Telecom-Churn-Predictor and telecom-customer-churn-prediction

These are competitors, as both tools predict customer churn in the telecom industry using machine learning, addressing the same problem independently.

Maintenance 13/25
Adoption 1/25
Maturity 9/25
Community 12/25
Maintenance 2/25
Adoption 6/25
Maturity 1/25
Community 15/25
Stars: 1
Forks: 1
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 20
Forks: 4
Downloads:
Commits (30d): 0
Language: HTML
License:
No Package No Dependents
No License Stale 6m 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 telecom-customer-churn-prediction

virajbhutada/telecom-customer-churn-prediction

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

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