Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

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Comparing Linear Regression and Decision Trees for Housing Price Prediction

Authors
Xiang Li1, *
1Fuzhou Pingdong Middle School of Fujian Province, Fuzhou, Fujian, 350000, China
*Corresponding author. Email: xil470@pitt.edu
Corresponding Author
Xiang Li
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_9How to use a DOI?
Keywords
Housing Price Prediction; Linear Regression; Decision Trees
Abstract

As artificial intelligence and machine learning become more and more advanced nowadays, they have been used in vast fields. As a fundamental and mature topic, housing price prediction remains popular among machine learning workers and researchers. Housing price prediction can contribute a lot to the real estate market and global economy as well as making it much more effective for investors to make decisions. There is a great variety of algorithms in machine learning, and algorithms are still updating as time passes. Housing price prediction applies a reasonable background for researchers conducting machine learning research. Linear regression and decision trees are two popular algorithms in machine learning, which are both possible for housing price prediction. Linear regression can fit a “line” that follows how housing prices change as variables change, while decision trees can also forecast house prices as their trees become deeper and deeper. In this research, the author will compare the performance and accuracy of linear regression and decision trees when used to predict house prices.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_9
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_9How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Xiang Li
PY  - 2024
DA  - 2024/02/14
TI  - Comparing Linear Regression and Decision Trees for Housing Price Prediction
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 77
EP  - 84
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_9
DO  - 10.2991/978-94-6463-370-2_9
ID  - Li2024
ER  -