## 5 Conclusions  
This study presents a framework based on machine learning techniques for failure machine prediction, with a particular emphasis on feature selection methodologies. By addressing a tangible industrial challenge that impacts production planning, this research offers a practical solution and delves into the importance of selecting relevant features for accurate predictions. Feature selection techniques, including mRMR and PCA, highlight crucial variables, shedding light on the most influential factors in predicting machine failures. Notably, the PCA method, emphasizing variables like product ID, air and process temperature, rotation speed, and torque, was instrumental in enhancing the performance of the RF prediction model, which demonstrated itself to be superior to SVM and MLP. The examples addressed in this work suggest that decision-makers in industries are highly recommended to invest in thermocouple sensors and thermistors for measuring air and process temperature, tachometers and magnetic encoders for measuring rotational speed, and torque sensors with magnetic and piezoelectric effects. These variables have been proven to be more significant in collecting information about torque. The example covered in this work suggests that decision-makers in industries invest in thermocouple sensors and thermistors for measuring air and process temperatures, tachometers and magnetic encoders for measuring rotational speed, and torque sensors with magnetic and piezoelectric effects. This is because these variables proved to be more significant in collecting information about the machines. In modern industrial environments, where efficiency and reliability are fundamental, investing in predictive machine failure models is essential to sustaining competitiveness and operational longevity. Furthermore, the insights gleaned from this research raise discussion regarding the significance of sensor selection, guiding industries to prioritize those that capture data that leads to machine failure predictions more effectively. This holistic approach strengthens predictive maintenance strategies and sustains operational excellence in dynamic industrial scenarios. In future work, we intend to address the problem of data imbalance by applying resampling techniques, investigate the use of metaheuristic approaches to optimize data acquisition, explore in more depth the selection of attributes by the RF algorithm itself using impurity metrics, such as the Gini index and entropy, and apply model interpretability techniques to better understand how machine learning techniques predict machine failures. Conceptualization, F.E.B., G.C.d.O.N., G.M.C., R.F.M., A.M.d.F. and M.V. ; Methodology, F.E.B., G.C.d.O.N., G.M.C., R.F.M., A.M.d.F. and M.V. ; software, F.E.B. ; Validation, F.E.B., G.C.d.O.N., G.M.C., R.F.M., A.M.d.F. and M.V. ; Formal analysis, F.E.B., G.C.d.O.N., S.A.d.A. and G.A.L. ; Investigation, F.E.B. and G.C.d.O.N. ; Resources, F.E.B. and G.C.d.O.N. ; Data curation, F.E.B. ; Writing--original draft preparation, F.E.B., G.C.d.O.N. and S.A.d.A. ; Writing--review and editing, F.E.B., G.C.d.O.N. and S.A.d.A. and M.A. ; Visualization, F.E.B., G.C.d.O.N., S.A.d.A., M.A. and M.S. ; Supervision, G.C.d.O.N. and M.A. ; Project administration, G.C.d.O.N. and M.A. ; Funding acquisition, M.S. and M.A. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the research unit on Governance, Competitiveness and Public Policy (UIDB/04058/2020) and (UIDP/04058/2020), funded by national funds through FCT--Fundacao para a Ciencia e a Tecnologia. The authors are grateful to CNPq Conselho Nacional de Desenvolvimento Cientifico e Tecnologico-Research funding in Productivity (PQ-2 09/2023-Process: 305950/2023-1). Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.