Feature Engineering for Modern Machine Learning with Scikit-Learn
Maximize machine learning potential with this guide to transforming raw data into powerful model inputs. Learn to boost accuracy and interpretability using Scikit-Learn, a leading machine learning library, through practical, hands-on approaches.
What You'll Get from This Book
8 chapters spanning over 430 pages
More than 330 explanatories blocks of code
More than 30 practical exercises
3 Quizzes to test your knowledge
2 Practical "Real World" Projects
Foundations of Feature Engineering
Feature engineering is the backbone of effective machine learning. This book begins by establishing a solid foundation, explaining why feature engineering is crucial for developing robust machine learning models. You will learn about different types of features—numerical, categorical, and time-based—and the specific techniques to process these data types effectively using Scikit-Learn.
The early chapters focus on preprocessing techniques such as normalization, scaling, and encoding, which are essential for making your data compatible with machine learning algorithms. You'll explore advanced strategies for handling missing values, reducing dimensionality, and selecting the most influential features that contribute to the predictive power of your models.
Practical case studies are included to demonstrate how these techniques are applied in real-world scenarios, such as finance, healthcare, and e-commerce. Each case study is carefully designed to reinforce the theoretical knowledge by applying it to datasets that mimic the complexities and challenges data scientists face today.
Advanced Feature Engineering Techniques
Delving deeper, "Feature Engineering for Modern Machine Learning with Scikit-Learn" explores complex techniques that can dramatically improve the performance of your machine learning models. This section covers interaction features that model complex relationships, polynomial features that capture non-linear effects, and feature selection techniques to identify and remove redundant variables.
You will also learn about automated feature engineering tools that can speed up the model-building process and reduce the risk of human error. The book provides a thorough examination of Scikit-Learn’s utilities for pipeline creation and feature combination, ensuring that you can build reproducible and scalable machine learning workflows.
Through detailed tutorials, you will gain hands-on experience with real datasets, applying these advanced techniques to build models that can forecast, classify, and make intelligent decisions based on large volumes of data.
Beyond technical skills, "Feature Engineering for Modern Machine Learning with Scikit-Learn" emphasizes the critical importance of developing a strategic mindset in feature engineering. This approach enables data scientists to not only create effective features but also to understand their broader impact on model performance and interpretability. The book delves into the art of balancing technical prowess with strategic thinking, teaching readers how to make informed decisions about feature selection and creation that align with overall project goals and business objectives.
The final chapters of the book offer an in-depth exploration of cutting-edge research and emerging trends in feature engineering. This includes a comprehensive look at the rapidly evolving field of automated feature engineering, where artificial intelligence and machine learning techniques are leveraged to discover and optimize features. The book examines various AutoML tools and frameworks, discussing their strengths, limitations, and potential applications in different scenarios. It also addresses the ethical considerations and potential biases that may arise from automated feature engineering processes, encouraging readers to approach these tools with a critical and responsible mindset.
Table of contents
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?
1.5 Chapter 1 Summary
Chapter 2: Feature Engineering for Predictive Modelscsv
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?
2.5 Chapter 2 Summary
Quiz Part 1: Practical Applications and Case Studies
Questions
Answers
Project 1: Customer Segmentation using Clustering Techniques
1. Understanding the K-means Clustering Algorithm
2. Advanced Clustering Techniques
3. Evaluating Clustering Results
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?
3.5 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?
4.5 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?
5.5 Chapter 5 Summary
Quiz Part 2: Integration with Scikit-Learn for Model Building
Questions
Answers
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?
6.5 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?
7.5 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?
8.5 Chapter 8 Summary
Quiz Part 3: Advanced Topics and Future Trends
Questions
Answers
What our readers are saying about this book
Explore the reviews to understand why this book is a great choice! Discover how others have gained from the knowledge and insights it provides. Get a taste of the exciting content that awaits you and see if this book is the perfect fit for your journey.
The book meticulously covers every aspect of feature engineering, from basic data preprocessing to advanced techniques that significantly enhance model performance. What sets this book apart is its practical approach, filled with real-world examples that demonstrate how to apply these techniques effectively using Scikit-Learn.
This book is a treasure trove of knowledge for data scientists looking to elevate their machine learning models beyond conventional methods. The explanations are clear, and the step-by-step tutorials make complex concepts accessible.
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