How This Workflow Works
This workflow connects to a Snowflake database to retrieve and combine customer data, prepares it for machine learning, and trains a Random Forest model to predict churn. The trained model is then applied directly within Snowflake to score large datasets efficiently. Finally, the workflow evaluates the model and visualizes churn risk so that analysts and business users can easily identify customers who may require retention actions.
Key Features:
- Load and preprocess large-scale customer data directly in Snowflake
- Train and test a Random Forest model for churn prediction
- Generate churn predictions on new customer directly inside the Snowflake database
- Visualize churn risk and model performance to support business decision-making
Step-by-step:
1. Load and Combine Customer Data in Snowflake:
The workflow connects to Snowflake, loads and joins customer data from multiple tables using shared identifiers. Because the data remains in the cloud database, large datasets can be processed efficiently without complex data movement.
2. Prepare Data for Machine Learning:
The dataset is cleaned and structured for modeling. Relevant features are selected, and the data is split into training and test sets to ensure reliable model evaluation.
3. Train and Score the Churn Predictor:
A Random Forest model is trained using historical customer data to identify patterns associated with churn. The trained model is evaluated using common scoring metrics, and saved so it can be reused for generating predictions on new customer data.
4. Apply Predictions at Scale in Snowflake:
The saved model is deployed to predict churn on new customer records directly within Snowflake. This allows predictions to run efficiently on very large datasets while keeping data centralized and accessible for business teams. The workflow presents the results with visualizations, helping analysts and business users quickly identify high-risk customers.