Customer Churn Prediction and Analysis in R

Retaining existing customers is one of the most cost-effective ways to grow a business, making churn prediction a critical skill for modern data analysts. This text-based course guides you through the process of identifying at-risk customers using R, from raw data to actionable predictive insights. You will transition from understanding basic customer metrics to constructing robust machine learning models. By working through clear explanations and structured code examples, you will learn how to prepare real-world datasets, handle class imbalances, train predictive algorithms, and evaluate their performance to drive business decisions. What you'll learn: Understand core churn concepts, business metrics, and the foundational lifecycle of customer retention; Clean and preprocess messy customer datasets using modern R packages like the tidyverse; Handle imbalanced data effectively using up-sampling and down-sampling techniques; Build predictive models using classification algorithms such as logistic regression and decision trees; Evaluate model performance using modern tidymodels workflows, confusion matrices, and ROC curves; Translate technical model predictions into actionable business retention strategies. The course begins with foundational concepts of customer churn and data preparation, then guides you step-by-step through exploratory data analysis, model building, and evaluation using modern R programming practices. This course is designed for aspiring data analysts, business analysts, and beginners to R who want to apply machine learning to real-world business problems. No prior experience with machine learning is required, though a basic familiarity with R syntax is helpful. Start reading today to master churn prediction and add a highly valuable analytics skill to your portfolio.