The National Learning Service - SENA receives Petitions, Complaints, Claims, Suggestions, Denunciations, Acknowledgments, Congratulations and Guardianship Actions (PQRS) that must be managed to guarantee a timely response to citizens who request the solution to their requirement; additionally, it must ensure the follow-up and compliance with the regulations that regulate the management of PQRS in Colombia, as well as automating processes that are currently carried out manually. For this reason, the purpose of this project is to analyze with Machine Learning models the PQRS received by SENA, which allow mitigating the risk of materializing regulatory breaches and manage to resolve the PQRS in a timely manner for the public. For this, the SEMMA methodology is used, being the more appropriate for the analysis of large databases. It should be noted that language tools were used of Python and R programming to execute and apply the analysis of the PQRS, obtaining conclusive results and satisfactory about the Machine Learning models chosen to predict the possible violation of rights of citizenship; therefore, with these results, it is considered necessary to suggest the implementation of the models executed before SENA.