🛒 Introduction

Customer segmentation helps businesses identify different customer groups and develop more effective marketing strategies. In this project, we will use the K-Means clustering algorithm to segment customers based on their purchasing behavior.

📌 Objective

• Analyze customers using RFM (Recency, Frequency, Monetary) metrics
• Apply the K-Means algorithm to identify customer segments
• Compare K-Means results with rule-based segmentation using RFM metrics

📊 Dataset Used

• Online Retail II: Contains online sales transactions of a UK-based retail company between 2009 and 2011.
• Features (Columns):
  • InvoiceNo: Invoice number (If it starts with "C", the transaction is canceled)
  • StockCode: Unique product code
  • Description: Product name
  • Quantity: Number of products sold
  • InvoiceDate: Date of transaction
  • UnitPrice: Price of the product (£)
  • CustomerID: Unique customer identifier
  • Country: Customer’s country

🔍 Project Steps

✅ Clean the data by handling missing and incorrect values
✅ Calculate RFM metrics for customer segmentation
✅ Apply K-Means clustering to group customers

This project aims to develop an effective data-driven customer segmentation approach to better understand customer behavior and improve business decisions. 🚀