Telecom-Churn-Predictor and Telecom-Customer-Churn-prediction
These are **competitors**: both implement telecom churn prediction using machine learning classification on similar feature sets (usage, billing, support data), requiring users to select one approach over the other based on methodology and accuracy claims.
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
Pradnya1208/Telecom-Customer-Churn-prediction
Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
This project helps telecom companies predict which customers are likely to switch providers so they can proactively offer retention incentives. By analyzing customer data such as services used, contract details, payment methods, and demographics, it identifies 'high-risk' clients. This allows customer retention teams to focus their efforts on those most likely to churn.
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