The customer churn dataset is a collection of customer data that focuses on predicting customer churn, which refers to the tendency of customers to stop using a company's products or services. The dataset contains various features that describe each customer, such as their credit score, country, gender, age, tenure, balance, number of products, credit card status, active membership, estimated salary, and churn status. The churn status indicates whether a customer has churned or not. The dataset is used to analyze and understand factors that contribute to customer churn and to build predictive models to identify customers at risk of churning. The goal is to develop strategies and interventions to reduce churn and improve customer retention

Bank Customer Churn Dataset is a collection of data related to customers of a bank who have either left (churned) or stayed with the bank. This dataset is typically used for predictive modeling to identify patterns and factors that lead to customer churn, enabling banks to take proactive measures to retain customers.

• id: Unique identifier for each customer.

• CustomerId: Unique identifier for the customer account.

• Surname: Last name of the customer.

• CreditScore: Numeric representation of the customer's creditworthiness.

• Geography:str, Gender:str:Country or region where the customer resides ,Gender of the customer (e.g., Male, Female).

• Age: Age of the customer.

• Tenure: Number of years the customer has been with the bank.

• Balance: Current balance in the customer's account.

• NumOfProducts: Number of bank products the customer uses.

• HasCrCard: Binary indicator (0 or 1) for whether the customer has a credit card.

• IsActiveMember: Binary indicator (0 or 1) for whether the customer is an active member.

• EstimatedSalary: Estimated salary of the customer.

• Exited: Binary indicator (0 or 1) for whether the customer has churned (the target).