Machine Learning-Based Web System for Predicting and Classifying Financial Incentives in the Automotive Sector

This research presents the development of a web-based system using machine learning to predict and classify financial incentives in the automotive sector, contributing to Sustainable Development Goal 9 (Industry, Innovation and Infrastructure) and SDG 12 (Responsible Consumption and Production). The main objective was to design and implement an intelligent system that enhances decision-making regarding incentives such as exemptions (EXEM), natural gas subsidies (GNT), and tax benefits (TAX). The study employed a quantitative approach, applied type, and pre-experimental design, assessing model performance through accuracy, error rate, and response time metrics. Results showed an accuracy of 93.44%, a 45.12% reduction in error rate, and an average response time of 0.13 seconds. It is concluded that the proposed system significantly improves efficiency in predicting financial incentives, positioning itself as a viable technological tool for the automotive sector and economic sustainability.

@article{Rivas2026,
  title     = {Machine Learning-Based Web System for Predicting and Classifying Financial Incentives in the Automotive Sector},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {3},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Antony Jesus Ramirez Rivas and Rosalynn Ornella Flores-Castañeda},
  doi       = {10.14569/IJACSA.2026.0170330},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170330}
}