Customer Email Engagement & Retention Tutorial

Notebooks

Click on a notebook to view the tutorial. Code cells are collapsed by default - click "Show/Hide Code" to expand.

• 00. Start Here
• 01. Data Discovery
• 01A A Temporal Text Deep Dive
• 01A Temporal Deep Dive
• 01B Temporal Quality
• 01C Temporal Patterns
• 01D Event Aggregation
• 02. Source Integrity
• 03. Dataset Merge
• 04. Column Deep Dive
• 04A Text Columns Deep Dive
• 05. Relationship Analysis
• 06. Feature Opportunities
• 07. Modeling Readiness
• 08. Baseline Experiments
• 09. Business Alignment
• 10. Spec Generation
• 11. Scoring Validation
• 12. View Documentation

About This Tutorial

This tutorial demonstrates the Customer Retention ML Framework using a sample Kaggle dataset. Each notebook walks through a stage of the ML pipeline, from data exploration to model deployment.

Features demonstrated:

• Automatic data profiling and type detection
• Data quality assessment and cleaning
• Feature engineering and selection
• Model training with MLflow tracking
• Business-aligned model deployment