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, matplotliband seaborn, the project uncovers meaningful patterns in customer behavior that can inform business strategies, improve targeting, and enhance customer experience

• Data Preprocessing
• Feature Engineering
• KMeans Clustering
• Visualization
• Interpretation

• Data Exploration - EDA Script
• KMeans Clustering Work - Clustering Script

• 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

Chen, D. (2012). 
Online Retail II [Dataset].
UCI Machine Learning Repository.
https://doi.org/10.24432/C5CG6D