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Predicting Housing Prices Using Supervised Machine Learning Models
- 1 University of Southern California
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Abstract
Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.
Keywords
Machine learning models, house price prediction, polynomial regression, K-nearest neighbors regression.
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Cite this article
Zhang,H. (2024). Predicting Housing Prices Using Supervised Machine Learning Models. Advances in Economics, Management and Political Sciences,118,1-7.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Financial Technology and Business Analysis
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