A Case Study Using Machine Learning Techniques for Prediction of House Prices in WP, Malaysia

DOI

10.2991/978-94-6463-094-7_7How to use a DOI?

Keywords

Case study; Machine learning; Multiple linear regression; Bayesian ridge regression; Decision tree regression; Random forest regression; Prediction of house prices; Wilayah Persekutuan; Malaysia

Abstract

The research for this study is to find out performance scores from machine learning techniques in predicting house prices as compared to its actual prices. One of the limitations of existing techniques is predictive models generated do not cover the situation within Malaysia. Therefore, a data collection of Malaysia within Wilayah Persekutuan state, predictive models and data visualizations have been implemented for this case study. The machine learning techniques chosen for the predictive models are multiple linear regression, Bayesian ridge regression, decision tree regression and random forest regression. The house dataset gathered within Wilayah Persekutuan state is from propertyguru.com, a popular property website available in Malaysia. The result concludes that among all the four machine learning techniques chosen, random forest yields the best performance score with a small margin of pricing errors.

Copyright

© 2022 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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TY  - CONF
AU  - Yoon-Teck Bau
AU  - Syed Muiz Syed Badrul Hisham
PY  - 2022
DA  - 2022/12/27
TI  - A Case Study Using Machine Learning Techniques for Prediction of House Prices in WP, Malaysia
BT  - Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)
PB  - Atlantis Press
SP  - 79
EP  - 91
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-094-7_7
DO  - 10.2991/978-94-6463-094-7_7
ID  - Bau2022
ER  -