README.md

Customer Churn Prediction — Project Documentation

📄 Full Document: 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 — 7,043 customers, 52 features
• Bank Customer Churn — 12 features

Key Papers

• Stacking Ensemble (99.28% acc): arXiv:2408.16284
• XGBoost Temporal (1st/575 teams): arXiv:1802.03396
• Transformer Time-Series (AUC=0.858): arXiv:2309.14390