[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119885-en":3,"doc-seo-119885-105":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},119885,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Web Application with Machine Learning for House Price Prediction - Paper","House prices vary over time due to multiple attributes, making accurate purchase and sale estimation a key challenge for real estate agencies. The work develops a machine learning model in Azure ML Studio and a companion web application to predict prices for urban and rural houses based on their characteristics, minimizing forecast error. Following standard ML construction stages and the Rational Unified Process, the model uses linear regression and reaches R² of 95%. Prediction quality is assessed via expert judgment on efficiency, usability, and functionality, yielding an average score of 4.88, indicating very high quality.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 17 No. 23 (2023) |   \n[https://doi.org/10.3991/ijim.v17i23.38073](https://doi.org/10.3991/ijim.v17i23.38073)  \nPAPER  \nWeb Application with Machine Learning for House Price Prediction  \nRaúlJáuregui-Velarde1, Laberiano Andrade-Arenas1, Domingo Hernández Celis2, Roberto Carlos Dávila-Morán3, Michael Cabanillas-Carbonell4(􀀍)  \n1Universidad Privada Norbert Wiener, Lima, Perú  \n2Universidad Nacional  \nFederico Villarreal, Lima, Perú  \n3Universidad Continental, Huancayo, Perú  \n4Universidad Privada del Norte, Lima, Perú  \n[mcabanillas@ieee.org](mcabanillas@ieee.org)  \nABSTRACT  \nEvery year, the price of a house changes due to different aspects, so accurately estimating the buying and selling price is a problem for real estate agencies. Therefore, the research work aims to build a Machine Learning (ML) model in Azure ML Studio and a web application to predict the buying and selling price of two types of houses: urban and rural houses, according to their characteristics, to minimize the forecast error in prediction. Following the basic stages of machine learning construction, we build the prediction model and the Rational Unified Process (RUP) methodology to build the web application. As a result, we obtained a model trained with a linear regression algorithm and a predictive ML model with a coefficient of determination of 95% and a web application that consumes the prediction model through an Application Programming Interface (API) that facilitates price prediction to customers. The quality of the prediction system was evaluated by expert judgment; they evaluated efficiency, usability, and functionality. After the calculation, they obtained an average quality of 4.88, which indicates that the quality is very high. In conclusion, the developed prediction system facilitates real estate agencies and their customers the accurate prediction of the price of urban and rural housing, minimizing accuracy errors in price prediction. Benefiting all people interested in the real estate world.  \nKEYWORDS  \nhouse price, linear regression, machine learning, price prediction, web application  \n1 INTRODUCTION  \nEveryone wants to buy and live in a house with features that suit their lifestyle and offer amenities that meet their needs, but predicting the price of a house is very difficult, as it is constantly changing [1] . Therefore, setting a price for the purchase and sale of a house is a process that must be analyzed in depth. The price at which the house is marketed directly affects the profitability of real estate agencies. In that sense, the price of the house plays an important role in the economy and is of great importance to the various interacting stakeholders, including homeowners, buyers,  \nJáuregui-Velarde, R., Andrade-Arenas, L., Celis, D. H., Dávila-Morán, R.C., Cabanillas-Carbonell, M. (2023) . Web Application with Machine Learning for House Price Prediction. International Journal of Interactive Mobile Technologies (iJIM), 17(23), pp. 85–104. [https://doi.org/10.3991/ijim.v17i23.38073](https://doi.org/10.3991/ijim.v17i23.38073)[ ](https://doi.org/10.3991/ijim.v17i23.38073)[Article submitted 2023-01-07. Revision uploaded 2023-02-13. Final acceptance 2023-02-15.](Article submitted 2023-01-07. Revision uploaded 2023-02-13. Final acceptance 2023-02-15.)  \n© 2023 by the authors of this article. Published under CC-BY.  \niJIM | Vol. 17 No. 23 (2023) International Journal of Interactive Mobile Technologies (iJIM) 85  \nJáuregui-Velarde et al.  \nbanks, real estate developers, real estate agents, among others [1], [2] . Moreover, since price is a crucial factor in the sale of a house, it is advisable to set the right purchase and sale price from the beginning to obtain the desired results.  \nThe price of housing increases every year, due t","cbCaivpkiahh8fPC","https://ap.wps.com/l/cbCaivpkiahh8fPC","pdf",2769511,1,20,"English","en",105,"# Introduction\n# Literature Review","[{\"question\":\"What goal does the research achieve for real estate pricing?\",\"answer\":\"It builds a machine learning model and a web application that estimate buying and selling prices for urban and rural houses based on their characteristics, aiming to reduce prediction accuracy errors.\"},{\"question\":\"Which machine learning approach and platform are used?\",\"answer\":\"The model is trained in Azure ML Studio and uses a linear regression algorithm for price prediction.\"},{\"question\":\"How is the predictive system evaluated and what results are reported?\",\"answer\":\"Experts evaluate efficiency, usability, and functionality, producing an average quality score of 4.88, indicating very high quality. The predictive model also reports a coefficient of determination of 95%.\"}]","Web Application with Machine Learning for House Price Prediction - Paper | PDF",1785726834,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"web-application-with-machine-learning-for-house-price-prediction-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/web-application-with-machine-learning-for-house-price-prediction-paper/119885/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What goal does the research achieve for real estate pricing?","Question",{"text":75,"@type":76},"It builds a machine learning model and a web application that estimate buying and selling prices for urban and rural houses based on their characteristics, aiming to reduce prediction accuracy errors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning approach and platform are used?",{"text":80,"@type":76},"The model is trained in Azure ML Studio and uses a linear regression algorithm for price prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the predictive system evaluated and what results are reported?",{"text":84,"@type":76},"Experts evaluate efficiency, usability, and functionality, producing an average quality score of 4.88, indicating very high quality. 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