Telecom-Churn-Predictor and Customer-Churn-Prediction

Telecom-Churn-Predictor

Emerging

Customer-Churn-Prediction

Experimental

Maintenance 13/25

Adoption 1/25

Maturity 15/25

Community 12/25

Maintenance 13/25

Adoption 0/25

Maturity 15/25

Community 0/25

Stars: 1

Forks: 1

Downloads: —

Commits (30d): 0

Language: Python

License: MIT

Stars: —

Forks: —

Downloads: —

Commits (30d): 0

Language: Jupyter Notebook

License: MIT

No Package No Dependents

About Telecom-Churn-Predictor

gattsu001/Telecom-Churn-Predictor

Predicts which telecom customers are likely to churn with 95% accuracy using engineered 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.

About Customer-Churn-Prediction

JavedFazlulahF/Customer-Churn-Prediction

📊 Predict customer churn in telecom using machine learning to enhance retention strategies and drive better business outcomes.

Related comparisons

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