Web Application with Machine Learning for House Price Prediction

Raúl Jáuregui-Velarde, Laberiano Andrade-Arenas, Domingo Hernández Celis, Roberto Carlos Dávila-Morán, Michael Cabanillas-Carbonell

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Resumen

Every 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.

Idioma originalInglés estadounidense
Páginas (desde-hasta)85-104
-20
PublicaciónInternational Journal of Interactive Mobile Technologies
Volumen17
N.º23
DOI
EstadoIndizado - 2023

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© 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

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