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Government Construction Project Budget Prediction Using Machine Learning

Wuttipong Kusonkhum 1, Korb Srinavin 1, Narong Leungbootnak 1, Preenithi Aksorn 1, and Tanayut Chaitongrat 2
1. Department of Civil Engineering, Khon Kaen University, Khon Kaen, Thailand
2. Faculty of Architecture, Urban Design and Creative Arts, Mahasarakham University, Mahasarakham, Thailand

Abstract—The construction industry could not avoid the technology disruptive era. Therefore, the Thai government has created a new policy and directed all departments to implement big data technology. Big data technology includes Machine Learning (ML). The present study attempts to predict over-budget construction projects using an ML algorithm. Data were collected from the comptroller general’s department of Thailand for over-budget project cases. Information about 692 projects completed in Thailand in 2019, covering all types of construction projects, was collected and analyzed. ML, an analytical technique for big data technology, was used as a tool in this study. In addition, k-Nearest Neighbors (KNN), an ML algorithm, was used to classify over-budget projects. The input data have four attributes: department of project, construction site location, type of project, and methods of procurement; the output is a yes/no decision on whether a project has been over budget. The dataset was preprocessed for analysis and modeled using the KNN function in Python 3. According to the test results, the KNN model achieves an accuracy (precision) of 0.86. Finally, the developed model has demonstrated that it can be used to predict the over-budget construction projects for the Thai government.
 
Index Terms—government construction project, big data technology, machine learning, k-nearest neighbors, over-budget project

Cite: Wuttipong Kusonkhum, Korb Srinavin, Narong Leungbootnak, Preenithi Aksorn, and Tanayut Chaitongrat, "Government Construction Project Budget Prediction Using Machine Learning," Journal of Advances in Information Technology, Vol. 13, No. 1, pp. 29-35, February 2022. doi: 10.12720/jait.13.1.29-35

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.