🤖 ML Project
Customer Churn Prediction ML Pipeline
Scikit-learn pipeline predicting member churn with 89% accuracy.
Project Overview
Internal Project
Machine Learning / Healthcare
2 months (2024)
ML Engineer
Developed a machine learning pipeline to predict member churn using historical activity data, enabling proactive retention strategies.
The Challenge
The healthcare platform was losing members without early warning:
No visibility into churn risk factors
Reactive approach to member retention
Manual analysis of member behavior
Inconsistent intervention strategies
High false-positive rates in existing rules
They needed a data-driven approach to predict and prevent churn.
My Solution
I built an end-to-end ML pipeline:
Feature Engineering
Extracted 50+ features from activity logs, plan usage, and engagement data.
Model Training
Tested multiple algorithms (Random Forest, XGBoost, Logistic Regression).
API Deployment
FastAPI endpoint for real-time predictions.
Monitoring
Model performance tracking and drift detection.
Key Features
89% Accuracy
High-precision predictions with low false positives.
Real-Time
Sub-100ms prediction latency.
Explainable
SHAP values for feature importance.
Auto-Retrain
Scheduled model updates with new data.
Tech Stack
ML Framework
API
Data
MLOps
Screenshots
Model Performance
Feature Importance
Prediction Dashboard
Results & Impact
89%
Accuracy
Achieved
50+
Features
Engineered
<100ms
Latency
Per Prediction
30%
Churn
Reduction
Key Achievements
Achieved 89% prediction accuracy
Reduced churn by 30% through early intervention
Sub-100ms prediction latency
Automated weekly model retraining
Interested in Something Similar?
I help businesses build robust backend systems, membership platforms, and automation tools.
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