🏦 Credit Limit Increase Eligibility Prediction 📘 Overview

This project demonstrates how to use Snowflake ML and Snowpark to build an end-to-end machine learning pipeline that predicts which customers are eligible for a credit limit increase. The solution covers the complete lifecycle — from data preparation to model training, evaluation, and deployment — all within the Snowflake ecosystem.

🎯 Objective

To develop a predictive model that enables financial institutions to make data-driven, risk-aware decisions about customer credit limit increases.

⚙️ Use Case Description

Financial institutions often review customer profiles to decide whether they qualify for a credit limit increase. This project uses Snowflake ML to analyze:

Customer demographics

Transaction patterns

Repayment behavior

Credit utilization trends

The trained model predicts eligibility, helping automate the credit review process while ensuring responsible lending.

🧠 Key Learning Outcomes

Participants will learn to:

🧩 Understand the ML workflow for credit decisioning using Snowpark ML

🧹 Perform data preprocessing within Snowflake

🏗️ Train and register models using the Snowflake Model Registry

🚀 Deploy models and interpret predictions using Snowpark ML and optionally SPCS

📈 Business Impact

⚡ Automation of credit review workflows

🛡️ Enhanced risk management through predictive insights

😊 Improved customer experience with faster and fairer decisions