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GitHub: https://github.com/knodax-labs-demo/aws-data-and-ml-labs/blob/main/29-classification-fraud-detection.md In this video, we walk step-by-step through building a complete fraud-detection classification model using Amazon SageMaker Studio. Whether you're preparing for an AWS certification, learning machine learning, or exploring SageMaker for the first time, this guide will help you understand every step of the workflow. ✅ What You Will Learn in This Lab How to set up an Amazon SageMaker Domain How to create and launch a JupyterLab Space How to generate a synthetic credit-card fraud dataset How to upload datasets to Amazon S3 How to preprocess and engineer ML features How to train a Random Forest classification model How to evaluate accuracy, precision, recall, and build a confusion matrix How to inspect feature importance How to safely delete SageMaker resources to avoid charges ⚠️ Important Note About AWS Costs Amazon SageMaker is not free, and creating a Domain, launching a JupyterLab Space, or storing data in S3 may incur charges. If you want to avoid any costs, feel free to simply watch the tutorial without running the steps in your AWS account. 🧰 Technologies Used Amazon SageMaker Studio Amazon S3 JupyterLab Python scikit-learn Pandas 📌 Ideal For: ML engineers and data scientists Students preparing for AWS ML/AI certifications Developers learning machine learning on AWS Anyone interested in fraud detection modeling 👉 If you found this video helpful, please Like, Subscribe, and share it with others learning AWS or Machine Learning. Enjoy the lab and happy learning! 🚀
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