iterative/demo-bank-customer-churn
Demo DVC project training a classification model on tabular data
Demonstrates DVC's end-to-end ML workflow using `dvc.yaml` pipeline definition and remote artifact management, with binary churn prediction optimized via F1-score across 10,000 customer records featuring demographic and financial attributes. Integrates with Kaggle datasets and supports remote storage backends (S3, local) for reproducible model training and versioning. Includes both interactive Jupyter notebook and CLI-based pipeline execution for flexible experimentation and production deployment.
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Language
Jupyter Notebook
License
MIT
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Last pushed
May 11, 2024
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