Learning-Based House Rent Prediction Using Stacking Integration Method
AU  - Kainuo Wang
AU  - Huiyi Zhao
AU  - Jingzhong Li
Y1  - 2023/04/18
PY  - 2023
N1  - https://doi.org/10.11648/j.ajmse.20230802.12
DO  - 10.11648/j.ajmse.20230802.12
T2  - American Journal of Management Science and Engineering
JF  - American Journal of Management Science and Engineering
JO  - American Journal of Management Science and Engineering
SP  - 50
EP  - 55
PB  - Science Publishing Group
SN  - 2575-1379
UR  - https://doi.org/10.11648/j.ajmse.20230802.12
AB  - With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.
VL  - 8
IS  - 2
ER  -

Copy | Download