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RafaelPrime/
telecom-churn-xgboost

Name: telecom-churn-xgboost
Availability: InStock

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telecom-churn-xgboost

Modelo de classificação binária para previsão de churn em clientes de telecomunicações.

Métricas (conjunto de teste — holdout 15%)

Métrica	Valor
Recall(churn)	0.7429
F1 (weighted)	0.7681
ROC-AUC	0.8162
Accuracy	0.7573

Uso

python

import joblib, pandas as pd
from huggingface_hub import hf_hub_download

model_path = hf_hub_download(repo_id="RafaelPrime/telecom-churn-xgboost", filename="xgboost.joblib")
prep_path  = hf_hub_download(repo_id="RafaelPrime/telecom-churn-xgboost", filename="preprocessor.joblib")

model      = joblib.load(model_path)
preprocessor = joblib.load(prep_path)

# X_raw: DataFrame com as colunas originais do dataset
X_proc = preprocessor.transform(X_raw)
pred   = model.predict(X_proc)