Machine Learning aplicado a un desarrollo de categorización de comisiones para proveedores.

Abstract

This thesis explores the analysis and categorization of commissions by suppliers, utilizing a decision tree model as the primary tool for evaluation and classification. The main objective is to provide an analytical framework that facilitates the understanding and optimization of commission structures, aiding in informed decision-making within the context of commercial negotiations. A detailed study is conducted on the various factors influencing commissions, as well as the benefits that effective categorization can offer to organizations. The methodology includes the collection of relevant data, its processing through advanced data analysis techniques, and the implementation of predictive models that allow the visualization of patterns and trends in commissions granted by various suppliers. The results highlight the importance of accurate classification and how it can impact the profitability and operational efficiency of companies. Finally, practical recommendations are presented for the application of the findings in real business environments.