Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia
Thuraiya Mohd1, Suraya Masrom2, Noraini Johari3
1Thuraiya Mohd, Department of Architecture, Planning and Surveying, Universiti Teknologi MARA, Perak Branch, Seri Iskandar, Malaysia.
2Suraya Masrom, Department of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, Malaysia.
3Noraini Johari, Department of Architecture, Planning and Surveying, Universiti Teknologi MARA, Perak Branch, Seri Iskandar, Malaysia.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 542-546 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10840982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1084.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.
Keywords: House Pricing, Machine Learning, Prediction, Real Dataset.
Scope of the Article: Machine Learning