Machine Learning For Customer Churn Prediction Telecom

Machine Learning For Customer Churn Prediction Telecom

Telecommunications · Subscriber Analytics

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: early churn warning and tenure preservation using network usage and billing friction signals.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Customer Churn Prediction Telecom. Within real-world operational environments, systems face severe obstacles including silent churn in prepaid accounts, promotion-induced switching, and multi-SIM usage masking intent. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.

The system ingests and processes records sourced from call detail records (CDR), data packet consumption decay, customer support IVR escalations, and contract renewal dates. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.

Modeling evaluates diverse algorithmic paradigms: XGBoost Classifier, CatBoost, Survival Analysis (Cox Proportional Hazards), Logistic Regression Baseline. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of ROC-AUC, Recall@Top-Decile, Lift Factor, Retained Subscriber MRR rather than uninformative global accuracy.

To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.

Tools & Technologies

The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:

Modular Machine Learning Workflow

Candidate Algorithms Benchmarked

• • XGBoost Classifier
• • CatBoost
• • Survival Analysis (Cox Proportional Hazards)
• • Logistic Regression Baseline

Final production selection is based on cross-validated Pareto efficiency balancing ROC-AUC, Recall@Top-Decile, Lift Factor, Retained Subscriber MRR against inference latency.