Predicts which telecom customers are likely to churn with 95% accuracy using real-world data features from usage, billing, and support data. Implements Sturges-based binning, one-hot encoding, stratified 80/20 train-test split, and a two-level ensemble pipeline with soft voting. Achieves 94.60% accuracy, 0.8968 AUC, 0.8675 precision, 0.7423 recall.
python customer-retention machine-learning reproducible-research naive-bayes scikit-learn telecom xgboost classification ensemble-learning logistic-regression decision-trees feature-engineering predictive-modeling churn-prediction model-evaluation real-world-data stratified-sampling joblib stacked-ensemble
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Updated
Jun 16, 2025 - Python