Customer Lifetime Value (CLV) Prediction – Jupyter Notebook Package

Customer Lifetime Value (CLV) Prediction – Jupyter Notebook Package

Unlock the full machine learning workflow behind the paper “Strategic Customer Lifetime Value Prediction: Leveraging Machine Learning to Maximize Profitability in Retail.”

This premium Jupyter Notebook package includes all Python code used for data cleaning, feature engineering, model training (Linear Regression & XGBoost), and RFM + K-Means customer segmentation. Designed for analysts, data scientists, and retail strategists, it offers a fully reproducible framework to predict CLV and identify high-value customer segments.

Includes

• Complete Jupyter Notebook (.ipynb)
• UCI Online Retail (2010–2011) dataset (free public data, attribution included)
• Step-by-step code documentation and visual outputs
• Ready-to-adapt pipeline for your own retail datasets

Reference Paper: https://dx.doi.org/10.2139/ssrn.5291821
Data source: https://archive.ics.uci.edu/dataset/352/online+retail

🔒 License: For personal and educational use only. Redistribution prohibited.