
The success of a machine learning project often hinges not on the sophistication of the algorithm chosen but on the craftsmanship of the features provided to it, making feature engineering both the most impactful and the most resource-intensive stage of the development cycle. Practitioners have long treated this phase as an art form, relying on domain expertise and painstaking manual










