Project 01
Customer Churn Decision Intelligence System
Year: 2024
Graphical representation of the system architecture using ASCII art. Visual elements include data sources, ingestion pipelines, model training, and deployment workflows.
------------------------------------| 1. DATA SOURCES || Kaggle Telco Churn, IBM CRM || exports, application usage logs, || and support ticket transcripts || form the core dataset. These || sources capture behavioral and || transactional signals tied to || customer lifecycle stages. All || inputs undergo schema validation || and anomaly screening to ensure || consistency. |------------------------------------ | v------------------------------------| 2. INGESTION & PREPROCESSING || Batch loaders merge CRM data || with historical usage metrics, || while event streams provide live || churn-indicating signals. || Preprocessing handles null || through demographic-aware || imputation, reconstructs || interaction sequences, and || normalizes heterogeneous || attributes to ML-ready formats. |------------------------------------ | v------------------------------------| 3. FEATURE ENGINEERING || Tenure indicators, RFM profiles, || contract-based risk markers, || sentiment from support messages, || and rolling engagement windows || are engineered. SHAP analysis || informs feature selection by || measuring predictive || contribution. Features are || versioned and stored for || consistent training/inference || usage. |------------------------------------ | v------------------------------------| 4. MODEL TRAINING (XGBoost + || SHAP Integration) || Gradient-boosted trees are || trained using cross-validation || to capture nonlinear churn || behavior. SHAP provides || interpretable reasoning for || model predictions, spotlighting || the strongest churn drivers. || Regularization and hyperparameter|| tuning ensure stable || generalization across cohorts. |------------------------------------ | v------------------------------------| 5. DEPLOYMENT & INFERENCE || SERVICE || A FastAPI model server exposes || real-time prediction endpoints || with low-latency scoring || optimized through caching and || lightweight containers. The || service supports batch scoring || for CRM workflows and integrates || with retention dashboards used || by business teams. |------------------------------------ | v------------------------------------| 6. MONITORING & FEEDBACK LOOP || Data drift monitors track shifts || in customer profiles and || engagement. Retraining pipelines || trigger when model decay is || detected. Business teams feed || back validated churn cases, || allowing continuous learning and || improved targeting strategies || over time. |------------------------------------
Generate an architecture view to see a narrative of how Boxes 1 → 6 work together for this project.