The video tutorial discusses feature engineering, emphasizing its role in improving model discrimination and calibration. It explains how to create new features like total income and EMI, and the importance of normalizing data and dropping correlated features. The tutorial provides code examples and demonstrates improvements in model performance, particularly in terms of the area under the curve and calibration plots.

What new insight or understanding did you gain from this video?