Feature Engineering for Customer Segmentation

Raw customer data is rarely ready for machine learning. To uncover meaningful customer segments, you must first transform raw attributes into powerful features that expose hidden patterns. This text-based course guides you through the essential process of feature engineering specifically tailored for customer segmentation. You will learn how to transition from messy transactional logs to structured datasets ready for clustering. What you will learn: Understand foundational feature engineering concepts and the data preparation lifecycle; Apply advanced encoding techniques for categorical customer attributes; Scale and normalize numerical data to ensure fair model performance; Analyze feature correlations and handle collinearity to avoid redundant data; Implement modern aggregation methods to summarize transactional history; Use modern Python libraries to build clean, reproducible preprocessing pipelines. Starting with basic terminology and key data concepts, this course guides you through structured text explanations and code snippets. You will read through practical scenarios that demonstrate how to shape and refine data for clustering algorithms. This course is designed for beginning data analysts and aspiring data scientists with a basic understanding of Python. Start reading today to unlock the true potential of your customer data.