Online Retail Customer Analysis

This project focuses on understanding customer behavior through an in-depth analysis of transaction data from an online retail store. By utilizing K-Means clustering, it uncovers insights that help businesses better understand their customer base and make informed strategic decisions. This analysis applies the Recency, Frequency, and Monetary (RFM) framework to profile customers effectively.

Dataset: Online Retail II

Key Features for Analysis

The analysis focuses on three primary metrics:

• Recency: Number of days since a customer’s last purchase.
• Frequency: Total number of purchases made by a customer.
• Monetary Value: Total monetary contribution of each customer

Final Clustering Result

Getting Started

To begin, ensure that Python 3.8+ is installed on your local machine.

Clone the Repository

git clone [email protected]:abdullahashfaq-ds/Online-Retail-Customer-Analysis.git
cd Online-Retail-Customer-Analysis

Virtual Environment Setup

python -m venv venv
# On Windows, use:
venv\Scripts\activate
# On Linux/MacOS, use:
source venv/bin/activate
# To set up the environment:
pip install -r requirements.txt

Once the environment is set up, open the Jupyter notebook to start the analysis.

License

This project is licensed under the MIT License. See the LICENSE file for more details.