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Assignment 4: Customer Segmentation with Clustering

Overview

This project implements a customer segmentation analysis for a UK-based online gift retailer using K-means clustering. The analysis segments customers based on their transaction history (2009-2011) using RFM (Recency, Frequency, Monetary) features to identify distinct customer groups and provide targeted marketing recommendations.

Key Objectives

  • Aggregate transaction data to customer-level RFM features
  • Determine optimal number of clusters using elbow method and silhouette analysis
  • Train K-means clustering model to segment customers
  • Visualize and interpret customer segments
  • Provide actionable business recommendations for each segment

Project Structure

Assignment-4/
├── README.md                    # Project documentation (this file)
├── starter_notebook.ipynb       # Main Jupyter notebook with analysis
└── data/
    └── online_retail_II.csv     # Transaction dataset (2009-2011)

Data Description

The online_retail_II.csv dataset contains transaction records with the following key fields:

  • Invoice: Unique transaction identifier
  • StockCode: Product code
  • Description: Product name
  • Quantity: Number of items purchased
  • InvoiceDate: Transaction date and time
  • Price: Unit price per item
  • Customer ID: Unique customer identifier
  • Country: Customer's country

How to Run

Option 1: Using Jupyter Notebook in VS Code

  1. Open the notebook:

    • Open starter_notebook.ipynb in VS Code

  2. Install dependencies:

    • Run the first code cell to install required packages: 
      pip install pandas matplotlib seaborn scikit-learn

  3. Run the analysis:

    • Execute cells sequentially from top to bottom
    • Fill in the TODO sections as you progress through the assignment

Option 2: Using Terminal

  1. Install dependencies:

    pip install pandas matplotlib seaborn scikit-learn

  2. Launch Jupyter:

    jupyter notebook starter_notebook.ipynb

    Or:

    jupyter lab

  3. Execute the notebook:

    • Run cells in order, completing the TODO sections

Notebook Structure

The analysis is organized into six main steps:

Step 1: Import Libraries and Load Data

  • Import required Python libraries
  • Load the Online Retail II dataset
  • Display basic dataset information

Step 2: Aggregate Transaction Data to Customer-Level RFM Features

  • Clean transaction data (remove missing IDs, returns, etc.)
  • Calculate RFM metrics for each customer:
    • Recency: Days since last purchase
    • Frequency: Number of unique transactions
    • Monetary: Total amount spent

Step 3: Standardize Features and Determine Optimal k

  • Standardize RFM features using StandardScaler
  • Apply elbow method to test k values from 2 to 10
  • Calculate silhouette scores for cluster quality validation
  • Select optimal k value with written justification

Step 4: Train K-Means Model and Visualize Segments

  • Train final K-means model with optimal k
  • Add cluster labels to customer data
  • Create scatter plot visualization (Frequency vs Monetary)
  • Calculate and display cluster centers

Step 5: Interpret Segments and Provide Business Recommendations

  • Analyze each customer segment's characteristics
  • Provide descriptive names for segments
  • Calculate detailed statistics for each segment

Step 6: Submit Your Work

  • Verify all code runs without errors
  • Push to GitHub
  • Submit repository link

Submission

After completing all steps:

git add .
git commit -m 'completed customer segmentation assignment'
git push

Submit your GitHub repository link on the course platform.