From Data Exploration to Production Deployment: A Complete Practical Project Analysis of Telecom Customer Churn Prediction

This article analyzes the Churn-Prediction-ML project maintained by Gabriel Furtado on GitHub, which implements the complete ML engineering practice of telecom customer churn prediction from data exploration and modeling to production deployment. Key content includes: using IBM dataset for feature engineering and EDA, comparing models like Logistic Regression and PyTorch MLP, selecting the highly interpretable Logistic Regression to deploy as a FastAPI service, and demonstrating modern ML technology stack and engineering practices. The project aims to solve the customer churn problem in the telecom industry and help enterprises formulate precise retention strategies.

In the telecom industry, customer churn directly affects revenue and operational costs; the cost of acquiring new customers is 5-7 times that of retaining existing ones. This project addresses this business problem by building an end-to-end machine learning solution, which not only implements a churn prediction model but also demonstrates the complete ML engineering practice from data exploration to production deployment.

The project uses the IBM Telecom Customer Churn Sample Dataset, which contains 7043 records and 19 features covering demographics, contract terms, and service usage. Key features include tenure (length of service), Contract (contract type), MonthlyCharges/TotalCharges (consumption amount), InternetService (internet service), TechSupport (technical support), and PaymentMethod (payment method). The dataset has class imbalance (churn rate ~26.5%), which is addressed via stratified cross-validation and threshold tuning.

EDA Section: Conducted feature distribution visualization, correlation analysis, and missing value handling (e.g., business-aware imputation for the TotalCharges field); selected ROC-AUC, PR-AUC, and F1-score as evaluation metrics (due to class imbalance).

Model Comparison:

1. DummyClassifier (random baseline: ROC-AUC0.50, F10.28);
2. Logistic Regression (after tuning: ROC-AUC0.84, F10.61, PR-AUC~0.70);
3. PyTorch MLP (with training loop and early stopping strategy: ROC-AUC0.83, F10.60, PR-AUC~0.69).

Model Selection: Although MLP has strong theoretical expressive power, Logistic Regression was chosen for deployment for the following reasons: high interpretability (coefficients reflect feature impact), fast inference speed, low operation and maintenance cost (no GPU required), and performance comparable to MLP.

Deployment: Built a RESTful service using FastAPI, with endpoints including GET /health (returns status ok) and POST /predict (receives customer features and returns prediction results and probabilities). The API design considers production requirements: Pydantic data validation, loguru structured logging, pytest automated testing, ruff code quality checks, and it has been deployed to the Render platform.

The project uses a modern ML technology stack: data processing (pandas, numpy, matplotlib, seaborn), modeling (scikit-learn, PyTorch), experiment tracking (MLflow), API services (FastAPI, Pydantic, Uvicorn), testing (pytest, pandera), code quality (ruff, loguru), packaging and deployment (pyproject.toml, joblib). The project structure follows modular design, separating data loading, feature engineering, model training, prediction logic, and API services to ensure maintainability and testability.

Future Plans: Integrate Git Actions to implement CI/CD pipeline, add data drift monitoring, explore MLP as an optional prediction endpoint, and introduce SHAP values to enhance interpretability.

Practical Insights: 1. End-to-end thinking (full-process design); 2. Importance of comparative experiments (verify the rationality of simple models); 3. Engineering priority (prioritize maintainability under the premise of meeting requirements); 4. Documentation practice (retain experiment records); 5. Modular architecture (clear structure supports long-term maintenance). This project provides a reference example for building enterprise-level ML systems.