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Machine Learning
Predictive Customer Churn
A predictive modeling pipeline for identifying customers at risk of churning.
Problem
Retaining a customer is cheaper than acquiring a new one, but interventions only work if the customers most likely to churn can be identified early.
Approach
Standard supervised-learning pipeline — from feature engineering on behavioral and transactional data, through model training and evaluation, to translating predictions into a business-usable risk signal.
Pipeline
- 01DataCustomer & usage history
- 02Feature EngineeringBehavioral signals
- 03ML ModelTrained classifier
- 04PredictionChurn probability
- 05Business InsightActionable risk segments
Technologies
- Python
- scikit-learn
- XGBoost
- SQL
- Feature Engineering
Notes
- Understand the data before modeling it — feature engineering treated as the core of the problem.
- Prefer interpretable systems when possible, so predictions translate into explainable business action.