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ML model to predict customer churn with feature importance analysis
Develop a machine learning system that predicts customer churn probability, identifies key factors, and provides actionable insights for retention strategies.
Churn prediction
Feature importance
Risk scoring
Segment analysis
Retention recommendations
Model monitoring
Python ML pipeline with XGBoost, SHAP for explainability, Streamlit dashboard, PostgreSQL for customer data
Machine learning
Feature engineering
Model evaluation
Data analysis
Business metrics
Build classification models
Perform feature engineering
Interpret ML models
Handle imbalanced data
Deploy ML solutions
EDA and data preparation
Create predictive features
Train and evaluate models
Add SHAP analysis
Build prediction interface
Deploy and monitor