Customer Churn Prediction

Explainable ML System with XGBoost, SHAP, and Actionable Retention Insights

XGBoost SHAP Explainability Feature Engineering A/B Testing
94.2%
Model Accuracy
0.89
AUC-ROC Score
23%
Churn Reduction
$2.4M
Revenue Saved

System Architecture

Data Pipeline

  • ETL from CRM, billing, support systems
  • Real-time feature computation
  • Data quality validation
  • Feature store integration

ML Engine

  • XGBoost gradient boosting
  • Hyperparameter optimization
  • Class imbalance handling
  • Model versioning & registry

Explainability

  • SHAP TreeExplainer
  • Local & global explanations
  • Feature interaction analysis
  • Counterfactual explanations

Interactive Demos

Explore churn prediction capabilities

Data Explorer

Explore customer datasets with interactive visualizations and statistics

Feature Engineering

Create and transform features with automated feature selection

Model Trainer

Train XGBoost models with hyperparameter tuning

SHAP Explainer

Interactive SHAP visualizations for model interpretability

Prediction API

Real-time churn scoring for individual customers

Cohort Analysis

Analyze churn patterns across customer segments

A/B Testing

Design and analyze retention experiments

Retention Strategies

AI-powered retention recommendations

Executive Dashboard

KPI tracking and business intelligence

Tools & Utilities

Development and deployment tools

Model Registry

Version control for ML models

Benchmarks

Performance metrics comparison

Data Validator

Data quality checks and profiling

Model Export

ONNX, PMML, Python export

Documentation

Learn and integrate

Theory & Background

ML concepts and methodology

API Reference

Complete API documentation

Tutorials

Step-by-step guides

Examples

Code samples and use cases

Quick Start

# Install dependencies
pip install xgboost shap pandas scikit-learn

# Load and prepare data
import pandas as pd
from churn_model import ChurnPredictor

# Initialize predictor
predictor = ChurnPredictor(model_path='models/xgboost_v2.json')

# Single customer prediction with explanation
customer_data = {
    'tenure': 24,
    'monthly_charges': 70.50,
    'total_charges': 1692.0,
    'contract_type': 'Month-to-month',
    'payment_method': 'Electronic check',
    'support_tickets': 3
}

result = predictor.predict_with_explanation(customer_data)
print(f"Churn Probability: {result['probability']:.2%}")
print(f"Risk Level: {result['risk_level']}")
print(f"Top Factors: {result['top_factors']}")
print(f"Recommended Actions: {result['recommendations']}")