Feature Engineering for Modern Machine Learning with Scikit-Learn

Mastering data preparation and transformation for robust ML models

Cuantum Technologies LLC

• Publisher: Packt Publishing
• Year: 2025
• eBook ISBN: 9781837026708
• Topic: Computer Science
• Subtopic: Data Mining
• Index: Computer Science

Master feature engineering with Scikit-Learn! Learn to preprocess, transform, and automate data for machine learning. Boost predictive accuracy with pipelines, clustering, and advanced techniques for real-world projects.

Key Features

• Comprehensive guide to feature engineering for Scikit-Learn
• Hands-on projects for real-world applications
• Focus on automation, pipelines, and deep learning integration

Book Description

Feature engineering is essential for building robust predictive models. This book delves into practical techniques for transforming raw data into powerful features using Scikit-Learn. You'll explore automation, deep learning integrations, and advanced topics like feature selection and model evaluation. Learn to handle real-world data challenges, enhance accuracy, and streamline your workflows. Through hands-on projects, readers will gain practical experience with techniques such as clustering, pipelines, and feature selection, applied to domains like retail and healthcare. Step-by-step instructions ensure a comprehensive learning journey, from foundational concepts to advanced automation and hybrid modeling approaches. By combining theory with real-world applications, the book equips data professionals with the tools to unlock the full potential of machine learning models. Whether working with structured datasets or integrating deep learning features, this guide provides actionable insights to tackle any data transformation challenge effectively.

What you will learn

• Create data-driven features for better ML models
• Apply Scikit-Learn pipelines for automation
• Use clustering and feature selection effectively
• Handle imbalanced datasets with advanced techniques
• Leverage regularization for feature selection
• Utilize deep learning for feature extraction

Who this book is for

Data scientists, machine learning engineers, and analytics professionals looking to improve predictive model performance will find this book invaluable. Prior experience with Python and basic machine learning concepts is recommended. Familiarity with Scikit-Learn is helpful but not required.

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Table of contents

• Who we are
• Our Philosophy:
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• TABLE OF CONTENTS
• Introduction
• Part 1: Practical Applications and Case Studies
• Chapter 1: Real-World Data Analysis Projects
• 1.1 End-to-End Data Analysis: Healthcare Data
• 1.2 Case Study: Retail Data and Customer Segmentation
• 1.3 Practical Exercises for Chapter 1
• 1.4 What Could Go Wrong?
• Chapter 1 Summary
• Chapter 2: Feature Engineering for Predictive Models
• 2.1 Predicting Customer Churn: Healthcare Data
• 2.2 Feature Engineering for Classification and Regression Models
• 2.3 Practical Exercises for Chapter 2
• 2.4 What Could Go Wrong?
• Chapter 2 Summary
• Quiz Part 1: Practical Applications and Case Studies
• Answers
• Project 1: Customer Segmentation using Clustering Techniques
• 1. Understanding the K-means Clustering Algorithm
• 2. Advanced Clustering Techniques
• 3. Evaluating Clustering Results
• Part 2: Integration with Scikit-Learn for Model Building
• Chapter 3: Automating Feature Engineering with Pipelines
• 3.1 Pipelines in Scikit-learn: A Deep Dive
• 3.2 Automating Data Preprocessing with FeatureUnion
• 3.3 Practical Exercises for Chapter 3
• 3.4 What Could Go Wrong?
• Chapter 3 Summary
• Chapter 4: Feature Engineering for Model Improvement
• 4.1 Using Feature Importance to Guide Engineering
• 4.2 Recursive Feature Elimination (RFE) and Model Tuning
• 4.3 Practical Exercises for Chapter 4
• 4.4 What Could Go Wrong?
• Chapter 4 Summary
• Chapter 5: Advanced Model Evaluation Techniques
• 5.1 Cross-Validation Revisited: Stratified, Time-Series
• 5.2 Dealing with Imbalanced Data: SMOTE, Class Weighting
• 5.3 Practical Exercises for Chapter 5
• 5.4 What Could Go Wrong?
• Chapter 5 Summary
• Quiz Part 2: Integration with Scikit-Learn for Model Building
• Answers
• Part 3: Advanced Topics and Future Trends
• Project 2: Feature Engineering with Deep Learning Models
• 1.1 Leveraging Pretrained Models for Feature Extraction
• 1.2 Integrating Deep Learning Features with Traditional Machine Learning Models
• 1.3 Fine-Tuning Pretrained Models for Enhanced Feature Learning
• 1.4 End-to-End Feature Learning in Hybrid Architectures
• 1.5 Deployment Strategies for Hybrid Deep Learning Models
• Chapter 6: Introduction to Feature Selection with Lasso and Ridge
• 6.1 Regularization Techniques for Feature Selection
• 6.2 Hyperparameter Tuning for Feature Engineering
• 6.3 Practical Exercises: Chapter 6
• 6.4 What Could Go Wrong?
• Chapter 6 Summary
• Chapter 7: Feature Engineering for Deep Learning
• 7.1 Preparing Data for Neural Networks
• 7.2 Integrating Feature Engineering with TensorFlow/Keras
• 7.3 Practical Exercises: Chapter 7
• 7.4 What Could Go Wrong?
• Chapter 7 Summary
• Chapter 8: AutoML and Automated Feature Engineering
• 8.1 Exploring Automated Feature Engineering Tools
• 8.2 Introduction to Feature Tools and AutoML Libraries
• 8.3 Practical Exercises: Chapter 8
• 8.4 What Could Go Wrong?
• Chapter 8 Summary
• Quiz Part 3: Advanced Topics and Future Trends
• Conclusion
• Where to continue?
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