06-23-2026, 03:57 PM
Feature Engineering and Selection
BY Max Kuhn and Kjell Johnson
Summary
Feature Engineering and Selection: A Practical Approach for Predictive Models explores one of the most important steps in modern machine learning: transforming raw data into meaningful information that allows predictive models to perform better. The book explains how feature engineering and feature selection help data scientists improve model accuracy by creating better representations of variables, identifying useful patterns, and avoiding unnecessary complexity.
In today’s world of artificial intelligence and data-driven decision making, having large amounts of data is not enough — the quality and structure of that data determine the success of a model. This book provides practical insights into predictive modeling, data preprocessing, visualization, handling missing information, and selecting the most valuable features.
A valuable resource for machine learning enthusiasts, data analysts, and AI professionals, it highlights how thoughtful data preparation can turn ordinary datasets into powerful tools for prediction and discovery. By combining theory with practical techniques, the book shows why feature engineering remains a key skill in building reliable and effective artificial intelligence systems.
BOOK
BY Max Kuhn and Kjell Johnson
Summary
Feature Engineering and Selection: A Practical Approach for Predictive Models explores one of the most important steps in modern machine learning: transforming raw data into meaningful information that allows predictive models to perform better. The book explains how feature engineering and feature selection help data scientists improve model accuracy by creating better representations of variables, identifying useful patterns, and avoiding unnecessary complexity.
In today’s world of artificial intelligence and data-driven decision making, having large amounts of data is not enough — the quality and structure of that data determine the success of a model. This book provides practical insights into predictive modeling, data preprocessing, visualization, handling missing information, and selecting the most valuable features.
A valuable resource for machine learning enthusiasts, data analysts, and AI professionals, it highlights how thoughtful data preparation can turn ordinary datasets into powerful tools for prediction and discovery. By combining theory with practical techniques, the book shows why feature engineering remains a key skill in building reliable and effective artificial intelligence systems.
BOOK
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