Clustering Analysis: Online Retail Dataset

Overview

The project utilizes the Online Retail Dataset, a transnational dataset capturing transactions from 01/12/2010 to 09/12/2011 for a UK-based non-store online retail company specializing in unique all-occasion gifts. The dataset includes transactions from both retail and wholesale customers.

Objectives

• Clustering Analysis: Implement K-means and hierarchical clustering algorithms to segment customers based on their purchasing behaviors.
• Customer Segmentation: Identify distinct customer groups (clusters) that exhibit similar purchasing patterns.

Dataset

The dataset includes attributes such as customer ID, product description, quantity purchased, and transaction date, providing rich information for clustering analysis.

Key Components

Code: Implementation of K-means and hierarchical clustering algorithms in Python.

Data: The Online Retail Dataset used for clustering analysis.

Documentation: Detailed explanation of the steps performed