You're reading from Scikit-learn Cookbook Over 80 recipes for machine learning in Python with scikit-learn

Product type Paperback

Published in Dec 2025

Last Updated in Sep 2025

Publisher Packt

ISBN-13 9781836644453

Length 414 pages

Edition 3rd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

John Sukup

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Table of Contents (14) Chapters

1. Scikit-learn Cookbook, Third Edition: Over 80 recipes for machine learning in Python with scikit-learn

2. Chapter 1: Common Conventions and API Elements of scikit-learn FREE CHAPTER

3. Chapter 2: Pre-Model Workflow and Data Preprocessing

4. Chapter 3: Dimensionality Reduction Techniques

5. Chapter 4: Building Models with Distance Metrics and Nearest Neighbors

6. Chapter 5: Linear Models and Regularization

7. Chapter 6: Advanced Logistic Regression and Extensions

8. Chapter 7: Support Vector Machines and Kernel Methods

9. Chapter 8: Tree-Based Algorithms and Ensemble Methods

10. Chapter 9: Text Processing and Multiclass Classification

11. Chapter 10: Clustering Techniques

12. Chapter 11: Novelty and Outlier Detection

13. Chapter 12: Cross-Validation and Model Evaluation Techniques

14. Chapter 13: Deploying scikit-learn Models in Production

Practical Exercises on Data Preprocessing

In this chapter, we’ve covered several methods commonly applied to data preprocessing. Now it’s time to put it all together! Can you guess what tool might be helpful for this exercise? You got it: the Pipeline() class!

How to do it…

For these exercises, we will use a publicly available dataset, California Housing, which is included in the scikit-learn library. The dataset contains 20,640 records and 9 features where the target value (what we are trying to predict with our model) is the average home price per 100,000 homes.

You are tasked with building a comprehensive data pipeline composed of steps you learned in this chapter. In the Jupyter Notebook for Chapter 2, you will find an incomplete code block at the end called “Comprehensive Pipeline” where you should add your code to complete the following: