Supervised Learning Challenge: Predicting Customer Churn

How do businesses know when a customer is about to leave? Predictive modeling with supervised learning allows you to identify churn before it happens, saving revenue and improving customer retention. In this practical, text-based course, you will step into the role of a data analyst to solve a realistic customer churn problem. You will build, fine-tune, and evaluate classification models, transforming raw data into actionable business predictions. What you'll learn: - Understand fundamental supervised learning concepts and classification terminology. - Prepare and preprocess customer data using modern pipelines to handle missing values and categorical features. - Implement key classification algorithms including logistic regression, decision trees, and ensemble methods. - Address class imbalance using modern evaluation metrics like F1-score and ROC-AUC. - Optimize model performance through hyperparameter tuning and cross-validation techniques. - Analyze model predictions to extract meaningful business insights. The course begins with foundational definitions of supervised learning and classification metrics before guiding you through data preparation. You will then progress through structured coding challenges, writing and analyzing Python code to build robust ensemble models. This course is designed for aspiring data scientists and beginners who want hands-on practice with classification algorithms. No advanced machine learning experience is required, though a basic understanding of Python is helpful. Start reading today to sharpen your machine learning skills with a practical business challenge.