About

This project builds a production-ready Customer Churn Prediction system using Zerve AI.

The goal is to identify users who are likely to churn based on their activity and engagement patterns.
The solution includes data preprocessing, feature engineering, and machine learning models (Logistic Regression and Random Forest) evaluated using accuracy, precision, recall, and ROC-AUC.

The final model is deployed as a scheduled production workflow using Zerve’s deployment features, demonstrating a real-world, automated analytical system.

Business teams can use this system to proactively target high-risk users, reduce churn, and improve customer retention and revenue.