Clustering Models and RFM Segmentation with H2O

Businesses collect vast amounts of customer data, but turning that data into clear, actionable segments is key to targeted marketing. Clustering algorithms help discover hidden patterns in customer behavior without manual labeling. In this text-based course, you will learn how to prepare transaction data, run unsupervised clustering models using the H2O framework, and translate mathematical clusters into practical business profiles. What you'll learn: 1. Understand the foundational concepts of unsupervised learning and KMeans clustering. 2. Prepare and transform raw transaction data into RFM (Recency, Frequency, Monetary) metrics. 3. Configure and train the H2OKMeansEstimator to group similar customer profiles. 4. Evaluate clustering performance using key metrics like within-cluster sum of squares. 5. Analyze and interpret resulting clusters to define distinct customer personas. 6. Apply modern data prep workflows using clean coding practices for machine learning pipelines. You will start with the basic theory of clustering and RFM analysis before diving into step-by-step code implementations using H2O. Through written explanations and structured code walk-throughs, you will progress from raw data to fully interpreted customer segments. This course is designed for beginner data analysts and aspiring data scientists who want to learn clustering. No advanced machine learning background is required, though basic Python familiarity is helpful. Start reading today to unlock customer insights using H2O clustering.