Advanced Machine Learning Techniques for Predicting House Prices: A Comparative Analysis

DOI

10.2991/978-94-6463-738-0_37How to use a DOI?

Keywords

House Price Predictions; Machine Learning; Comparative Analysis; Neural Networks; Real Estate

Abstract

Predicting house prices accurately is a complex challenge due to various influencing factors. This study compares multiple machines learning models, including Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Network, to determine the most effective approach for house price prediction. The objective is to evaluate these models based on performance Metrix like Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. The methodology involves data the preprocessing, feature selection, model training and hyperparameter tuning using Python libraries such as Scikit-learn and TensorFlow. Experimental results show that Neural Networks outperform other models, achieving the lowest MSE and highest R-squared, followed by Random Forests and SVM. However, Neural Networks require significant computational resources.

The study highpoints the importance of feature selection in improving model efficiency and suggests integrating real-time economic indicators for better predictions. These findings contribute to the real estate sector by providing data-driven insights for property valuation, aiding stakeholders in making informed decisions. Future work will focus on enhancing model interpretability and adapting to dynamic market conditions.

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  - Gautam Kumar
AU  - Satyam Mishra
AU  - Anmol Patwal
AU  - Milap Grover
AU  - Ravi Kumar
AU  - Pulkit Srivastava
PY  - 2025
DA  - 2025/06/22
TI  - Advanced Machine Learning Techniques for Predicting House Prices: A Comparative Analysis
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 458
EP  - 470
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_37
DO  - 10.2991/978-94-6463-738-0_37
ID  - Kumar2025
ER  -