A new data mining-based framework to predict the success of private participation in infrastructure projects

Muhammad Ayat, Byunghoon Kim, Chang Wook Kang

Research output: Contribution to journalArticlepeer-review

Abstract

The study aims to propose a data mining-based framework to predict the success of private participation in infrastructure projects in developing countries. Data have been collected from the World Bank’s maintained PPI projects database. The proposed framework in this study consists of imputation of missing values, selection of significant features method, resampling imbalanced classes, and application of classification algorithms, including random forest, logistic regression, and support vector machines to predict the binary classes (project success). The results suggest multivariate imputation by chained equations(MICE) as the best method for the imputation, Boruta for the feature selection method, and logistic regression for the classification to predict binary classes in PPI project dataset. The major contribution of this study is that it builds a new data mining-based framework, which considers different feature selection methods and classification techniques. This study will help the practitioners to predict the success of projects carried out under different contractual arrangements and adopt different proactive project management approaches.
Original languageEnglish
Number of pages9
JournalInternational Journal of Construction Management
Early online date7 Mar 2022
DOIs
Publication statusE-pub ahead of print - 7 Mar 2022
Externally publishedYes

Keywords

  • logistic regression
  • random forest
  • support vector machine
  • feature selection methods
  • oversampling

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