Identifying Public Tenders of Interest Using Classification Models: A Comparative Analysis

Abstract

Public procurement processes related to tenders represent one of the most important financing strategies for companies in diverse sectors, as they present the opportunity to offer services, supplies and product sales to state entities. Given the high volume of public tenders in the Colombian Government SECOP II database, manually identifying tenders of interest can be a cumbersome and time-consuming process. In this work, we propose automating the identification of interesting tenders by training a supervised classification model. We manually label a sample of tenders published in the National Government open data platform, according to whether or not they are of interest to the Minuto de Dios University, and use them for model training. Several models are evaluated in order to select the best model for deployment, taking into account various metrics to determine best performance according to business needs. The best model is selected based on different analyses, comparing the application of data balancing techniques, the performance of the proposed models, and hyper-parameter settings measured against the test data.