https://github.com/renoyegon/customer_segmentation_using_kmeans_clustering
This project applies KMeans clustering to segment customers in the Online Retail II dataset. Using powerful Python libraries such as pandas, scikit-learn, matplotlib, and seaborn, we uncover meaningful customer behavior patterns
https://github.com/renoyegon/customer_segmentation_using_kmeans_clustering
kmeans-clustering matplotlib scikit-learn seaborn
Last synced: 2 months ago
JSON representation
This project applies KMeans clustering to segment customers in the Online Retail II dataset. Using powerful Python libraries such as pandas, scikit-learn, matplotlib, and seaborn, we uncover meaningful customer behavior patterns
- Host: GitHub
- URL: https://github.com/renoyegon/customer_segmentation_using_kmeans_clustering
- Owner: RENOYEGON
- Created: 2025-06-12T03:29:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-12T03:34:07.000Z (about 1 year ago)
- Last Synced: 2025-06-12T04:31:08.943Z (about 1 year ago)
- Topics: kmeans-clustering, matplotlib, scikit-learn, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 1.19 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Customer Segmentation Using KMeans Clustering
#### Analyzing Online Retail II Dataset with Python, pandas, and scikit-learn
## Project Overview
This project applies KMeans clustering to the Online Retail II dataset to identify distinct customer segments.
By leveraging powerful Python libraries like `pandas`, `scikit-learn`, `matplotlib`and `seaborn`, the project uncovers meaningful patterns in customer behavior that can inform business strategies, improve targeting, and enhance customer experience
## Clustering Approach
- Data Preprocessing
- Feature Engineering
- KMeans Clustering
- Visualization
- Interpretation
## Scripts
- Data Exploration - [EDA Script](Scripts/online-retail-data-clustering_EDA.ipynb)
- KMeans Clustering Work - [Clustering Script](Scripts/clustering_to_classify_online_retail_customers.ipynb)
## Dataset Information
- Source: UCI Machine Learning Repository
- Title: Online Retail II
- Dataset Link: https://doi.org/10.24432/C5CG6D
- Period Covered: *December 1, 2009* – *December 9, 2011*
- Contains Missing Values? Yes
### Citation
```bibtex
Chen, D. (2012).
Online Retail II [Dataset].
UCI Machine Learning Repository.
https://doi.org/10.24432/C5CG6D