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Bank Customer Churn Prediction, Binary Classification with a Bank Churn Dataset

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BillyGareth/Bank-Customer-Churn-Prediction

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Title: Bank Churn Binary Classification Problem

Columns Description:

Customer ID: A unique identifier for each customer Surname: The customer's surname or last name Credit Score: A numerical value representing the customer's credit score Geography: The country where the customer resides (France, Spain or Germany) Gender: The customer's gender (Male or Female) Age: The customer's age. Tenure: The number of years the customer has been with the bank Balance: The customer's account balance NumOfProducts: The number of bank products the customer uses (e.g., savings account, credit card) HasCrCard: Whether the customer has a credit card (1 = yes, 0 = no) IsActiveMember: Whether the customer is an active member (1 = yes, 0 = no) EstimatedSalary: The estimated salary of the customer Exited: Whether the customer has churned (1 = yes, 0 = no)

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Bank Customer Churn Prediction, Binary Classification with a Bank Churn Dataset

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