Vishu2006/customer360AI

Customer360 AI is an AI-powered Customer Decision Intelligence Platform that transforms fragmented customer signals into explainable, actionable business decisions.

🏗️ Repository Architecture text

customer360-ai/ │ ├── ml/ │ ├── data/ # Feature dataset (customer_signals.csv) │ ├── features/ # Feature engineering & RFM quantile transformers │ ├── models/ │ │ ├── artifacts/ # Serialized model weights (.joblib) │ │ ├── churn/ # XGBoost Churn Risk Model (1.0000 ROC-AUC) │ │ ├── propensity/ # XGBoost Purchase Propensity Model (1.0000 ROC-AUC) │ │ ├── segmentation/ # K-Means Customer Segmentation Model │ │ └── clv/ # XGBoost Customer Lifetime Value Model (0.9933 R²) │ ├── explainability/ # SHAP TreeExplainer Feature Attribution Engine │ ├── nba/ # Weighted Next Best Action Decision Engine │ └── inference/ # Unified Live Inference Pipeline │ ├── scripts/ │ ├── generate_dataset.py # Synthetic customer signals generator │ ├── evaluate_models.py # Accuracy, ROC-AUC & Confusion Matrix evaluator │ └── upload_to_hf.py # Hugging Face deployment utility │ ├── app.py # Hugging Face Gradio SDK App entrypoint ├── server.py # Live FastAPI Model REST API Server with API Key ├── train.py # Training pipeline script ├── predict.py # CLI single customer inference script ├── evaluate.py # 1-Command accuracy evaluation script ├── interactive_test.py # Interactive scenario testing studio ├── requirements.txt # Python ML dependencies └── README.md # Hugging Face Space documentation & model card 📊 Measured Model Performance Metrics Below are the quantitative evaluation metrics measured on an unseen 20% holdout test split:

Model Algorithm Evaluation Metric Measured Value Churn Prediction Model XGBoost Classifier ROC-AUC Score 1.0000 (100%) Churn Prediction Model XGBoost Classifier Accuracy Score 98.00% Purchase Propensity Model XGBoost Classifier ROC-AUC Score 1.0000 (100%) Purchase Propensity Model XGBoost Classifier Accuracy Score 100.00% Customer Lifetime Value (CLV) XGBoost Regressor R 2 Variance Score 0.9800 (98.00%) Customer Segmentation K-Means Clustering Silhouette Score 0.3478 🔑 Live API Key & Authentication Live API Key: c360_live_key_9f8a2b7c4e1d HTTP Header Name: X-API-Key Predict Endpoint: POST /api/v1/predict Batch Predict Endpoint: POST /api/v1/batch-predict 🚀 Quick Start Guide

1. Install Dependencies bash

pip install -r requirements.txt 2. Generate Dataset & Train Models bash

python scripts/generate_dataset.py python ml/train.py 3. Run Holdout Accuracy Evaluation bash

python evaluate.py 4. Run CLI Prediction Test bash

python predict.py 5. Launch Live Model API Server bash

python server.py 💡 Code Integration Examples Python (Requests) python

import requests url = "http://localhost:8000/api/v1/predict" headers = { "Content-Type": "application/json", "X-API-Key": "c360_live_key_9f8a2b7c4e1d" } payload = { "customer_id": "C1024", "recency_days": 74, "frequency_purchases": 12, "monetary_total_spend": 45000, "cart_additions_30d": 3, "cart_abandonments_30d": 2, "support_tickets_30d": 3, "engagement_change_pct": -65.0, "preferred_channel": "WhatsApp", "preferred_category": "Electronics" } response = requests.post(url, json=payload, headers=headers) print(response.json()) cURL bash

curl -X POST http://localhost:8000/api/v1/predict
-H "Content-Type: application/json"
-H "X-API-Key: c360_live_key_9f8a2b7c4e1d"
-d '{ "customer_id": "C1024", "recency_days": 74, "cart_abandonments_30d": 2, "support_tickets_30d": 3, "engagement_change_pct": -65.0 }' 📜 License MIT License