[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121828-en":3,"doc-seo-121828-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},121828,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Comparative analysis of machine learning algorithms for predicting Dubai property prices","Machine learning algorithms are compared for forecasting Dubai property prices, with emphasis on evaluating eight established models: EEMD-SD-SVM, SVM, gradient boosting, random forest, KNN, linear regression, artificial neural networks, and decision trees. Model quality is assessed using R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). Results describe which algorithms perform best under different boundary structures, data complexity, local pattern capture, and interpretability needs. Practical guidance highlights tuning, feature selection, and data preprocessing for improved predictive power.","TYPE Original Research PUBLISHED 13 February 2024 DOI 10.3389/fams.2024.1327376  \nOPEN ACCESS  \nEDITED BY  \nXueying Zeng,  \nOcean University of China, China  \nREVIEWED BY  \nDeepak Gupta,  \nNational Institute of Technology Arunachal Pradesh, India  \nMhamed Mesfioui,  \nUniversité du Québec à Trois-Rivières, Canada  \n*CORRESPONDENCE  \nAbdulsalam Elnaeem Balila  \n [abdalsalam153@hotmail.com](abdalsalam153@hotmail.com)  \nRECEIVED 24 October 2023  \nACCEPTED 15 January 2024  \nPUBLISHED 13 February 2024  \nCITATION  \nElnaeem Balila A and Shabri AB (2024) Comparative analysis of machine learning algorithms for predicting Dubai property prices.  \nFront. Appl. Math. Stat. 10:1327376 .  \ndoi: 10.3389/fams.2024.1327376  \nCOPYRIGHT  \n© 2024 Elnaeem Balila and Shabri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nComparative analysis of machine learning algorithms for predicting Dubai property prices  \nAbdulsalam Elnaeem Balila * and Ani Bin Shabri  \nDepartment of Mathematics, Faculty of Science, University of Technology Malaysia, Skudai, Johor, Malaysia  \nIntroduction: Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)–stochastic (S) + deterministic (D)–support vector machine (EEMDSD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context.  \nMethods: The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) . These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability.  \nResults: Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices.  \nDiscussion: The study emphasizes the significance of model tuning, featureselection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluat","cbCaiiVVLRIR3j06","https://ap.wps.com/l/cbCaiiVVLRIR3j06","pdf",1073600,1,10,"English","en",105,"# Introduction\n## Methods\n## Results\n## Discussion","[{\"question\":\"Which machine learning algorithms are evaluated for predicting Dubai property prices?\",\"answer\":\"The study compares eight algorithms: EEMD-SD-SVM, SVM, gradient boosting, random forest, KNN, linear regression, artificial neural networks (ANN), and decision trees.\"},{\"question\":\"What performance metrics are used to assess predictive accuracy?\",\"answer\":\"Four metrics are used: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).\"},{\"question\":\"How do the results guide algorithm selection for different data characteristics?\",\"answer\":\"The findings indicate that EEMD-SD-SVM and SVM suit scenarios needing precise decision boundaries, gradient boosting and random forests handle complex/noisy data well, KNN captures localized patterns, linear regression fits straightforward regression, ANN benefits from large datasets, and decision trees improve interpretability.\"}]","Comparative analysis of machine learning algorithms for predicting Dubai property prices | 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machine learning algorithms are evaluated for predicting Dubai property prices?","Question",{"text":75,"@type":76},"The study compares eight algorithms: EEMD-SD-SVM, SVM, gradient boosting, random forest, KNN, linear regression, artificial neural networks (ANN), and decision trees.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What performance metrics are used to assess predictive accuracy?",{"text":80,"@type":76},"Four metrics are used: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE).",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results guide algorithm selection for different data characteristics?",{"text":84,"@type":76},"The findings indicate that EEMD-SD-SVM and SVM suit scenarios needing precise decision boundaries, gradient boosting and random forests handle complex/noisy data well, KNN captures localized patterns, linear regression fits straightforward regression, ANN benefits from large 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