A Polynomial Linear Prediction Model for Housing Price in the USA

Research Article
Open access

A Polynomial Linear Prediction Model for Housing Price in the USA

Yuyao He 1*
  • 1 University College London, London, WC1E 6BT, UK    
  • *corresponding author hecaroline@yeah.net
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220593
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

The purpose of this research was to build a linear model for predicting the price of houses. The price of the house could be approximated without knowing the price of every house. In the process of the experiment, the data from real estate markets would be analyzed for the supervised study. A linear model would be utilized to predict the price. Different values of learning rate would be compared, and the most efficient value according to the cost function would be chosen. Finally, the prediction model with learning rate 2 would be chosen and used by people who would like to know the price of houses without spending a long time. The price of the house can be successfully accessed by inputting the values of features -numbers of bedrooms, bathrooms, area, and floors- to the model.

Keywords:

supervised learning, cost function, linear regression, house price prediction, gradient descent

He,Y. (2023). A Polynomial Linear Prediction Model for Housing Price in the USA. Applied and Computational Engineering,2,672-678.
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References

[1]. University College of London, “Number of housing units in the United States”, in 2021, Available: https://www.statista.com/statistics/240267/number-of-housing-units-in-the-united-states/

[2]. ScienceDirect, “Supervised Learning”, Available: https://www.sciencedirect.com/topics/computer-science/supervised-learning

[3]. Adyan Nur Alfiyatin, Hilman Taufiq, Ruth Ema Febrita, and Wayan Firdaus Mahmudy, in 2017,

[4]. “Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization” Available:https: //www.researchgate.net/profile/Wayan-Mahmudy-2

[5]. Shree, updated in 2019, “House data”, Available: https://www.kaggle.com/shree1992/housedata

[6]. Baijayanta Roy, published in 2020, “All about Feature Scaling”, Towards Data Science, Available: https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35

[7]. D.P.Mandic, Feb. 2004, “A generalized normalized gradient descent algorithm”, published in “IEEE Signal processing letters”

[8]. Hang Zhang. In 2020, “House Price Prediction with An Improved Stack Approach”, available in: https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012062/meta

[9]. Matthew D. Zeiler, 2012, “An Adaptive Learning Rate Method”, Available: https://arxiv.org/abs/1212.5701

Cite this article

He,Y. (2023). A Polynomial Linear Prediction Model for Housing Price in the USA. Applied and Computational Engineering,2,672-678.

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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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. University College of London, “Number of housing units in the United States”, in 2021, Available: https://www.statista.com/statistics/240267/number-of-housing-units-in-the-united-states/

[2]. ScienceDirect, “Supervised Learning”, Available: https://www.sciencedirect.com/topics/computer-science/supervised-learning

[3]. Adyan Nur Alfiyatin, Hilman Taufiq, Ruth Ema Febrita, and Wayan Firdaus Mahmudy, in 2017,

[4]. “Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization” Available:https: //www.researchgate.net/profile/Wayan-Mahmudy-2

[5]. Shree, updated in 2019, “House data”, Available: https://www.kaggle.com/shree1992/housedata

[6]. Baijayanta Roy, published in 2020, “All about Feature Scaling”, Towards Data Science, Available: https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35

[7]. D.P.Mandic, Feb. 2004, “A generalized normalized gradient descent algorithm”, published in “IEEE Signal processing letters”

[8]. Hang Zhang. In 2020, “House Price Prediction with An Improved Stack Approach”, available in: https://iopscience.iop.org/article/10.1088/1742-6596/1693/1/012062/meta

[9]. Matthew D. Zeiler, 2012, “An Adaptive Learning Rate Method”, Available: https://arxiv.org/abs/1212.5701

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