Dark Mode
Banking Churn Behaviour Dataset
Finance & Banking Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This Global Customer Churn Dataset is carefully put together to help understand and predict customer churn behaviour across various industries. It includes detailed customer profiles with demographics, product interactions, and banking behaviours. This dataset is a valuable resource for developing machine learning models to identify customers at risk of churning and to design targeted retention strategies.
Columns
- RowNumber: A unique identifier for each row in the dataset, ranging from 1 to 10,000.
- CustomerId: A unique identification number for each customer.
- Surname: The last name of the customer; it contains 2,932 unique values, with 'Smith' being the most common.
- CreditScore: The customer's credit score at the time of data collection, with values ranging from 350 to 850. The average score is 651.
- Geography: The customer's country or region, providing insights into location-based churn trends. There are 3 unique regions, with France being the most common (50%).
- Gender: The customer's gender, with Male (55%) and Female (45%) categories.
- Age: The customer's age, valuable for demographic analysis. Ages range from 18 to 92 years, with an average age of 38.9.
- Tenure: The number of years the customer has been with the bank, ranging from 0 to 10 years, with an average of 5.01 years.
- Balance: The customer's account balance, with values ranging from 0 to £251,000 and an average balance of £76,500.
- Num Of Products: The number of products the customer has purchased or subscribed to, ranging from 1 to 4, with an average of 1.53 products.
- HasCrCard: Indicates whether the customer has a credit card (1) or not (0). Approximately 71% of customers have a credit card.
- IsActiveMember: Indicates whether the customer is an active member (1) or not (0). Approximately 52% of customers are active members.
- EstimatedSalary: The customer's estimated salary, ranging from £11.60 to £200,000, with an average of £100,000.
- Exited: The target variable, indicating whether the customer has churned (1) or not (0). Approximately 20% of customers have churned.
Distribution
This dataset is provided in CSV format ("Churn_Modelling.csv") and has a file size of 684.86 kB. It contains 14 columns and 10,000 individual records or rows. All columns have valid and complete data, with no missing or mismatched values.
Usage
This dataset is ideal for:
- Developing machine learning models to predict customer churn.
- Identifying at-risk customers proactively.
- Devising targeted customer retention strategies.
- Business intelligence and data analytics to understand customer behaviour.
- Performing exploratory data analysis in the banking sector.
- Applications in classification tasks.
Coverage
The dataset primarily covers customer data from three geographic regions: France (being the most prevalent), Germany, and Spain. Demographic scope includes customer age (18-92 years), gender (Male/Female), credit scores, and estimated salaries. While specific time ranges for data collection are not provided, the 'Tenure' column reflects the duration of customer relationships, and the dataset is expected to be updated monthly, suggesting ongoing relevance.
License
CC0: Public Domain
Who Can Use It
This dataset is valuable for:
- Data Scientists and Machine Learning Engineers for building and testing predictive models.
- Business Analysts seeking insights into customer behaviour and churn drivers.
- Banking and Finance Professionals aiming to enhance customer retention.
- Researchers studying consumer patterns and market dynamics related to attrition.
Dataset Name Suggestions
- Global Banking Churn Predictor
- Customer Retention Analytics Data
- Banking Churn Behaviour Dataset
- Customer Exited Prediction Model Data
- Financial Services Churn Data
Attributes
Original Data Source:Banking Churn Behaviour Dataset
QUALITY