[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120748-en":3,"doc-seo-120748-105":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":4,"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},120748,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms - read online free","Accurate urban housing price forecasting supports better decisions for buyers, sellers, real estate agents, and investors, and helps interpret broader economic conditions where price changes may reflect growth or recession. This study compares three machine learning methods—Linear Regression, Random Forest, and Gradient Boosting—for house price prediction. Experiments use the Melbourne real estate dataset with 34,857 property sales and 21 features, evaluating predictive effectiveness through standard error and fit metrics.","A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms  \nLale EL Mouna 1, Hassan Silkan 1, Youssef Haynf 2, Mohamedade FaroukNann 3, Stéphane C. K. Tekouabou 4,5  \n1Laroseri Laboratory, Chouaib Doukkali University, Morocco  \n2The National School of Business and Management of Dakhla, Ibn Zohr University, Morocco  \n3Scientific Computing, Computer Science and Data Science Research Unit (CSIDS), University of Nouakchott, Mauritania  \n4 Center of Urban Systems (CUS), Mohammed VI Polytechnic University (UM6P), Hay Moulay Rachid, 43150 Benguérir, Morocco  \n5 Department of Computer and Educational Technology, Higher Teacher Training College (HTTC), University of Yaoundé I, Yaoundé, Cameroon  \nAbstract. Accurate housing price forecasts are essential for several reasons. First, it allows individuals to make informed decisions about buying or selling real estate and to determine appropriate prices. Secondly,  \nit helps real estate agents and investors make better investment decisions and negotiate contracts more  \neffectively. In addition, housing prices are often an indication of the general state of the economy. A price  \ndecrease may indicate an economic recession, while an increase in prices may signal economic growth. In  \nthis study, we proposed to address this subject by predicting house prices using machine learning by  \nchoosing three types of machine learning: Linear Regression (LN), Random Forest (RF) and  \nGradientBoosting (GB) . We tested our models on the Melbourne real estate dataset, which includes 34,857  \nproperty sales and 21 features.  \nKeywords: urban real estate, house price, machine learning, house price prediction.  \n1. Introduction  \nHousing is a critical component by which the success of a national economy can be measured. When an economy grows, people migrate from cities to rural areas, which leads to an increase in the urban population. As the urban population increases, the demand for housing also increases. The increase in demand drives up housing prices.  \nThe housing price in general is influenced by several variables. The authors [1] define these variables as physical conditions, design, and location. Physical conditions that can be observed through physical perception include the size of the property, the number of rooms, the size of the kitchen and garage, the availability of landscaped space, the land and building size, and the age of the property. The physical characteristics of a house, such as the size of the structure, the year of building, the number of bedroomsand bathrooms, and other elements that determine the internal characteristics of the house can affect the price of the house [2] . While these terms refer to various marketing tactics utilized by real estate developers to interest target investors. For example, the proximity of a property to hospitals, markets, educational centers, airports, major highways, etc. The location has a significant influence over price of a property. The  \ncurrent price of land is determined by the region. Therefore, it is not only in the interest of tenants, but also in the interest of landlords, analysts, and policymakers, as well as urban and regional planning authorities, to understand housing price patterns and their determinants [3] . A computerized forecasting system can help them in making well-informed decisions about the desirability and timing of buying a property [4,5,6,7] .  \nIn recent years, machine learning has made significant advances due to increasing computational power, the availability of large datasets and advances in algorithm development [8,9] . These advances have had a significant impact on several industries, including accurate house price forecasting. Using the power of machine learning, real estate professionals and property owners can now make more informed decisions based on reliable estimates of property values.  \nIn the past, estimating property values relied significantly on human expertise and trad","cbCaiuk5pbIrxjBG","https://ap.wps.com/l/cbCaiuk5pbIrxjBG","pdf",1043685,1,6,"English","en",105,"# Introduction\n## Housing price determinants\n## Role of machine learning\n# Related work\n# Methodology\n# Results\n# Discussion and conclusion","[{\"question\":\"Why is accurate urban house price forecasting important?\",\"answer\":\"It enables informed buying and selling decisions, supports real estate agents and investors in negotiations and investments, and provides signals about the economy.\"},{\"question\":\"Which machine learning algorithms are compared in the study?\",\"answer\":\"The study compares Linear Regression (LN), Random Forest (RF), and Gradient Boosting (GB).\"},{\"question\":\"What dataset and evaluation metrics are used?\",\"answer\":\"Experiments use the Melbourne housing dataset (34,857 sales, 21 features) and evaluate performance using MAE, R², and RMSE.\"}]","A Comparative Study of Urban House Price Prediction using Machine Learning Algorithms - 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