RFM Customer Segmentation using K-Means Clustering on Snowpark - Adapted from this article

This project leverages the power of Snowpark and Snowflake to perform RFM (Recency, Frequency, Monetary) analysis, a proven method for customer segmentation based on purchasing behavior.

RFM analysis is combined with K-Means clustering, a popular machine learning algorithm that groups customers into distinct segments based on their RFM scores, enabling more targeted marketing strategies. Snowflake is an excellent choice for this type of analysis due to its robust, scalable architecture and comprehensive support for machine learning (ML) and MLOps workflows.

Snowflake offers a range of MLOps features, including model versioning, deployment, monitoring, and automated retraining. With Snowpark for Python, you can seamlessly develop and deploy ML models within Snowflake, while its integrated MLOps capabilities ensure efficient model lifecycle management, from training to production.

This repository contains a notebook and dataset to help you easily implement and explore these techniques.