Award Price Estimator for Public Procurement Auctions Using Machine Learning Algorithms: Case Study with Tenders from Spain

Abstract

The public procurement process plays an important role in the efficient use of public resources. In this context, the evaluation of machine learning techniques that are able to predict the award price is a relevant research topic. In this paper, the suitability of a representative set of machine learning algorithms is evaluated for this problem. The traditional regression methods, such as linear regression and random forest, are compared with the less investigated paradigms, such as isotonic regression and popular artificial neural network models. Extensive experiments are conducted based on the Spanish public procurement announcements (tenders) dataset and employ diverse error metrics and implementations in WEKA and Tensorflow 2.