Real-time Customer Churn Prediction System

Building end-to-end ML pipeline for real-time churn prediction using advanced time-series analysis.

PythonMLOpsTime SeriesEnsemble MethodsAWS

Project Overview

This project focuses on creating a comprehensive machine learning system that predicts customer churn in real-time. The system uses advanced time-series analysis, ensemble methods, and automated model retraining to provide accurate predictions that help businesses retain customers and optimize their retention strategies.

Key Features

Real-time churn prediction with low latency
Advanced time-series feature engineering
Ensemble methods combining multiple algorithms
Automated model retraining and deployment
A/B testing framework for model validation
Comprehensive monitoring and alerting system

Challenges

Handling imbalanced churn datasets
Ensuring real-time prediction performance
Managing model drift over time
Integrating with existing business systems
Maintaining model interpretability

Solutions

Implemented advanced sampling techniques for imbalanced data
Used feature store for efficient real-time serving
Created automated retraining pipeline with drift detection
Built API-first architecture for easy integration
Developed SHAP-based explainability features

View Source Code

Interested in the technical implementation? Check out the complete source code on GitHub.

View on GitHub