Churn Classification for Bank Customers

---

This data set contains details of a bank's customers and the target variable is a binary variable reflecting the fact whether the customer left the bank (closed his account) or he continues to be a customer. Dataset which contain some customers who are withdrawing their account from the bank due to some loss and other issues with the help this data we try to analyse and maintain accuracy.

Dataset Content

We've Dataset with 10000 rows and 14 columns that're :

1. RowNumber—corresponds to the record (row) number and has no effect on the output.
2. CustomerId—contains random values and has no effect on customer leaving the bank.
3. Surname—the surname of a customer has no impact on their decision to leave the bank.
4. CreditScore—can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
5. Geography—a customer’s location can affect their decision to leave the bank.
6. Gender—it’s interesting to explore whether gender plays a role in a customer leaving the bank.
7. Age—this is certainly relevant, since older customers are less likely to leave their bank than younger ones.
8. Tenure—refers to the number of years that the customer has been a client of the bank. Normally, older clients are more loyal and less likely to leave a bank.
9. Balance—also a very good indicator of customer churn, as people with a higher balance in their accounts are less likely to leave the bank compared to those with lower balances.
10. NumOfProducts—refers to the number of products that a customer has purchased through the bank.
11. HasCrCard—denotes whether or not a customer has a credit card. This column is also relevant, since people with a credit card are less likely to leave the bank.
12. IsActiveMember—active customers are less likely to leave the bank.
13. EstimatedSalary—as with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
14. Exited—whether or not the customer left the bank.

For more information about dataset and problem : https://www.kaggle.com/shrutimechlearn/churn-modelling