Welcome to the Churn Analysis repository! This project focuses on analyzing customer churn data to predict which customers are likely to leave a service or subscription. Understanding churn is crucial for businesses looking to improve customer retention strategies and enhance overall customer satisfaction.
Another important topic is, explaining complex results to non-tec stakeholders, to deal with the output of a black box model such as xgboost, I've leveraged shap package to interpret the importance of the variables.
Churn analysis aims to identify the key factors that contribute to customer attrition. This repository contains the datasets, scripts, and models used to predict churn based on historical data. The analysis can help businesses develop targeted interventions to retain high-risk customers.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
What things you need to install the software and how to install them
pip install numpy
pip install scipy
pip install itertools
pip install pandas
pip install scikit-learn
pip install matplotlib
pip install shap
pip install xgboostA step-by-step series of examples that tell you how to get a development environment running:
git clone https://github.com/IanniMuliterno/churn_analysis.gitWe welcome contributions to this project. If you would like to contribute, please follow these guidelines:
- Fork the Repository
- Create your Feature Branch (git checkout -b feature/AmazingFeature)
- Commit your Changes (git commit -m 'Add some AmazingFeature')
- Push to the Branch (git push origin feature/AmazingFeature)
- Open a Pull Request
Fridrich, M., & Dostál, P. (2022). User Churn Model in E-Commerce Retail. Scientific Papers of the University of Pardubice, Series D: Faculty of Economics and Administration, 30(1). https://doi.org/10.46585/sp28031105