This project implements unsupervised machine learning techniques for customer segmentation in a bank. It aims to classify customers into four distinct groups to help the bank's marketing team launch targeted ad campaigns.
The bank marketing team would like to leverage AI/ML to launch a targeted mareting ad campaign that is tailored to specific grop of customers. This task is crucial for maximizing marketing campaign conversion rate.
In order for this campaign to be successful, the bank has to divide it customers into at least 3 distinctive groups. So, we going to divide the the customers into 4 distinctive groups as follows:
- First Customers cluster
Transactors: Those are customers who pay least amount of intrerest charges and careful with their money, Cluster with lowest balance ($104) and cash advance ($303), Percentage of full payment = 23% - Second customers cluster
revolverswho use credit card as a loan (most lucrative sector): highest balance ($5000) and cash advance (~$5000), low purchase frequency, high cash advance frequency (0.5), high cash advance transactions (16) and low percentage of full payment (3%) - Third customer cluster
VIP/Prime: high credit limit $16K and highest percentage of full payment, target for increase credit limit and increase spending habits - Fourth customer cluster
New Customers: these are customers with low tenure (7 years), low balance
we are going to apply K-Means algorithm (clustering) along with Principle Component Analysis (PCA) alogorithm.
Install the required Python packages:
pandas==2.2.1numpy==1.26.4seaborn==0.13.2matplotlib==3.8.3scikit-learn==1.4.1
The dataset used in this project is Credit Card Dataset for Clustering. It includes the usage behavior of about 9000 active credit card holders during the last 6 months.