[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126429-en":3,"doc-seo-126429-105":30,"detail-sidebar-cat-0-en-105":96},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126429,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS","In the rapidly evolving real estate market, machine learning enables the analysis and prediction of price trends with data-driven rigor. This study compares seven supervised ML regression models—including multiple linear regression, random forest regression, support vector regression, decision tree regression, and XGBoost—to identify the most effective predictor of housing prices in Astana, Kazakhstan. Using a dataset of 9,000+ records drawn from 30,000+ transactions, model quality is evaluated via MAE, RMSE, and R-squared. Random Forest Regressor and XGBRegressor achieve the best accuracy, indicating strong robustness for forecasting and practical deployment.","DOI: 10. 37943/21EBBH5859  \n© Saya Sapakova, Zhansaya Bekaulova, Almas Nurlanuly, Duriya Daniyarova, Galiya Ybytayeva, Aizhan Kaldybayeva  \n33  \nDOI: 10.37943/21EBBH5859  \nSaya Sapakova  \nCand. Sc. (Physics and Mathematics), Associate Professor  \n[s.sapakova@iitu.edu.kz](s.sapakova@iitu.edu.kz), [orcid.org/0000-0001-6541-6806](orcid.org/0000-0001-6541-6806)  \nInternational University of Information Technology, Kazakhstan  \nZhansaya Bekaulova  \nMaster of Technical Sciences, Assistant Professor  \n[zh.bekaulova@iitu.edu.kz](zh.bekaulova@iitu.edu.kz), [orcid.org/0009-0000-9339-9222](orcid.org/0009-0000-9339-9222)  \nInternational Information Technology University, Kazakhstan  \nAlmas Nurlanuly  \nMaster of technical science, Senior Lecturer  \n[a.nurlanuly@agakaz.kz](a.nurlanuly@agakaz.kz), [orcid.org/0000-0002-0364-0455](orcid.org/0000-0002-0364-0455)  \nCivil Aviation Academy, Kazakhstan  \nDuriya Daniyarova  \nPhD of Technical Science, Associate Professor,  \n[duriya.daniyarova@mail.ru](duriya.daniyarova@mail.ru)., [orcid.org/0009-0000-5730-7407](orcid.org/0009-0000-5730-7407)  \n[International Educational corporation. Kazakh American University](International Educational corporation. Kazakh American University), Kazakhstan  \nGaliya Ybytayeva  \nPhD, Associate Professor, Department of Technical and Natural Sciences [ybytayeva.galiya@gmail.com](ybytayeva.galiya@gmail.com), [orcid.org/0000-0002-4243-0928](orcid.org/0000-0002-4243-0928)  \nInternational Educational Corporation, Kazakhstan  \nAizhan Kaldybayeva  \nSenior Lecturer, Department of Information Technology and Librarianship [aizhan.seisebek@gmail.com](aizhan.seisebek@gmail.com), [orcid.org/0000-0002-2062-182X](orcid.org/0000-0002-2062-182X)  \nKazakh National Women’s Teacher Training University, Kazakhstan  \nDEVELOPMENT OF MACHINE LEARNING METHODS FOR  \nMARKET TRENDS  \nAbstract: In the rapidly evolving real estate market, the application of machine learning (ML) is crucial for understanding and predicting price trends. This study evaluates and compares seven ML models, including multiple linear regression, random forest regression, support vector regression (SVR), decision tree regression, and XGBoost, to determine the most effective predictor of real estate prices in Astana, Kazakhstan. The study focuses on the Yesil district, a key area in the city, utilizing a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan. Through rigorous experimentation, model performance was assessed using statistical metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared). The results indicate that the Random Forest Regressor and XGBRegressor models outperformed others, achieving the highest R-squared values (99.55% and 99.18%, respectively) and the lowest MAE and RMSE values. These findings highlight their robustness in predicting housing prices with high accuracy. The primary objective of this study was to develop a precise ML model capable of accurately forecasting real estate prices in Astana based on key market attributes. The superior predictive performance of the Random Forest and XGBRegressor models justifies their selection for deployment in real-world applications. Their high predictive accuracy suggests their potential utility for real estate professionals, policymak  \nCopyright © 2025, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 29.10.2024 Accepted: 21.02.2025 Published: 30.03.2025  \n34  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 21, MARCH 2025  \ners, and investors seeking data-driven insights into market dynamics. This research expands knowledge on the applications of ML in the real estate sector, reinforcing the importance of evidence-based decision-making within the industry.  \nKeywords: machine learning; real estate; data processing; regression anal","cbCaijhwWAaBboNk","https://ap.wps.com/l/cbCaijhwWAaBboNk","pdf",2539552,1,16,"English","en",105,"# Introduction\n## Objectives and Scope\n# Methods and Model Comparison\n## Dataset and Evaluation Metrics\n# Results and Discussion\n## Best-Performing Models","[{\"question\":\"Which machine learning models are compared for real estate price prediction?\",\"answer\":\"The study compares seven regression models, including multiple linear regression, random forest regression, support vector regression, decision tree regression, and XGBoost (with XGBRegressor).\"},{\"question\":\"What dataset size and source are used for the Astana case study?\",\"answer\":\"The work uses a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan, focusing on Astana’s Yesil district.\"},{\"question\":\"How is model performance evaluated in the study?\",\"answer\":\"Performance is assessed using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared).\"},{\"question\":\"Which models deliver the highest predictive accuracy?\",\"answer\":\"Random Forest Regressor and XGBRegressor outperform the others, reaching the highest R-squared values (99.55% and 99.18%) with the lowest MAE and RMSE.\"}]","DEVELOPMENT OF MACHINE LEARNING METHODS FOR MARKET TRENDS | 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machine learning models are compared for real estate price prediction?","Question",{"text":76,"@type":77},"The study compares seven regression models, including multiple linear regression, random forest regression, support vector regression, decision tree regression, and XGBoost (with XGBRegressor).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset size and source are used for the Astana case study?",{"text":81,"@type":77},"The work uses a dataset of over 9,000 records extracted from a broader collection of more than 30,000 real estate transactions across Kazakhstan, focusing on Astana’s Yesil district.",{"name":83,"@type":74,"acceptedAnswer":84},"How is model performance evaluated in the study?",{"text":85,"@type":77},"Performance is assessed using mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R-squared).",{"name":87,"@type":74,"acceptedAnswer":88},"Which models deliver the highest predictive accuracy?",{"text":89,"@type":77},"Random Forest Regressor and XGBRegressor outperform the others, reaching the highest R-squared values (99.55% and 99.18%) with the lowest MAE and RMSE.","https://schema.org",{"og:url":52,"og:type":92,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":94,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":97},[98,102,106,110,115,120,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Story & 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