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.