Data and AI Training

Home | Power BI | Excel | Python | SQL | Generative AI | Visualising Data | Analysing Data

Bank Churn Dataset

This dataset relates to factors that may contribute to a customer leaving a bank.

This data is used in Microsoft’s Fabric Getting Started in Data Science Tutorial. See this page for the data description

Data Description and Links

The data is in CSV file in a public web location. The URL is Bank Churn Data.

Some AI tools may not allow you to upload a large file of 10,000 rows. In that case, download a small subset of the data with 50 rows from here.

The dataset contains churn status of 10,000 customers. It also includes attributes that could impact churn such as:

• Credit score
• Geographical location (Germany, France, Spain)
• Gender (male, female)
• Age
• Tenure (years of being bank’s customer)
• Account balance
• Estimated salary
• Number of products that a customer has purchased through the bank
• Credit card status (whether a customer has a credit card or not)
• Active member status (whether an active bank’s customer or not)

The dataset also includes columns such as row number, customer ID, and customer surname that should have no impact on customer’s decision to leave the bank.

The event that defines the customer’s churn is the closing of the customer’s bank account. The column exited in the dataset refers to customer’s abandonment. There isn’t much context available about these attributes so you have to proceed without having background information about the dataset. The aim is to understand how these attributes contribute to the exited status.

The objective of this exercise is to understand what factors contribute to a customer churn. The full dataset contains churn status of 10,000 customers.

A customer churns when they close all their accounts and leave the bank. The column “Exited” has a value of 1 if the customer has churned, 0 if the customer remains with the bank.