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Exercise - Analyse Bank Churn Data

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.

The data 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.

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.

Download the full dataset with 10,000 rows from here.

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.

FYI: This is fictional data. The original source the Microsoft Fabric Getting Started in Data Science Tutorial