Predicting Bank Customer Churn

This is an exampel for solving classification problems with Python’s Scikit-learn library.

We will try to predict whether or not a bank customer will churn in the future, base on the data.

Identify potential churn factors

  • Credit Score
  • Country
  • Gender
  • Age group
  • Tenure
  • Balance
  • Number of products
  • Has credit card
  • Is active member
  • Estimated Salary

Potential churn factors?

Country

Country distribution on churners It is seen that customer churn rates in Germany and France are 2 times higher than in Spain.

Gender

As can be seen on the pie graph, the female churner rate is higher than the male churner rate.

Number of product

The heat map indicates that the churned customer number decreases in all countries when the number of products increases.

Credit Card

Interestingly, churn decision among customers is higher even they have a credit card.

Being an active member

Churner number is lower among customers who are active members.

The average age of churned customers who are not active members and don’t have a credit card is higher. The age range of customers having 1 product is very large. There is no non-churner even they have 4 products if they don’ have a credit card and are not active members.

The average age of churned customers who are active members and have a credit card is highest if they have 4 products.

Age Group

Customers, who are under age 25 & age 65 and older in Germany and France, tend to churn more with respect to balance and credit score.

In addition, credit score, tenure, balance, and estimated salary don’t affect churn decision. Still, I would like to point on the balances in Germany where there is no zero balance in their account.

Less Loyal Customers

  • Germany & France
  • Female
  • NumOfProduct < 2
  • Age 25-64
  • Has Credit Card
  • Student & retired customers in Germany & France

More Loyal Customers

  • Spain
  • Male
  • NumOfProduct >= 2
  • Active member
  • Student & retired customers in Spain

Data

The data for this excampel are from https://www.kaggle.com/datasets/hj5992/bank-churn-modelling