Why you should do Feature Engineering first, Hyperparameter Tuning second as a Data Scientist

In fact, the realization that feature engineering is more important than hyperparameter tuning came to me as a lesson -- an awakening and vital lesson -- that drastically changed how I approached problems and handled data even before building any machine learning models. When I first started my first full time job as a research engineer in machine learning, I was so excited and obsessed with building fancy machine learning models without really paying much attention to the data that I had. As a matter of fact, I was impatient. I wanted results so badly that I only cared about squeezing every single percent of performance out of my model. Needless to say, I failed after so many attempts and wondered why.