A production-ready machine learning solution to predict customer churn and help businesses retain valuable customers through data-driven insights.
- Overview
- Key Features
- Demo
- Installation
- Quick Start
- Usage Guide
- Project Structure
- How It Works
- Model Performance
- API Documentation
- Examples
- Contributing
- License
- Contact
Customer churn prediction is crucial for business success. This ML model helps identify customers likely to leave, enabling proactive retention strategies that:
- π° Reduce customer acquisition costs by 5-7x (retaining vs acquiring)
- π Increase revenue through targeted retention campaigns
- π― Improve customer lifetime value by identifying at-risk segments
- π Data-driven decisions backed by interpretable ML insights
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Multiple Algorithms: XGBoost, LightGBM, Random Forest, Gradient Boosting, Logistic Regression
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Production Ready: Easy deployment, batch predictions, REST API support
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Handles Imbalance: Built-in SMOTE implementation
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Interpretable: SHAP values, feature importance, risk scoring
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Well Documented: Comprehensive guides for data scientists and developers
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Extensible: Easy to customize and integrate with existing systems
- π Comprehensive EDA Notebook - Jupyter notebook with full exploratory analysis
- π¬ Multiple ML Algorithms - Compare and choose the best model
- ποΈ Hyperparameter Tuning - Automated GridSearchCV optimization
- π Evaluation Metrics - ROC-AUC, Precision-Recall, Confusion Matrix
- π Model Interpretability - SHAP values and feature importance
- βοΈ Imbalanced Data Handling - SMOTE oversampling
- π Clean Python Code - PEP 8 compliant, well-documented
- π¦ Modular Architecture - Easy to extend and maintain
- π API Ready - Flask integration examples
- πΎ Model Persistence - Save and load trained models
- π§ͺ Batch Processing - Handle thousands of predictions efficiently
- π Detailed Logging - Track training and prediction processes
- πΌ Risk Stratification - Categorize customers into Low/Medium/High risk
- π Actionable Insights - Understand what drives customer churn
- π― Targeted Campaigns - Focus retention efforts where they matter most
- π ROI Tracking - Measure effectiveness of retention strategies
- π Real-time Predictions - API for live customer scoring
- π Dashboard Ready - Export results for visualization tools
# Train XGBoost model with default settings
python main.py --mode train --model xgboost
# Train with hyperparameter tuning (slower but better)
python main.py --mode train --model lightgbm --tune