AI-Driven Telecom Intelligence Platform
Customer Churn Predictor
Advanced machine learning system designed to estimate customer churn risk patterns using customer metadata, active service subscriptions, contract types, and billing history.
Platform Overview
This platform was developed to analyze customer profiles and identify patterns associated with churn behavior. The prediction pipeline combines statistical learning, feature preprocessing, and subscription activity indicators to estimate customer churn probability.
XGBoost Classifier
Gradient boosted decision trees optimized for high classification performance, class imbalance, and stable prediction behavior.
GridSearchCV Optimization
Exhaustive hyperparameter tuning performed to maximize model ROC AUC score and ensure robust generalization across demographic segments.
Telecom Feature Engineering
One-hot categorical encodings, standardized numerical billing features, product usage counts, and integration risk levels built into the pipeline.
Optimization Overview
This version utilizes XGBoost to achieve high classification power on structured customer data. Hyperparameters (learning rate, max depth, estimators) were tuned using GridSearchCV to maximize predictive reliability and precision-recall trade-offs.
System Capabilities
The model evaluates customer profiles using a combination of customer tenure, service subscriptions, charges, payment history, and contract parameters.
82% Overall Accuracy
& 75% Churn Recall
Optimized XGBoost model performance showcasing overall accuracy and recall for churn detection
XGB
Extreme Gradient Boosting used for predictions
ML
Machine learning pipeline with preprocessing integration
Analytical Disclaimer:
This platform is intended for analytical and research demonstration purposes. Predictions generated by the system should be used in combination with qualitative customer insights and business policy.