sagnikghoshcr7/Bank-Customer-Churn-Prediction
Predict the Churn rate of a bank.
Implements multiple machine learning algorithms to classify customer churn using a 10,000-record dataset with 14 features including credit score, tenure, balance, and product engagement metrics. The pipeline performs feature engineering and validates model performance across train/test splits (70/30) to detect overfitting. Targets binary classification of the "Exited" variable to identify at-risk customers for retention strategies.
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Jupyter Notebook
License
MIT
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Last pushed
Jun 22, 2022
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