Prediction and Visualisation of SICONV Project Profiles Using Machine Learning

Background: Inefficient use of public funds can have a negative impact on the lives of citizens. The development of machine learning-based technologies for data visualisation and prediction has opened the possibility of evaluating the accountability of publicly funded projects. Methods: This study describes the conception and evaluation of the architecture of a system that can be utilised for project profile definition and prediction. The system was used to analyse data from 20,942 System of Management of Agreements and Transfer Contracts (SICONV) projects in Brazil, which are government-funded projects. SICONV is a Brazilian Government initiative that records the entire life cycle of agreements, transfer contracts, and partnership terms, from proposal formalisation to final accountability. The projects were represented by seven variables, all of which were related to the timeline and budget of the project. Data statistics and clustering in a lower-dimensional space calculated using t-SNE were used to generate project profiles. Performance measures were used to test and compare several project-profile prediction models based on classifiers. Results: Data clustering was achieved, and ten project profiles were defined as a result. Among 25 prediction models, k-Nearest-Neighbor (kknn) was the one that yielded the highest accuracy (0.991 +/- 0.002). Conclusions: The system predicted SICONV project profiles accurately. This system can help auditors and citizens evaluate new and ongoing project profiles, identifying inappropriate public funding.

Automated summary

This study describes the conception and evaluation of a system for defining and predicting project profiles. By analyzing data from government-funded SICONV projects in Brazil, data clustering was achieved and ten project profiles were defined. Among multiple prediction models, the k-Nearest-Neighbor model performed the best with high accuracy.