Work place: Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, 35900 Tg. Malim Perak.
Research Interests: Computer systems and computational processes, Computer Architecture and Organization, Database Management System, Data Structures and Algorithms
Dr. Nor Hasbiah Ubaidullah is an Associate Professor at Universiti Pendidikan Sultan Idris. She received degree in Computer Science from Universiti Kebangsaan Malaysia and Master of Science in Information System from University of Salford. She obtained her PhD degree in Information Technology from Universiti Kebangsaan Malaysia. She has been teaching in higher education institutions since 1991 in several subjects such as Programming, Courseware Engineering and Information Systems Development. Her research areas include Data Management, Educational Software and Computer Science Education.
DOI: https://doi.org/10.5815/ijmecs.2020.06.04, Pub. Date: 8 Dec. 2020
Data mining is now commonly applied in the real estate market. Data mining's ability to extract relevant knowledge from raw data makes it very useful to predict house prices, key housing attributes, and many more. Research has stated that the fluctuations in house prices are often a concern for house owners and the real estate market. A survey of literature is carried out to analyze the relevant attributes and the most efficient models to forecast the house prices. The findings of this analysis verified the use of the Artificial Neural Network, Support Vector Regression and XGBoost as the most efficient models compared to others. Moreover, our findings also suggest that locational attributes and structural attributes are prominent factors in predicting house prices. This study will be of tremendous benefit, especially to housing developers and researchers, to ascertain the most significant attributes to determine house prices and to acknowledge the best machine learning model to be used to conduct a study in this field.[...] Read more.
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