Churn Risk Intelligence
Production-ready machine learning solution for predicting customer churn with 84.77% ROC-AUC. Features 16 optimized models, SHAP interpretability, Optuna hyperparameter tuning, and comprehensive business insights.
Business Impact
Problem Solved
Customer acquisition costs are 5-25x higher than retention costs. This solution identifies at-risk customers with 84.77% ROC-AUC score (80.62% accuracy), enabling proactive retention campaigns.
Value Delivered
Identifies high-risk customers for targeted retention campaigns, reduces revenue loss from customer attrition, and optimizes marketing budget allocation with precision targeting.
Key Results
84.77% ROC-AUC with XGBoost (Optuna-optimized), 80.62% accuracy with Logistic Regression. 16 models trained and evaluated with comprehensive business insights.
Model Performance
| Model | ROC-AUC | Accuracy | Precision | Recall | Optimization |
|---|---|---|---|---|---|
| XGBoost (Optuna) | 84.77% | 79.58% | 65.12% | 71.23% | Optuna |
| Stacking Classifier | 84.71% | 79.45% | 64.89% | 72.15% | Ensemble |
| Logistic Regression | 83.12% | 80.62% | 66.45% | 68.32% | Standard |
| Random Forest (Optuna) | 83.89% | 79.12% | 66.78% | 69.45% | Optuna |
| KNN + SMOTE | 81.23% | 75.34% | 58.92% | 77.27% | SMOTE |
| Gradient Boosting | 83.45% | 78.67% | 63.21% | 70.89% | Standard |
Model Portfolio: 16 machine learning models including Logistic Regression, KNN, Random Forest, Gradient Boosting, XGBoost, and ensemble methods (Stacking, Voting). Both regular and SMOTE variants trained with Optuna hyperparameter optimization.
Technical Architecture
Data Preprocessing
Missing value imputation, categorical encoding, feature scaling, and data leakage detection. Modular design with separation of concerns.
Feature Engineering
One-hot encoding, data leakage detection, and train-test splitting with stratification. Automated pipeline from data loading to model deployment.
Model Development
16 machine learning models: Logistic Regression, KNN, Random Forest, Gradient Boosting, XGBoost. Both regular and SMOTE variants.
Hyperparameter Optimization
Optuna integration for Bayesian optimization. Automated tuning for XGBoost and Random Forest with cross-validation for reliable performance estimation.
Ensemble Learning
Stacking Classifier with optimized base models and Voting Classifier for robust predictions. Meta-learner optimization for improved performance.
Model Interpretability
SHAP values for feature importance analysis and individual prediction explanations. Business-friendly insights generation.
Comprehensive Evaluation
Multiple metrics: ROC-AUC, PR-AUC, Accuracy, Precision, Recall, F1-Score. Advanced visualizations: ROC curves, Precision-Recall curves, heatmaps, interactive Plotly dashboards.
Advanced Features
Optuna Optimization
Bayesian hyperparameter optimization for XGBoost and Random Forest. Automated tuning with cross-validation for reliable performance estimation.
Ensemble Methods
Stacking Classifier with optimized base models achieving 84.71% ROC-AUC. Voting Classifier for robust predictions across multiple scenarios.
SHAP Interpretability
SHAP values provide feature importance analysis and individual prediction explanations. Generate actionable, business-friendly insights.
Interactive Dashboards
Plotly dashboards with ROC curves, Precision-Recall curves, heatmaps, and comprehensive business intelligence reports.
Class Imbalance Handling
SMOTE implementation for balanced training. Models trained on both balanced and imbalanced data for comprehensive comparison.
Production-Ready
Automated pipeline with comprehensive error handling, logging, and validation. End-to-end automation from data loading to model deployment.
Key Code Components
Main Pipeline Orchestrator
Model Training Pipeline
Dataset Information
Source
Telco Customer Churn Dataset
Size
7,043 customers × 21 features
Target
Customer churn (Yes/No)
Class Distribution
26.5% churn rate (imbalanced dataset)
Feature Categories
Demographics
Gender, age range, partner/dependent status
Account Information
Contract type, payment method, tenure, billing preferences
Services
Phone, internet, security, backup, streaming services
Financial
Monthly charges, total charges
Business Recommendations
For Maximum Churn Detection
- Use KNN + SMOTE model for maximum churn detection (77.27% recall)
- Implement comprehensive retention campaigns for all flagged customers
- Focus on customers with fiber optic internet and month-to-month contracts
- Best for high-value customer businesses where missing churners is costly
For Optimal Performance
- Use XGBoost (Optuna-optimized) for best overall performance (84.77% ROC-AUC)
- Leverage SHAP values for understanding key churn drivers
- Use Stacking Classifier (84.71% ROC-AUC) for robust ensemble predictions
- Ideal for balanced business needs requiring both precision and recall
For Cost-Conscious Operations
- Use Logistic Regression model for efficient targeting (80.62% accuracy, 66.45% precision)
- Use Random Forest (Optuna) for highest precision (66.78%)
- Prioritize customers with electronic check payments and high monthly charges
- Develop automated retention workflows for scalability
Key Risk Factors to Monitor
Contract Type
Month-to-month contracts show highest churn rates
Internet Service
Fiber optic users demonstrate elevated churn risk
Payment Method
Electronic check payments correlate with increased churn
Tenure
New customers (< 6 months) require attention