A machine learning project predicting customer churn using classification techniques: logistic regression and ensemble methods reaching 85%+ accuracy, with XGBoost and feature engineering driving a ~12% improvement over the baseline. A full analytical report with visualizations supports data-driven retention decisions.
Every customer passes through the ensemble model once. Most flow through to "Retained," and a smaller share gets flagged as "Churned."
XGBoost classifier combined with logistic regression, boosting prediction performance ~12% above the baseline model.
ROC-AUC scoring, confusion matrices, and precision/recall analysis to validate and compare model performance.
Detailed report with key statistics, visualizations, and insights supporting data-driven retention strategies.
Tenure, contract type, monthly charges, and support call volume were the strongest predictors of churn identified in feature importance analysis.