Premium Project

Credit Card Customer Segmentation

For this project, we’ll play the role of a data scientist at a credit card company to segment customers into groups using K-means clustering in Python, allowing the company to tailor strategies for each segment.

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4.8 (359 reviews)
366,013 learners enrolled in this project
  • Intermediate
  • Jupyter project
  • 7 steps
  • Python
Joel Ampong

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Joel Ampong

Data Scientist

Credit Card Customer Segmentation

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Project overview

In this project, you’ll take on the role of a data scientist at a credit card company that wants to segment its customers into groups to tailor its business strategies. You’ll use Python and popular data science libraries to prepare and cluster the customer data using the K-means algorithm.

By analyzing the characteristics of each customer segment, you’ll gain experience in translating data insights into actionable business recommendations. This project will strengthen your skills in data preparation, unsupervised machine learning, and applying data science to solve real-world business problems.

Objective: Use K-means clustering to segment credit card customers into groups with distinct characteristics in order to tailor business strategies to each segment.

Key skill required

To complete this project, it's recommended to build these foundational skills in Python

  • Understanding key concepts of unsupervised machine learning
  • Implementing the k-means clustering algorithm in Python
  • Working with Python data science libraries including NumPy, pandas, and scikit-learn
  • Preparing and standardizing data for cluster analysis

Projects steps

Step 1: Introduction

Step 2: Feature Engineering

Step 3: Feature Engineering - Part 2

Step 4: Scaling the Data

Step 5: Choosing K

Step 6: Analyzing Results

Step 7: Next Steps

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