Customer Churn Prediction with Logistic Regression

Retaining customers is one of the most critical challenges for modern businesses, making churn prediction an essential skill for any data professional. This text-based course guides you through the foundational concepts of binary classification, data preparation, and logistic regression to build your first predictive model from scratch. What you'll learn: 1. Understand the fundamentals of customer churn and binary classification. 2. Clean and prepare customer data using modern Python libraries. 3. Address class imbalance to ensure fair and accurate predictions. 4. Train a logistic regression model and interpret its coefficients. 5. Evaluate performance using confusion matrices, precision, recall, and ROC-AUC metrics. 6. Apply your model to identify at-risk customers and support business decisions. You will start with core terminology and exploratory data analysis before moving step-by-step through data preprocessing, model training, and rigorous evaluation using written explanations and code snippets. Designed for beginners with basic Python knowledge, this course requires no advanced mathematical background. Start reading today to master the fundamentals of predictive classification.