Comparative Analysis of Machine Learning Models for House Price Prediction

DOI

10.2991/978-94-6463-823-3_12How to use a DOI?

Keywords

Artificial Intelligence; Machine Learning; House Price Prediction

Abstract

In the current real estate market, accurate house price predictions are important for buyers, sellers, and policy makers. However, traditional evaluation techniques, often relying on expert judgment and historical comparisons, are not effective in addressing the complexity and dynamic nature of housing markets. The emergence of artificial intelligence has introduced more data-driven, accurate, and efficient ways have been developed. The study explores the performance of various machine learning models in estimating house prices by using a publicly available dataset from Kaggle, which includes 1, 460 records and 80 features. The model analyzed linear regression, K-Nearest Neighbors (KNN), random forest regressor, gradient boosting regressor, and neural networks. Each model is evaluated through performance metrics, for example, Mean Square Error (MSE) and R 2 . Among the models, gradient boosting shows the best overall performance with the highest R 2 of 0.92 and lowest error metrics, followed by linear regression and neural network. KNN shows the worst performance due to its high sensitivity to irrelevant features and outliers. The findings highlight the potential of ensemble-based machine learning models for accurate house price forecasting and provide insights into key features driving housing values.

Copyright

© 2025 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  - Hongyi Gong
PY  - 2025
DA  - 2025/08/31
TI  - Comparative Analysis of Machine Learning Models for House Price Prediction
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 125
EP  - 134
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_12
DO  - 10.2991/978-94-6463-823-3_12
ID  - Gong2025
ER  -