Data AI Customer Churn reference implementation

This project is part of the IBM Data and AI reference architecture and defines an interesting use case as solution implementation.

The goals of this implementation is to illustrate how to build an intelligent application using:

• Data Ingestion and Organization
• Model Development and Deployment
• Batch Scoring
• Model Monitoring
• Cognitive service like Tone Analyzer and Chatbot
• Integrate with Customer Data base and unstructured data to build a customer churn predictive scoring model
• Deploy and monitor the model

For better reading experience go to the book view.

Building this booklet locally

The content of this repository is written with markdown files, packaged with MkDocs and can be built into a book-readable format by MkDocs build processes.

1. Install MkDocs locally following the official documentation instructions.
2. Install Material plugin for mkdocs: pip install mkdocs-material
3. git clone https://github.com/ibm-cloud-architecture/refarch-ai-data-customer-churn.git (or your forked repository if you plan to edit)
4. cd refarch-ai-data-customer-churn
5. mkdocs serve
6. Go to http://127.0.0.1:8000/ in your browser.

Pushing the book to GitHub Pages

1. Ensure that all your local changes to the master branch have been committed and pushed to the remote repository.
   1. git push origin master
2. Ensure that you have the latest commits to the gh-pages branch, so you can get others' updates.

   git checkout gh-pages
   git pull origin gh-pages
   
   git checkout master

3. Run mkdocs gh-deploy from the root refarch-ai-data-customer-churn directory.