Predicting Bank Churn Rate

This project aims to predict if the existing customer is going to stay with the bank or not.It is beneficial for banks to know what leads a client towards the decision to leave he company

Bank Churn Rate
Table of contents
Dataset
Methods Used
Technologies
Required Packages
Authors

Dataset

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Dataset is taken from Kaggle : https://www.kaggle.com/datasets/mathchi/churn-for-bank-customers

RowNumber: Corresponds to the record (row) number and has no effect on the output.
CustomerId: Contains random values and has no effect on customer leaving the bank.
Surname: The surname of a customer has no impact on their decision to leave the bank.
CreditScore: Can have an effect on customer churn, since a customer with a higher credit score is less likely to leave the bank.
Country: A customer’s location can affect their decision to leave the bank.
Gender: It’s interesting to explore whether gender plays a role in a customer leaving the bank.
Age: This is certainly relevant, since older customers are less likely to leave their bank than younger ones.
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.
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.
NumOfProducts: Refers to the number of products that a customer has purchased through the bank.
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.
IsActiveMember: Active customers are less likely to leave the bank.
EstimatedSalary: As with balance, people with lower salaries are more likely to leave the bank compared to those with higher salaries.
Exited: Whether or not the customer left the bank.

This dataset can be used to predict whether a customer is likely to leave the bank or not based on the various features provided. The columns can be used for exploratory data analysis and predictive modeling.