This repository contains code and resources for predicting visitor-to-customer conversion in an online store using machine learning. The goal of this project is to help online store owners improve their revenue by accurately predicting which visitors are likely to make a purchase.
The project involves building a predictive model using machine learning algorithms to classify website visitors into two groups - those who are likely to make a purchase and those who are not. The data used for the project includes various features such as visitor location, time spent on the website, and product categories viewed.
To run the code in this repository, you will need the following:
- Python 3.6 or higher
- Jupyter Notebook
- scikit-learn
- pandas
- numpy
This project includes CI/CD pipelines to automate the testing, building, and deployment of the code. The pipelines are implemented using GitHub Actions and can be found in the .github/workflows directory.
This project is licensed under the MIT License - see the LICENSE file for details.