Customer Churn Prediction with Logistic Regression

Retaining existing customers is one of the most critical challenges for modern subscription and telecom businesses. Understanding who is likely to leave, and why, allows organizations to take proactive action before it is too late. This text-only course guides you through the process of building a predictive model to solve this exact business problem using Python. You will learn how to analyze customer behavior data, prepare it for machine learning, and implement a logistic regression model to forecast customer churn. By working through clear, written explanations and code snippets, you will gain the skills needed to transform raw customer metrics into actionable business intelligence. What you'll learn: - Understand the fundamental concepts of customer churn and binary classification. - Clean and preprocess raw data using modern Python data libraries. - Address class imbalance using current best practices to ensure reliable predictions. - Train and fit a logistic regression model to customer datasets. - Evaluate model performance using key metrics like precision, recall, and ROC-AUC. - Interpret model coefficients to identify the primary drivers of customer attrition. The course begins with foundational definitions of churn metrics and logistic regression theory before moving into hands-on data preprocessing, model training, and evaluation. This course is designed for aspiring data analysts, business analysts, and beginner data scientists who want to apply predictive analytics to real-world business scenarios. No prior machine learning experience is required, though a basic familiarity with Python is helpful. Start reading today to master the fundamentals of predictive churn modeling.