"Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists" by Alice Zheng and Amanda Casari is a comprehensive guide to the principles and techniques of feature engineering for machine learning. The book is aimed at data scientists and machine learning practitioners who want to develop a deep understanding of feature engineering.
The book covers various topics related to feature engineering, including data cleaning, feature selection, feature transformation, and feature creation. The authors provide practical examples and case studies throughout the book to illustrate the concepts being discussed.
One of the strengths of the book is that it provides a clear and concise explanation of the importance of feature engineering in machine learning. The authors explain how feature engineering can impact the performance of a machine learning model and provide practical guidance on how to develop effective feature engineering strategies.
The book also covers important topics such as dimensionality reduction, handling missing data, and dealing with imbalanced datasets. The authors provide clear explanations of these topics, making it easy for readers to understand the underlying concepts.
Overall, "Feature Engineering for Machine Learning" is an excellent resource for data scientists and machine learning practitioners who want to develop a deep understanding of feature engineering. The book is well-written, easy to follow, and provides practical guidance on how to develop effective feature engineering strategies.
