This project builds a fully automated, end-to-end data engineering and data science pipeline to predict customer churn using AWS Free Tier services.

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nd-to-End Customer Churn Prediction Pipeline using FastAPI, MLflow, Docker, and GitHub Actions (Deployed on AWS EC2)

End-to-end ML pipeline for customer churn prediction using XGBoost (AUC: 0.91), MLflow experiment tracking, and a versioned feature store on AWS S3.

End-to-End ML Pipeline with MLflow + FastAPI for churn prediction model, experiments tracked with MLflow, served via FastAPI, and deployed on AWS/GCP free tier.

End-to-end customer churn prediction MLOps project built on AWS. Trains a machine learning model using AWS Sagemaker to predict customer churn, deploys it as a FastAPI microservice on AWS ECS Fargate, containerized with Docker, and automates the pipeline using GitHub Actions CI/CD.

Churn Prediction Model: Built an end-to-end Customer Churn Prediction system using machine learning algorithms with a Django-based web interface, deployed on AWS EC2 with S3 for data storage; implemented full ML pipeline from data preprocessing to real-time prediction via API integration

End-to-end customer analytics platform with churn prediction — built on AWS S3, Apache Airflow, Databricks (Spark), and Streamlit. Includes synthetic event generation, ETL pipelines, star schema modeling, LightGBM ML, and SHAP explainability.

End-to-end Customer 360° data engineering pipeline built on AWS using S3, Glue (PySpark), and Step Functions. Implements Bronze–Silver–Gold architecture with Terraform-based infrastructure and modular ETL pipelines for analytics use cases like churn prediction, fraud detection, and customer behavior analysis.

Production-grade Customer Churn Prediction system built on AWS SageMaker with FastAPI, SHAP explainability, hyperparameter tuning, CI/CD, and Docker. From data validation to deployment — this isn’t just a model, it’s an end-to-end ML engineering pipeline designed for real-world scale.

This project implements a complete end-to-end MLOps pipeline for customer churn prediction on the AWS cloud. It automates the entire machine learning lifecycle, from data ingestion and model training to deployment and monitoring. The architecture is designed to be robust, scalable, and maintainable, leveraging a modern MLOps stack.