https://github.com/machinelearningzuu/mlops-01-churn-analysis
This repository is dedicated to the application of MLOps practices in churn analysis. Churn analysis is a critical aspect of customer retention strategies, and through this project, we aim to streamline and automate the process using Machine Learning Operations (MLOps).
https://github.com/machinelearningzuu/mlops-01-churn-analysis
Last synced: about 1 year ago
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This repository is dedicated to the application of MLOps practices in churn analysis. Churn analysis is a critical aspect of customer retention strategies, and through this project, we aim to streamline and automate the process using Machine Learning Operations (MLOps).
- Host: GitHub
- URL: https://github.com/machinelearningzuu/mlops-01-churn-analysis
- Owner: machinelearningzuu
- License: mit
- Created: 2024-04-13T07:55:56.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T16:30:37.000Z (about 2 years ago)
- Last Synced: 2024-04-13T21:58:10.148Z (about 2 years ago)
- Language: Jupyter Notebook
- Size: 3.02 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MLOps-01-Churn Analysis
## Introduction
This repository is dedicated to the application of MLOps practices in churn analysis. Churn analysis is a critical aspect of customer retention strategies, and through this project, we aim to streamline and automate the process using Machine Learning Operations (MLOps).
## Project Description
The `mlops-01-churn-analysis` project utilizes various machine learning models to predict customer churn based on historical data. The project is structured to facilitate continuous integration and delivery for machine learning systems, ensuring that the churn prediction models are always up-to-date and performant.
## Features
- Automated data pipelines for real-time churn analysis
- Continuous training and deployment of ML models
- Monitoring and logging for model performance
- Easy integration with existing CRM systems
## Tech Stack
- **Data Processing**: Pandas, NumPy
- **Machine Learning**: Scikit-Learn, TensorFlow, PyTorch
- **MLOps Tools**: MLflow, Kubeflow, Docker, Kubernetes
- **Monitoring**: Prometheus, Grafana
## Getting Started
To get started with this project, follow the instructions below:
### Prerequisites
- Python 3.8+
- Docker
- Kubernetes cluster (Minikube or equivalent)
### Installation
1. Clone the repository:
- git clone https://github.com/machinelearningzuu/mlops-01-churn-analysis.git
- cd mlops-01-churn-analysis
2. Set up a virtual environment and install dependencies:
- python -m venv venv
- source venv/bin/activate
- pip install -r requirements_dev.txt
3. Initialize the MLflow tracking server:
- mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./artifacts --host 0.0.0.0
### Usage
To run the churn analysis pipeline, execute:
**python pipeline.py**
## Contributing
We welcome contributions to the `mlops-01-churn-analysis` project. Please read [CONTRIBUTING.md](https://www.freecodecamp.org/news/how-to-write-a-good-readme-file/) for details on our code of conduct, and the process for submitting pull requests to us.
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://dev.to/mfts/how-to-write-a-perfect-readme-for-your-github-project-59f2) file for details.
## Acknowledgments
- Thanks to the contributors who have helped shape this project.
- Special thanks to the MLOps community for their guidance and support.
## Contact
For any queries regarding this project, please open an issue in the repository or contact us directly at [machinelearningzuu@gmail.com](https://gist.github.com/PurpleBooth/109311bb0361f32d87a2).