Credit card clustering involves categorizing credit card holders according to their purchasing patterns, credit limits, and various financial criteria. If you're interested in learning how to apply clustering analysis to group credit card holders, this article is tailored for you. Here, I will guide you through the process of credit card clustering using Machine Learning in Python.

 Credit Card Clustering

Credit card clustering involves grouping credit card holders according to their purchasing behaviors, credit limits, and various other financial factors. This practice is also referred to as credit card segmentation. Such clustering analysis assists businesses in identifying potential customers and formulating effective marketing strategies.

To conduct credit card clustering using Machine Learning, it's essential to have a dataset that includes the purchasing histories of credit card holders. I have identified an ideal dataset for this purpose, which contains all the necessary features to facilitate credit card cluster analysis. You can download the dataset from this link: https://statso.io/customer-segmentation-case-study/

In the following section, I will help you through the process of performing credit card clustering analysis using the Python programming language.

Credit Card Clustering using Python

Let’s start the task of credit card cluster analysis by importing the necessary Python libraries and the dataset:

1. import pandas as pd

2. import numpy as np

3. from sklearn import cluster

4. data = pd.read_csv("CC GENERAL.csv")

5. print(data.head())

Before moving forward, I checked whether this dataset contains any null values or not:

1. data.isnull().sum()

The dataset has some null values in the minimum payments column. I will drop the rows with null values and move further:

data = data.dropna()

There are three features in the dataset that are very valuable for the task of credit card segmentation:

1. BALANCE: The balance left in the accounts of credit card customers.

2. PURCHASES: Amount of purchases made from the accounts of credit card customers.

3. CREDIT_LIMIT: The limit of the credit card.

These three features are enough to group credit card holders as they tell us about the buying history, bank balance, and credit limit of the credit card holders. So let’s use these features to create clusters from the dataset: