Customer Churn Prediction
End-to-end ML pipeline for predicting customer churn
Client
Internal Project
Industry
Technology
Services
Tech Stack
Challenge
Businesses lose valuable customers to churn, but identifying at-risk customers before they leave is difficult. We needed to build a complete machine learning pipeline that could process customer data, train predictive models, and deploy them for real-time predictions—all with proper MLOps practices.
Our Contribution
Our team built the complete ML infrastructure from data processing to model deployment. We created automated pipelines for data ingestion, feature engineering, model training, and deployment, with comprehensive monitoring and retraining capabilities.
Scope of Work
- Built ETL pipelines with Python and Pandas
- Implemented data validation and cleaning
- Created feature engineering workflows
- Set up S3 for data lake architecture
Solution
We delivered a production-ready ML system that continuously trains and deploys churn prediction models. The pipeline automatically ingests new customer data, retrains models, and deploys improvements without manual intervention. The system achieves 85% accuracy in identifying at-risk customers.
Impact
The churn prediction system enables proactive customer retention, identifying at-risk customers weeks before they would typically churn.