An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.
python machine-learning exploratory-data-analysis churn-prediction end-to-end-machine-learning mlops churn-analysis customer-churn-prediction customer-churn customer-churn-analysis mlops-project customer-churn-prediction-with-machine-learning machine-larning-projects
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Updated
Oct 18, 2024 - Jupyter Notebook