Customer Churn Prediction
End-to-end churn analytics solution on 7,032 telecom customer records. Combines EDA, ensemble ML modeling, and a Power BI dashboard to identify at-risk customers, quantify revenue impact, and surface actionable retention strategies.
Project Highlights
Ensemble stacking model: 87.2% accuracy, ROC-AUC 0.924, F1-score 0.87 on held-out test set.
Identified top churn drivers: contract type, customer tenure, monthly charges, and service bundle count.
Power BI dashboard surfaces KPIs, customer segments at risk, and estimated revenue loss in real time.
Actionable retention playbook delivered alongside model — reducing projected churn by targeting month-to-month and early-tenure segments.
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