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Customer Churn Prediction with R: Build Machine Learning Models to Identify and Retain At-Risk Customers
Paperback
Series: Real-World Data Science with R, Book 22
DatabasesGeneral MathematicsProbability & Statistics
Publisher: Independently Published
Published: Mar 10 2026
Pages: 154
Weight: 0.47
Height: 0.33 Width: 6.00 Depth: 9.00
Language: English
Customer Churn Prediction with R: Build Machine Learning Models to Identify and Retain At-Risk Customers
Understanding why customers leave is one of the biggest challenges businesses face today. Customer churn can quietly drain revenue, reduce growth, and weaken long-term business stability. Organizations that fail to predict and prevent churn often spend far more money acquiring new customers than retaining the ones they already have.
Customer Churn Prediction with R: Build Machine Learning Models to Identify and Retain At-Risk Customers provides a practical, step-by-step guide to using machine learning and data analytics to identify customers who are most likely to leave. Using the powerful capabilities of R programming, this book demonstrates how businesses can transform raw customer data into actionable insights that drive smarter retention strategies.
Designed for data analysts, business analysts, data scientists, and students, this book explains how to build real-world churn prediction models while maintaining a strong focus on practical business applications.
Inside this book, readers will learn how to:
- Understand the fundamentals of customer churn analytics
- Prepare and clean customer data for machine learning models
- Explore customer behavior using exploratory data analysis and visualization
- Build baseline churn prediction models such as logistic regression and decision trees
- Apply advanced machine learning algorithms including random forest, gradient boosting, and support vector machines
- Evaluate model performance using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC
- Handle imbalanced churn datasets effectively
- Segment customers and identify high-risk customer groups
- Design targeted customer retention strategies based on predictive insights
- Deploy churn prediction models into real business environments
This book also demonstrates how churn prediction can be integrated into business systems such as customer relationship management platforms and marketing automation tools, allowing companies to intervene before valuable customers leave.
Whether you are a beginner learning machine learning with R or a business professional seeking to implement predictive analytics for customer retention, this book provides the tools and strategies needed to turn data into actionable business intelligence.
By the end of this book, readers will understand how to build and deploy customer churn prediction systems that help businesses reduce churn, increase customer loyalty, and improve long-term profitability.
If you want to learn how to use R programming and machine learning to identify and retain at-risk customers, this book provides the practical roadmap you need.
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