Skip to Main content Skip to Navigation
Conference papers

Analysis and Prediction for House Sales Prices by Using Hybrid Machine Learning Approaches

Abstract : Over the past few years, machine learning has played an increasingly vital role in every aspect of our society. There are countless applications of machine learning, from tradition topic such as image recognition or spam detection, to advanced areas like automatic customer service or secure automobile systems. This paper analyzes a popular machine learning application, namely housing price prediction, by applying a full machine learning process: feature extraction, data preparation, model selection, model training and optimization, and last, but not least, prediction and evaluation. We experiment with different algorithms: linear regression, random forest, and gradient boosting. This paper demonstrates the comparison of effectiveness of these algorithms that may help sellers and buyers to have a fair deal of their respective businesses.
Document type :
Conference papers
Complete list of metadata
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2021 - 6:10:40 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:05:50 PM


 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2024-01-01

Please log in to resquest access to the document


Distributed under a Creative Commons Attribution 4.0 International License



S. Hossain, Jyoti Rawat, Doina Logofatu. Analysis and Prediction for House Sales Prices by Using Hybrid Machine Learning Approaches. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.594-604, ⟨10.1007/978-3-030-79150-6_47⟩. ⟨hal-03287678⟩



Record views