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.