[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128596-en":3,"doc-seo-128596-105":30,"detail-sidebar-cat-0-en-105":92},{"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},128596,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Development of a decision-making module in the field of real estate rental using machine learning methods","The study develops a prototype decision-support information system for managers in the real estate rental industry, focused on improving complex managerial choices through data analysis, mathematical modeling, and expert knowledge. It investigates practical DSS design and specifies functional requirements for the proposed system. The implementation uses Python and integrates machine learning models and an expert system, including linear regression, DBSCAN, and backpropagation training for a classifying perceptron. The tool delivers analysis and forecasting capabilities to support effective management in dynamic rental scenarios.","Development of a decision-making module in the field of real estate rental using machine learning methods  \nAyagoz Mukhanova1, Madiyar Baitemirov1, Artyom Ignatovich2, Aigulim Bayegizova3, Adilbek Tanirbergenov4, Assemgul Tynykulova5, Ideyat Bapiyev6, Galiya Mukhamedrakhimova2  \n1Department of Information Systems, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan 2Doctor of Business Administration Program, Maqsut Narikbayev University, Astana, Republic of Kazakhstan 3Department of Radio Engineering, Electronics and Telecommunications, L. N. Gumilyov Eurasian National University, Astana,  \nKazakhstan  \n4Department of Algebra and Geometry, L. N. Gumilyov Eurasian National University, Astana, Kazakhstan 5Higher School of Information Technology and Engineering, Astana International University, Astana, Kazakhstan 6Department of Information Technology, Faculty of Technology, Zhangir Khan University, Uralsk, Kazakhstan  \nArticle history:  \nReceived Mar 6, 2024 Revised Jun 9, 2024 Accepted Jun 16, 2024  \nKeywords:  \nDecision support systems  \nDBSCAN Expert system Linear regression Machine learning  \nCorresponding Author:  \nThe research is aimed at developing a prototype of a decision support information system for managers of a company operating in the real estate rental industry. The system provides tools for data analysis, the use of mathematical models and expert knowledge to solve complex problems. The work analyzes the practical aspects of the design and use of decision support systems and formulates the requirements for the functionality of the system being developed. The Python programming language was used for implementation. The prototype includes machine learning models, expert systems, user interface and reports. Linear regression, data clustering density-based spatial clustering of applications with noise (DBSCAN) and backpropagation methods were implemented to train the classifying perceptron. The developed tool represents a significant contribution to the field of decision support, providing unique analysis and forecasting capabilities in the dynamic real estate rental environment. This prototype isan innovative solution that promotes effective management and strategic decision making in complex real estate business scenarios.  \nThis is an open access article under the CC BY-SA license.  \nMadiyar Baitemirov  \nDepartment of Information Systems, L. N. Gumilyov Eurasian National University 010000 Astana, Kazakhstan  \n[Email: madiyar.baytemirov@inbox.ru](Email: madiyar.baytemirov@inbox.ru)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn modern conditions of increasing volumes of information and increasing complexity of tasks in enterprise management, especially in the field of rental real estate [1]–[3], the relevance of developing decision support systems (DSS) [4], [5] becomes undeniable. The growth in the amount of data every year acquires a magnitude that exceeds the capabilities of human processing [6] . Consequently, there is a rapid need to develop more tools to manage data [7] and make better decisions in specific areas. Decision support systems are an important tool that can help organizations manage vast amounts of information and make informed decisions. Automation of data analysis, the use of intelligent algorithms and models [8], as well asthe provision of intuitive reports and graphs-all this allows you to effectively manage data [9], identify risks, predict trends and adapt to changes in the external environment. In this research work, the focus is on developing a prototype information system for decision support in the field of real estate rental using machine learning methods [10]–[12] . The goal of the work is to create a module capable of optimizing  \ninformation processes and providing more efficient enterprise management. The object of the study is the developed prototype, and the subject is the theoretical foundations, design methods and effective implementation of a decision support system","cbCaifot8Ju7nqmR","https://ap.wps.com/l/cbCaifot8Ju7nqmR","pdf",642095,1,13,"English","en",105,"# Introduction\n## Decision support systems and data-driven automation\n## Prototype development goals and research focus\n# Methods and Implementation\n## Machine learning models and training approach\n## Expert system integration and reporting","[{\"question\":\"What is the main objective of the proposed system in the real estate rental domain?\",\"answer\":\"It aims to create a decision-support module that optimizes information processes and enables more efficient enterprise management for managers in the rental real estate industry.\"},{\"question\":\"Which machine learning methods are implemented in the prototype?\",\"answer\":\"The prototype implements linear regression, DBSCAN (density-based spatial clustering of applications with noise), and backpropagation methods to train a classifying perceptron.\"},{\"question\":\"What key functionalities does the prototype provide to decision makers?\",\"answer\":\"It supports data analysis, applies mathematical models and expert knowledge to solve complex problems, and automates report generation with intuitive outputs for faster decision making.\"}]","Development of a decision-making module in the field of real estate rental using machine learning methods | 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