Feature Engineering for Modern Machine Learning with Scikit-Learn

Chapter 2 Summary

In Chapter 2, we delved into the critical role of feature engineering in enhancing predictive models for both classification and regression tasks. Feature engineering is about transforming raw data into features that improve model performance, making data more informative and representative of real-world patterns. This chapter covered a range of techniques and practical examples, focusing on predicting customer churn and customer lifetime value (CLTV) through carefully crafted features.

We started by examining the use case of churn prediction in healthcare. To predict whether a patient would disengage from a healthcare provider, we created features that captured various aspects of patient behavior and interaction. These included ...