Improving the Prediction of Project Success in the Telecom Sector by Means of Advanced Data Balancing

Abstract

As governments access capital, technology, and managerial expertise from private investors, there is an increasing trend of privatizations in infrastructure projects worldwide. Given the large size of these investments, notably in the sector of telecommunications, investors typically create a consortium with other interested firms, an investment vehicle known as private participation project (PPP). Given the critical repercussions on the rest of the economy, and its large financial losses in case of failure, the prediction of the success of PPPs in telecommunications is of utmost importance. The success of PPPs can be predicted by Machine Learning and it is probed in this article. Hence, widely acknowledged classifiers (k-nearest neighbors [k-NNs], support vector machines [SVMs], and random forest [RF]) are applied to PPPs publicly available data from the World Bank. The results on this highly imbalanced dataset are greatly improved by the application of data balancing techniques. It includes some standard ones (random oversampling, random undersampling, and synthetic minority oversampling technique [SMOTE]), together with some other advanced ones (density-based SMOTE and borderline SMOTE). The satisfactory results validate the proposed application of classifiers on the dataset improved by data-balancing techniques.