CLTV 0.1.0

A customer Lifetime Value package for preprocessing data

Customer Lifetime Value (CLTV) Analysis

Overview

This package provides tools for Customer Lifetime Value (CLTV) analysis, including data processing, visualization, and predictive modeling. It utilizes the BetaGeoFitter and GammaGammaFitter from the lifetimes library to calculate and analyze customer lifetime value.

Features

• Data Processing and Visualization:

  • Clean and preprocess data
  • Calculate CLTV using BG/NBD and Gamma-Gamma models
  • Visualize CLTV and customer segments

• Logistic Regression:

  • Train a logistic regression model to classify high-value customers
  • Evaluate model performance using various metrics

• Enhanced Visualizations:

  • Generate correlation heatmaps
  • Cluster customers using K-Means and visualize clusters

Requirements

To use this package, you need the following Python packages:

• pandas
• numpy
• matplotlib
• seaborn
• lifetimes
• scikit-learn

You can install these dependencies using:

pip install -r requirements.txt

Usage

To import the package and run the main function, use the following format:

from CLTV.cltv import main