House Price Prediction Based on Machine Learning Model

DOI

10.2991/978-2-38476-346-7_18How to use a DOI?

Keywords

house price; prediction; machine learning

Abstract

This paper explores the challenging task of housing price prediction using machine learning algorithms. Leveraging a dataset of Beijing housing prices from 2011 to 2017, various preprocessing techniques, including handling missing values and feature extraction, were employed. Attributes were selected based on Pearson correlation coefficient, covariance, and principal component analysis (PCA) to improve prediction accuracy. The performance of different models was evaluated using root-mean-square error (RMSE), with RandomForest demonstrating the best performance initially. However, through attribute selection and model optimization, notably using Pearson correlation coefficient and covariance, significant improvements were observed, particularly in GradientBoost and ExtraTree models. Additionally, PCA enhanced the performance of Linear Regression. The combination of covariance and PCA further optimized model performance, underscoring the importance of attribute selection and model optimization in housing price prediction.

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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TY  - CONF
AU  - Haojie Chen
PY  - 2024
DA  - 2024/12/27
TI  - House Price Prediction Based on Machine Learning Model
BT  - Proceeding of the 2024 International Conference on Diversified Education and Social Development (DESD 2024)
PB  - Atlantis Press
SP  - 133
EP  - 142
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-346-7_18
DO  - 10.2991/978-2-38476-346-7_18
ID  - Chen2024
ER  -