Work place: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam
Research Interests: Computer Science & Information Technology, Computational Science and Engineering, Data Structures and Algorithms
Nor Hamizah Zulkifley received her BSc. degree of Actuarial Science in 2019 from University Teknologi Mara. Currently, she is a master’s degree student of Data Science at the Faculty of Computer and Mathematical Science in the same university.
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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