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Banking Customer Churn Dataset
Finance & Banking Analytics
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Free
About
This dataset is designed for understanding customer behaviour and predicting churn in banking institutions. It contains detailed information about bank customers and their current churn status, indicating whether they have exited the bank or not. It is highly suitable for exploring various factors that influence customer churn within the banking sector and for developing predictive models to identify customers at risk of leaving the bank.
Columns
- RowNumber: A sequential number assigned to each row in the dataset.
- CustomerId: A unique identifier for each customer.
- Surname: The customer's surname.
- CreditScore: The customer's credit score.
- Geography: The customer's geographical location, such as a country or region.
- Gender: The customer's gender.
- 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 holds.
- HasCrCard: A binary indicator (yes/no) showing whether the customer possesses a credit card.
- IsActiveMember: A binary indicator (yes/no) showing whether the customer is an active member of the bank.
- EstimatedSalary: The customer's estimated salary.
- Exited: A binary indicator (yes/no) showing whether the customer has exited the bank.
Distribution
The dataset is typically provided as a CSV file, named
Churn_Modelling.csv. It contains 10,000 records (rows), each with 14 distinct columns. All columns have 100% valid data, with no missing or mismatched entries reported.Usage
This dataset can be effectively used for:
- Exploratory data analysis to gain insights into the key factors driving customer churn in banking.
- Building machine learning models to predict customer churn based on the provided features, enabling proactive customer retention strategies.
Coverage
The dataset includes customer information from three unique geographical locations, with France being the most common, accounting for 50% of the customer base. The age of customers ranges from 18 to 92 years, with an average age of 38.9 years. Gender distribution is also included, with 55% Male and 45% Female customers. The dataset is tagged as relevant to Europe.
License
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Who Can Use It
- Banking institutions: To identify at-risk customers and formulate retention strategies.
- Data analysts: For understanding customer behaviour and churn patterns.
- Machine learning practitioners: To develop and evaluate predictive churn models.
- Researchers: Studying customer loyalty and attrition in financial services.
Dataset Name Suggestions
- Banking Customer Churn Dataset
- Bank Customer Attrition Predictor
- Customer Exit Behaviour in Banking
- Financial Churn Analysis Data
Attributes
Original Data Source: Banking Customer Churn Dataset
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