House price prediction using Artificial Neural Network with ADAGRAD optimizer

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dc.contributor.authorAbdulwahid, Ehab Saad
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorAbdulrazzaq, Mohammed Majid
dc.date.accessioned2024-12-12T08:15:23Z
dc.date.available2024-12-12T08:15:23Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.descriptionVolume Editors : Garcia F.P., Ramirez I.S., Jamil A., Hameed A.A., Ortis A.
dc.description.abstractThe real estate market is a dynamic and complex ecosystem influenced by myriad factors, making accurate price predictions a formidable challenge. Understanding the intricate relationships between variables such as location, property characteristics, economic indicators, and market trends is essential for making informed investment decisions. In this paper, we undertake a comprehensive exploration of machine learning and artificial neural networks (ANNs), establishing a framework to understand how these powerful computational tools can be harnessed to solve complex problems across various domains. The novel part of this study involves a comparative analysis of different optimization algorithms, including AdaGrad, Adam, SGD, and RMSprop, in the context of their application to ANN in real estate price prediction. AdaGrad’s unique approach to handling learning rates dynamically makes it particularly suited for data with varying scales, as demonstrated by our experimental results Comparative training and testing outcomes revealed that AdaGrad consistently outperformed other methods, showcasing lower mean squared error (MSE) and root mean squared error (RMSE), along with higher R2 values, indicating its superior predictive accuracy and efficiency. These findings underline the potential of AdaGrad to optimize real estate predictive models more effectively than traditional methods, marking a significant advancement in the application of machine learning techniques in real estate market analysis.en_US
dc.identifier.citationAbdulwahid, E. S., Ibrahim, A. A., Abdulrazzaq, M. M. (2024). House price prediction using Artificial Neural Network with ADAGRAD optimizer. Lecture Notes in Networks and Systems, 1138 LNNS, 761-776. 10.1007/978-3-031-70924-1_58en_US
dc.identifier.endpage776en_US
dc.identifier.isbn9783031709234
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85210550221
dc.identifier.scopusqualityQ4
dc.identifier.startpage761en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5082
dc.identifier.volume1138 LNNSen_US
dc.indekslendigikaynakScopus
dc.institutionauthorAbdulwahid, Ehab Saad
dc.institutionauthorÄ°brahim, Abdullahi Abdu
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.isversionof10.1007/978-3-031-70924-1_58en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectADAGRADen_US
dc.subjectANNen_US
dc.subjectHousing Price Predictionen_US
dc.subjectReal Estate Analyticsen_US
dc.titleHouse price prediction using Artificial Neural Network with ADAGRAD optimizer
dc.typeConference Object

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