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+# Customer Churn Prediction — Project Documentation
+
+📄 **Full Document:** [Customer_Churn_Prediction_Model_Document.md](./Customer_Churn_Prediction_Model_Document.md)
+
+## Overview
+Comprehensive project document for building a Customer Churn Prediction system using Adaptive Ensemble Machine Learning with Explainable AI.
+
+## Document Sections
+1. **Title** — Project framing and subtitle
+2. **Problem Statement** — Business context, technical challenges, and gaps
+3. **Idea of Solution** — Stacking ensemble architecture with 5 base models
+4. **Objectives** — Primary/secondary goals and success criteria
+5. **Literature Review & References** — 21 cited papers spanning 2016–2024
+6. **Dataset Understanding** — Audit of Telco (52 features) and Bank churn datasets
+7. **Proposed Methodology** — 7-phase pipeline from preprocessing to CLV scoring
+8. **Implementation Strategy** — Tech stack, 4-week timeline, code architecture
+9. **Experimental Design** — 5 experiments, 10 metrics, statistical rigor
+10. **Result Analysis** — Expected performance, SHAP analysis, business impact
+11. **Iterative Improvement** — 6 iterations from feature engineering to production
+
+## Key Datasets
+- [Telco Customer Churn](https://hf.co/datasets/aai510-group1/telco-customer-churn) — 7,043 customers, 52 features
+- [Bank Customer Churn](https://hf.co/datasets/tayaee/bank-customer-churn-prediction) — 12 features
+
+## Key Papers
+- Stacking Ensemble (99.28% acc): [arXiv:2408.16284](https://hf.co/papers/2408.16284)
+- XGBoost Temporal (1st/575 teams): [arXiv:1802.03396](https://hf.co/papers/1802.03396)
+- Transformer Time-Series (AUC=0.858): [arXiv:2309.14390](https://hf.co/papers/2309.14390)