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In this hands-on tutorial, you’ll learn how to build a customer segmentation model using KMeans clustering in Amazon SageMaker Studio. This video walks step-by-step through setting up a SageMaker Domain and JupyterLab space, generating synthetic customer data, storing it in Amazon S3, and applying KMeans clustering to identify meaningful customer segments. You’ll also visualize clusters and interpret them from a real-world business perspective. This lab is ideal for: • AWS Machine Learning beginners • Data science and analytics learners • AWS certification candidates • Anyone exploring customer segmentation use cases 🔍 What You’ll Learn ✔️ Create and configure an Amazon SageMaker Domain ✔️ Launch JupyterLab in SageMaker Studio ✔️ Generate and upload synthetic data to Amazon S3 ✔️ Scale features using StandardScaler ✔️ Apply KMeans clustering ✔️ Use the Elbow Method to select optimal clusters ✔️ Visualize customer segments ✔️ Interpret cluster behavior for business decisions 🧠 Use Case Customer segmentation using Age, Annual Income, and Spending Score to identify high-value, low-value, and premium customer groups. ⚠️ Cost Notice Amazon SageMaker resources are not free. Be sure to delete the SageMaker Domain, spaces, and S3 files after completing the lab to avoid charges. 📌 Subscribe for more AWS, Machine Learning, and Data Engineering labs 📘 Follow KnoDAX for hands-on cloud and AI learning
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