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 APPLICATION OF MACHINE LEARNING IN REAL ESTATE MARKET RESEARCH
Tác giả hoặc Nhóm tác giả: TRAN H.H.
Nơi đăng: МЯГКИЕ ИЗМЕРЕНИЯ И ВЫЧИСЛЕНИЯ Учредители: ООО "Издательский дом "Научная библиотека" ISSN: 2618-9976eISSN: 2713-2072; Số: 7;Từ->đến trang: 43-58;Năm: 2024
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT

ABSTRACT
This article discusses the use of machine learning for analysis and forecasting in the residential real estate market. The authors emphasize various machine learning methods, including supervised, unsupervised, and semi-supervised learning. These technologies are used to work with real estate data to improve the efficiency of price forecasting, analyzing market trends and optimizing marketing strategies. One of the significant benefits of machine learning is its ability to recognize complex patterns and relationships in large volumes of data, which is invaluable for in-depth analysis of the real estate market. The article also discusses the main challenges associated with using this approach, including possible data quality issues and the risks of overfitting models, which can lead to biased analysis results. The author provide specific examples of the successful use of machine learning in this area, demonstrating how these technologies can effectively cope with the task of identifying non-obvious patterns in data. This opens up new perspectives for developing marketing strategies and improving the accuracy of real estate price forecasts. The final part of the article highlights the importance of further research in this area. Particular attention is paid to the need to integrate diverse types of data and develop new algorithms that could improve predictive ability and analytical accuracy in real estate valuations. The author point out the need to continue research to expand the functionality of machine learning, which will make it possible to more fully use its potential in economic and commercial applications in the real estate market.
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