Applied Machine Learning

Using Machine Learning to Solve Business Problems
1. Auflage 2026
ISBN: 978-1-4932-2758-7
Verlag: Rheinwerk Verlag GmbH

Buch, Englisch, 440 Seiten, Format (B × H): 179 mm x 254 mm, Gewicht: 782 g

Put machine learning theory into practice with this hands-on guide! Learn about the real-world application of machine learning models by following three use cases, each with its own dataset. Get started with tools like GitHub and Anaconda, and then follow detailed instructions to prepare your data, select your model, evaluate its results, and measure its impact over time. With sample code for download, this book has everything you need to implement machine learning models for your business!

In this book, you’ll learn about:

a.Data Preparation

The first step is to understand your data. Learn about the different data sources, and then explore your data through visualization, descriptive statistics, and correlation analysis. Clean up your data by identifying errors, writing dummy code, and more.

b.Model Selection

Choose the machine learning model that suits your needs! Follow a model decision framework and master key algorithms: regression, decision trees, random forest, gradient boosting, clustering, and ensembling.

c.Evaluation and Iteration

Assess and improve the quality of your model! Apply a variety of validation metrics to your model and enhance interpretability to avoid black box code. Then iterate through feature engineering and adding or removing data.

d.Implementation and Monitoring

Your model is ready to go—now see it in action! Learn how to implement the model to make predictions, monitor its performance, and measure its impact for your business.

Highlights include:

1)Real-world use cases

2)Data exploration

3)Data cleaning

4)Model decision framework

5)Regression algorithms

6)Decision trees

7)Clustering

8)Validation metrics

9)Model iteration

10) Interpretability

11)Implementation

12)Monitoring

Autoren/Hrsg.

Hodson, Jason

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

... Preface ... 13

... Who Is This Book For? ... 13

... The Structure of This Book ... 16

... Conclusion ... 18

1 ... Introduction ... 19

1.1 ... Aligning on Nomenclature ... 19

1.2 ... Learning to Google (or Prompt) ... 21

1.3 ... Predictions for Generative AI’s Impact on Machine Learning ... 26

1.4 ... Summary ... 26

2 ... Getting Started ... 27

2.1 ... GitHub ... 27

2.2 ... Anaconda ... 30

2.3 ... Summary ... 38

3 ... Introduction to Our Use Cases ... 39

3.1 ... Importance of Understanding the Business Problem ... 39

3.2 ... Use Case 1: The Retail Tyrant ... 41

3.3 ... Use Case 2: Customer Retention ... 47

3.4 ... Use Case 3: Crime Predictions ... 50

3.5 ... Summary ... 53

4 ... Starting with the Data ... 55

4.1 ... Types of Data Sources ... 55

4.2 ... Data Exploration ... 66

4.3 ... Data Cleaning (For Now) ... 120

4.4 ... Summary ... 178

5 ... Picking Your Model ... 181

5.1 ... The Simpler the Model, the Better ... 181

5.2 ... Model Decision Framework ... 183

5.3 ... Train-Test Split ... 187

5.4 ... Regression Models ... 189

5.5 ... Machine Learning Models ... 221

5.6 ... Clustering ... 291

5.7 ... Summary ... 297

6 ... Evaluating the Model and Iterating ... 299

6.1 ... Importance of Picking Validation Metrics ... 299

6.2 ... Validation Metrics ... 301

6.3 ... K-Fold Cross-Validation ... 311

6.4 ... Business Validations ... 311

6.5 ... Machine Learning Interpretability ... 314

6.6 ... Iterating on the Model ... 321

6.7 ... Application to Use Cases ... 328

6.8 ... Summary ... 374

7 ... Implementing, Monitoring, and Measuring the Model ... 375

7.1 ... Implementing Your Model for Predictions ... 375

7.2 ... Model Monitoring ... 394

7.3 ... Measuring the Impact of Your Model ... 401

7.4 ... Summary ... 426

8 ... Closing Thoughts ... 427

8.1 ... Learning How to Learn with Generative AI ... 427

8.2 ... Learning How to Learn with Use Cases ... 428

8.3 ... Explore and Visualize Your Data ... 428

8.4 ... Cleaning Your Data and Dummy Coding ... 429

8.5 ... Machine Learning Models ... 430

8.6 ... Hyperparameters and Grid Search ... 430

8.7 ... Variable Lagging ... 431

8.8 ... The End ... 431

8.9 ... Acknowledgments ... 431

... The Author ... 433

... Index ... 435

Über Autor(innen)

Hodson, Jason
Jason Hodson has worked in data-centric roles for nearly a decade. He currently works as an HR analytics manager, and he has prior experience in a forecasting role using the full range of applied machine learning. In a previous role, Jason wrote the end-to-end code for an enterprise hiring manager and candidate experience process, collaborating with recruiting leaders to understand and leverage data from a company-wide survey. He’s built large data models and dashboards and taught nontechnical users how to adopt and use them. Jason has been a technical mentor in all his roles, helping others develop their analytics and programming skill set. The common thread across Jason’s career is his ability to be a translator for stakeholders, peers, and junior team members. His learning journey also gives him a unique perspective: Before earning a master’s degree in business analytics, he was entirely self-taught. This has made his approach to teaching more practical, allowing concepts to translate better (and faster) into the business world.