https://github.com/krik8235/ml-clv-prediction
Predict customer lifetime value of e-commerce customers using ML algorithms
https://github.com/krik8235/ml-clv-prediction
decision-trees gradient-boosting jupyter-notebook linerregression machine-learning machine-learning-algorithms random-forest
Last synced: 6 months ago
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Predict customer lifetime value of e-commerce customers using ML algorithms
- Host: GitHub
- URL: https://github.com/krik8235/ml-clv-prediction
- Owner: krik8235
- Created: 2024-09-27T06:28:13.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-09-27T10:04:23.000Z (about 1 year ago)
- Last Synced: 2025-02-12T05:45:12.635Z (8 months ago)
- Topics: decision-trees, gradient-boosting, jupyter-notebook, linerregression, machine-learning, machine-learning-algorithms, random-forest
- Language: Jupyter Notebook
- Homepage: https://kuriko.vercel.app/ml-projects/predict-clv
- Size: 48.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predict customer lifetime value of e-commerce customers
This repository contains the code and analysis for a machine learning project aimed at predicting Customer Lifetime Value (CLV) in an e-commerce context. The project utilizes a range of machine learning models to forecast the total revenue a business can expect from a customer throughout their relationship.
## Project Overview
The goal of this project is to build a predictive model that can accurately forecast the Customer Lifetime Value (CLV) for an e-commerce business. Accurate predictions of CLV assist businesses in optimizing marketing strategies, focusing on customer retention, and efficiently allocating resources toward the most valuable customers.
### Dataset
The project is based on the "Online Retail II" dataset from the UCI Machine Learning Repository, which includes transactional data of a UK-based online retailer from December 2009 to December 2011.
### Objectives
- Perform exploratory data analysis to understand customer purchasing behavior.
- Develop predictive models for CLV and compare their performance.
- Extract actionable insights to guide marketing and business strategies.
## Models(Go to the last section in the notebook)
The project explores several machine learning models:
- Linear Regression (Baseline Model)
- Decision Tree Regressor
- Random Forest Regressor
- Gradient Boosting Regressor
- Neural Network (Multilayer Perceptron)
Each model's performance is evaluated based on RMSE (Root Mean Square Error) and R-squared metrics.