Guided Project: Credit Card Customer Segmentation
- Last updated on April 25, 2025 at 2:43 PM
About this Webinar
In this hands-on Project Lab, Dataquest’s Senior Content Developer, Anna Strahl, walks you through how to apply K-means clustering in Python to segment credit card customers into distinct groups.
You’ll step into the role of a data scientist at a credit card company, using real-world data and unsupervised learning techniques to uncover patterns that inform strategic business decisions.
This project is ideal for learners comfortable with Python, pandas, NumPy, Matplotlib, Seaborn, and intermediate-level machine learning concepts.
What You'll Learn:
- How to prepare and scale data for unsupervised learning
- Techniques for conducting feature engineering and data standardization
- How to determine the optimal number of customer segments using K-means clustering
- Ways to analyze and interpret cluster results for strategic insights
- Real-world techniques to turn data into clear, actionable business decisions
Key Skills Covered in This Project:
- Data preparation and feature engineering using pandas and NumPy
- Data visualization with Matplotlib and Seaborn
- Implementing K-means clustering with scikit-learn
- Choosing optimal cluster numbers with the elbow method
- Translating data science findings into business strategy
- Unsupervised learning for customer segmentation
📌 Note: This is a premium project that has been opened up for free to all webinar participants from April 18–25.
New to Python? Begin with our Python Basics for Data Analysis course to build the foundational skills needed for this project.
New to machine learning? Begin with our Machine Learning in Python course to build the foundational skills needed for this project.
Before You Start: Pre-Instruction
To make the most of this project walkthrough, follow these preparatory steps:
1. Review the Project
Access the project and familiarize yourself with the goals and structure:
- Start the project here
2. Access the Solution Notebook:
You can view and download it here to see what we’ll be covering:
Helpful Tips
New to Markdown? We recommend learning the basics to format headers and add context to your Jupyter notebook: Markdown Guide.
For file sharing and project uploads, it is important that you create a GitHub account ahead of the webinar: Sign Up on GitHub.
Want to work offline?
1. Set Up Your Workspace
We'll work with a basics.ipynb file, which can be rendered in the following tools:
- Jupyter Notebook (local installation required)
- Google Colab (browser-based, no installation needed)
2. Download the Resource Files
To follow along with the webinar, you'll need the customer_segmentation.csv dataset, which contains information about the company’s clients, and we're asked to help segment them into different groups in order to apply different business strategies for each type of customer.