# 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)