Predicting Customer Churn on AWS: An End-to-End ML and Analytics Project
The Idea Most companies sit on customer data they never put to work. This project turns a plain CSV file into a churn prediction system: it identifies which customers are likely to cancel, explains why, and gives business teams a dashboard to act on it. The goal was to cover the full data journey, from raw data to business decision, using managed AWS services and as little infrastructure as possible.
The Problem
Acquiring a new customer costs far more than keeping an existing one. If a retention team can spot at-risk customers early, it can target offers where they matter most instead of spreading budget across everyone. That requires two things: understanding the patterns behind churn (analytics) and anticipating it (machine learning).
The Approach
The project combines the roles of a data engineer, a data analyst, and an ML engineer in one pipeline:
Data engineering: ingest, catalog, and clean the data into an analysis-ready format
Data analysis: explore churn drivers with SQL, before any modeling
Machine learning: train a classification model to score each customer's risk
Business intelligence: deliver results through a dashboard non-technical teams can use
Architecture at a Glance
Data analysis: explore churn drivers with SQL, before any modeling
Machine learning: train a classification model to score each customer's risk
Business intelligence: deliver results through a dashboard non-technical teams can use
Architecture at a Glance
1
2
3
4
5
CSV → S3 → Glue (catalog + ETL) → S3 (Parquet) → Athena
↓
SageMaker
↓
Predictions → QuickSightWhat Made It Work
- Parquet over CSV: smaller storage and much faster Athena queries
- SQL before ML: simple queries already revealed key insights, such as month-to-month contracts and new customers churning the most
- Recall over precision: for churn, missing a departing customer costs more than contacting a loyal one
- Batch Transform over a permanent endpoint: cheaper when predictions are needed periodically rather than in real time
Enjoyed reading this content? Let the author know!
Your likes, comments, shares, and saves help creators reach more builders.
Loading recommendations
Loading article