Customer Churn Prediction Data App

This project is about embedding Machine Learning models into Streamlit for use by third party stakeholders allowing a better user experience than a Jupyter notebook.

Overview

Vodafone seeks to enhance its customer retention strategies by predicting customer churn using machine learning models. This project, leveraging the Streamlit framework, outlines the creation of a data application to deploy predictive models with a user-friendly interface.

Framework

The CRoss Industry Standard Process for Data Mining (CRISP-DM).

Streamlit app

Customer Churn Prediction Data App

Demo video

customer_churn_prediction_demo.mp4

Technologies Used

• Anaconda
• Streamlit
• Python
• Pandas
• Plotly
• Git
• Scipy
• Sklearn
• Adaboost
• Catboost
• Decision tree
• Kneighbors
• LGBM
• LogisticRegression
• RandomForest
• SVC
• XGBoost
• Joblib

Installation

Quick install

 pip install -r requirements.txt

Recommended install

conda env create -f streamlit_environment.yml

Usage

Use this command to run this data app:

streamlit run 🏠_Home.py

Contributions

How to Contribute

1. Fork the repository and clone it to your local machine.
2. Explore the Python scripts and documentation.
3. Implement enhancements, fix bugs, or propose new features.
4. Submit a pull request with your changes, ensuring clear descriptions and documentation.
5. Participate in discussions, provide feedback, and collaborate with the community.

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Feedback and Support

Feedback, suggestions, and contributions are welcome! Feel free to open an issue for bug reports, feature requests, or general inquiries. For additional support or questions, you can connect with me on LinkedIn.

Link to article on Medium: Building a Data App with Streamlit: Embedding Machine Learning Models for Predicting Customer Churn at Vodafone

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👥 Authors

🕺🏻Gabriel Okundaye

• GitHub: GitHub Profile

• LinkedIn: LinkedIn Profile

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⭐️ Show your support

If you like this project kindly show some love, give it a 🌟 STAR 🌟. Thank you!

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📝 License

This project is MIT licensed.

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