Feature Engineering: Techniques and Applications

Resumen

Machine Learning is a rising concept in today's society. In the past decade, ML-based systems have become part of people's daily routines, and their usage has been disseminated through diverse sectors. This evolution is supported by the exponential increase in data created worldwide. Feature Engineering is a critical process focused on transforming data into suitable inputs for Machine Learning algorithms. This work explores the Feature Engineering process by developing a baseline for its implementation. Hence, a pipeline of Feature Engineering techniques and their taxonomy is proposed, along with a set of R scripts to implement. The validity of the code is then demonstrated through its application to a real-world dataset.