Ismail Ibrahim

Work place: Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Selangor



Research Interests: Computer systems and computational processes, Artificial Intelligence, Image Compression, Image Manipulation, Image Processing, Combinatorial Optimization


Dr. Ismail Ibrahim is working as a lecturer at Universiti Teknologi Mara, Malaysia. He received a degree in Electrical Engineering from Universiti Sains Malaysia and a master’s in electrical engineering. He obtained his PhD degree in Electrical Engineering from Universiti Malaysia Pahang. He has been working in the industry since 2005. His research areas include Computational Intelligence, Optimization, Image Processing and Artificial Intelligence.

Author Articles
House Price Prediction using a Machine Learning Model: A Survey of Literature

By Nor Hamizah Zulkifley Shuzlina Abdul Rahman Nor Hasbiah Ubaidullah Ismail Ibrahim

DOI:, 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.

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