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GitHub: https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/30-regression-demand-forecasting.md 🚀 In this hands-on AWS machine learning tutorial, you will learn how to build a complete demand-forecasting regression model inside Amazon SageMaker Studio—from setting up the domain to generating synthetic data, feature engineering, training a Linear Regression model, evaluating RMSE/MAE, and visualizing forecasts. This step-by-step walkthrough is perfect for beginners, students, AWS learners, and data science practitioners who want real cloud-based ML experience using SageMaker’s JupyterLab environment. 🔥 What You Will Learn in This Video ✔️ How to create and configure an Amazon SageMaker Domain ✔️ How to set up a JupyterLab Space for ML development ✔️ How to generate 1,000 synthetic demand records ✔️ Upload datasets to Amazon S3 ✔️ Perform feature engineering on time-series data ✔️ Train and interpret a Linear Regression model ✔️ Evaluate forecasting performance using RMSE & MAE ✔️ Visualize actual vs. predicted demand ✔️ (Optional) Train an XGBoost Regression model ✔️ Clean up SageMaker resources to avoid charges 📌 About This Tutorial This video follows a real-world machine learning workflow commonly used for: Retail demand forecasting Supply chain optimization Inventory planning Time-series regression modeling The dataset includes synthetic daily sales for a single product across 1,000 days. You’ll see how SageMaker integrates compute, storage, and notebooks into one seamless environment for production-ready ML. 🧰 Tools Used in This Video Amazon SageMaker Studio JupyterLab Amazon S3 Python (Pandas, NumPy, Matplotlib) Scikit-Learn XGBoost (optional) 👍 If You Found This Helpful… Please Like, Subscribe, and Share to support the channel and help others learn AWS + Machine Learning.
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