Credit Card Customer Segmentation
- Guided Project
- 0.9 hours
- Intermediate
- Python
Practice using K-means clustering in Python to segment credit card customers and tailor business strategies.
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.
What You'll Learn
- ✓ Perform exploratory data analysis on customer data
- ✓ Transform categorical variables for machine learning algorithms
- ✓ Use K-means clustering to segment customers
- ✓ Apply the elbow method to determine the optimal number of clusters
- ✓ Interpret and visualize clustering results for actionable insights
Before You Start
- ✓ 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
Project Steps
7 steps
- 1 Introduction
- 2 Feature Engineering
- 3 Feature Engineering - Part 2
- 4 Scaling the Data
- 5 Choosing K
- 6 Analyzing Results
- 7 Next Steps
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