https://github.com/sharathsphd/coffee_causality
Data-driven analysis of coffee shop sales using correlation, regression, and causal inference. A Jupyter Book project exploring foot traffic, weather patterns, and business analytics.
https://github.com/sharathsphd/coffee_causality
business-analytics causal-inference correlation data-analysis foot-traffic forecasting github-pages jupyter-notebook machine-learning open-source python regression retail-analytics statistics storytelling time-series visualization weather-analysis
Last synced: 2 months ago
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Data-driven analysis of coffee shop sales using correlation, regression, and causal inference. A Jupyter Book project exploring foot traffic, weather patterns, and business analytics.
- Host: GitHub
- URL: https://github.com/sharathsphd/coffee_causality
- Owner: SharathSPhD
- License: other
- Created: 2025-02-04T14:28:02.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-04T17:58:35.000Z (8 months ago)
- Last Synced: 2025-02-04T18:32:31.563Z (8 months ago)
- Topics: business-analytics, causal-inference, correlation, data-analysis, foot-traffic, forecasting, github-pages, jupyter-notebook, machine-learning, open-source, python, regression, retail-analytics, statistics, storytelling, time-series, visualization, weather-analysis
- Language: Jupyter Notebook
- Homepage: https://sharathsphd.github.io/coffee_causality/intro.html
- Size: 11.9 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Coffee Shop Causality Analysis
A Python package demonstrating causal inference methods through a coffee shop business case study.
## Features
- Synthetic data generation for coffee shop sales
- Multiple causal inference methods:
- Correlation Analysis
- Instrumental Variables (IV)
- Double Machine Learning (DML)
- Transfer Entropy
- Interactive visualizations
- Story-driven Jupyter notebooks
- Documentation using Sphinx
- Comprehensive test suite
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/coffee_causality.git
cd coffee_causality
```
2. Create and activate virtual environment:
```bash
# Windows
python -m venv venv
.\venv\Scripts\activate
# Linux/Mac
python -m venv venv
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Set up Jupyter kernel:
```bash
python -m ipykernel install --user --name=coffee_causality_venv --display-name "Python (coffee_causality)"
```
## Project Structure
```
coffee_causality/
├── .github/ # GitHub Actions workflows and templates
├── book/ # Documentation and tutorials
├── docs/ # Sphinx documentation
│ ├── build/ # Generated documentation
│ ├── source/ # Documentation source files
│ ├── Makefile # Documentation build script
│ └── conf.py # Sphinx configuration
├── notebooks/ # Jupyter notebooks
│ ├── A_Problem_Definition.ipynb # Problem setup and data generation
│ ├── B_Initial_Analysis.ipynb # Basic statistical analysis
│ ├── C_Advanced_Analysis.ipynb # Advanced causal methods
│ └── D_Transfer_Entropy.ipynb # Information flow analysis
├── plots/ # Generated visualizations
├── results/ # Analysis results and outputs
├── src/ # Source code
│ ├── __init__.py
│ ├── data_generator.py # Synthetic data generation
│ ├── causal_analysis.py # Causal inference implementations
│ └── visualization.py # Visualization tools
├── .gitignore # Git ignore rules
├── LICENSE # GPLv3 License
├── README.md # This file
└── requirements.txt # Project dependencies
```
## Core Components
### Source Code (`src/`)
- `data_generator.py`: Creates synthetic coffee shop data with hidden confounders
- `causal_analysis.py`: Implements various causal inference methods
- `visualization.py`: Provides visualization tools for analysis results
### Documentation (`docs/`)
- API Reference
- Implementation details
- Usage guides and tutorials
- Example notebooks
### Analysis Notebooks (`notebooks/`)
- Step-by-step analysis examples
- Interactive visualizations
- Results interpretation
## Dependencies
- **Data Processing**
- numpy: Numerical computing
- pandas: Data manipulation
- **Visualization**
- matplotlib: Basic plotting
- seaborn: Statistical visualizations
- networkx: Network analysis
- **Machine Learning**
- scikit-learn: Machine learning utilities
- econml: Double ML implementation
- **Causal Analysis**
- statsmodels: Statistical modeling
- IDTxl: Information theory
- **Development**
- jupyter: Notebook interface
- pytest: Testing framework
- sphinx: Documentation generation
- sphinx-rtd-theme: Documentation theme
## Documentation
The documentation is built using Sphinx and can be found in the `docs/` directory. To build the documentation:
```bash
cd docs
make html
```
The built documentation will be available in `docs/build/html/index.html`.
## Contributing
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing_feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing_feature`)
5. Open a Pull Request
## License
This project is licensed under the GPLv3 License - see the LICENSE file for details.