Application of Multiple Linear Regression with Regularization on Boston Housing Datasets

DOI

10.2991/978-94-6463-488-4_3How to use a DOI?

Keywords

Linear Regression; Multiple Linear Regression; Lasso regression; Ridge regression; Machine learning

Abstract

This paper first introduces the principle of multi-objective linear regression, and studies the Boston housing price data set with regularized multiple linear regression. Then this paper combines the knowledge of machine learning to build a prediction model. In the final forecast of the Boston house price, it was about 78 percent accurate compared to the real house price.

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  - Yuanwei Ding
AU  - Hexing Zhou
AU  - Chak Hoi Huang
AU  - Haoxiang Zhang
PY  - 2024
DA  - 2024/08/29
TI  - Application of Multiple Linear Regression with Regularization on Boston Housing Datasets
BT  - Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024)
PB  - Atlantis Press
SP  - 14
EP  - 26
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-488-4_3
DO  - 10.2991/978-94-6463-488-4_3
ID  - Ding2024
ER  -