Customer Lifetime Value Prediction, Survival Analysis & Segmentation

This project predicts Customer Lifetime Value (CLTV), implements survival analysis and performs customer segmentation for an e-commerce business.

Data: E-commerce Customer Data

Key Features

• Exploratory Data Analysis (EDA)
• Survival Analysis: Applied Kaplan-Meier model, Log-Rank Test, Cox model.
• CLTV Prediction: Applied BG/NBD and Gamma-Gamma models.
• Customer Segmentation: K-means and classification into segments like Loyal, Lost, and At-Risk.

Tools and Libraries

• pandas
• numpy
• lifelines (for Kaplan-Meier model, Log-Rank Test, Cox model)
• lifetimes (for BG/NBD & Gamma-Gamma models)
• Scikit-learn (for K-means clustering)
• Matplotlib, Seaborn (for visualizations)

License

This project is licensed under the MIT License.