Predictive modeling success hinges on selecting the features that are most likely to affect the desired outcome and sub-optimal featuring engineering is one of the culprits behind poorly performing models. Today more art than science, feature engineering is a difficult process even for the most experienced data scientist.

What is feature engineering and what does the process look like

Where does feature engineering fit in the machine learning life cycle

The importance of data pipelines in feature selection

The downstream modeling impacts of feature selection

Ways that data scientists can improve this process

Solving the cold start problem by choosing the right candidate features

Integrating feature engineering into your broader modeling process

Feature Engineering: What It Is and How to Leverage It in Your Data Science Journey