Analysis of the Effectiveness of Predicting Housing Prices Based on Different Machine Learning Models

DOI

10.2991/978-94-6463-542-3_50How to use a DOI?

Keywords

Predicting Housing Prices; Machine Learning Models

Abstract

With the growing housing price market, effectively predicting housing prices not only has an important impact on the economy but also is related to people’s living standards. However, the fluctuation of housing prices is affected by many factors, and in most cases, there is a non-linear relationship between housing price fluctuations and housing factors. In reality, there are differences in the effectiveness of different machine learning models in predicting home prices. Therefore, this paper uses multiple machine-learning models to explore how effective different machine-learning models are for house price prediction. In this work, the authors searched the Kaggle website for a data set of housing prices and housing factors in Bangalore, India. The housing attribute data includes the housing price, number of hardware facilities (number of bedrooms, number of swimming pools, number of sofas, etc.), and number of service facilities around the house (stadiums, shopping malls, etc.). Then, this data set was used to evaluate the house price prediction method of random forest, ridge regression, and XGboost. The results showed that the mixed model showed the best fit. This study can be used to select appropriate machine learning model predictions for home 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.

Download article (PDF)

TY  - CONF
AU  - Wen Wang
PY  - 2024
DA  - 2024/10/15
TI  - Analysis of the Effectiveness of Predicting Housing Prices Based on Different Machine Learning Models
BT  - Proceedings of the 2024 2nd International Conference on Management Innovation and Economy Development (MIED 2024)
PB  - Atlantis Press
SP  - 433
EP  - 438
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-542-3_50
DO  - 10.2991/978-94-6463-542-3_50
ID  - Wang2024
ER  -