# 🍳 Culinary Arts

### BeautifulSoup

* **Description**: A library for pulling data out of HTML and XML files.
* **Use Case**: Scraping recipes, food blogs, and culinary reviews from websites for analysis and culinary research.
* **Documentation**: [Beautiful Soup Documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)
* **GitHub Repository**: [Beautiful Soup GitHub](https://www.crummy.com/software/BeautifulSoup/)

### FlavorNetwork

* **Description**: Based on a scientific study that presents a flavor network to understand and explore food pairings.
* **Use Case**: Analyzing and predicting flavor pairings and recipe component compatibility.
* **Documentation**: [Flavor Network Research Paper](https://www.nature.com/articles/srep00196)

### Matplotlib

* **Description**: A library for creating static, animated, and interactive visualizations in Python.
* **Use Case**: Visualization of culinary data, such as nutrition analysis, food trends, and culinary preference statistics.
* **Documentation**: [Matplotlib Documentation](https://matplotlib.org/)
* **GitHub Repository**: [Matplotlib GitHub](https://github.com/matplotlib/matplotlib)

### NumPy

* **Description**: Fundamental package for scientific computing with Python.
* **Use Case**: Numerical computations for nutritional data analysis and culinary science research.
* **Documentation**: [NumPy Documentation](https://numpy.org/doc/)
* **GitHub Repository**: [NumPy GitHub](https://github.com/numpy/numpy)

### Pandas

* **Description**: Data analysis and manipulation library.
* **Use Case**: Organizing, analyzing, and manipulating culinary datasets such as ingredient databases and recipe collections.
* **Documentation**: [Pandas Documentation](https://pandas.pydata.org/)
* **GitHub Repository**: [Pandas GitHub](https://github.com/pandas-dev/pandas)

### Pillow (PIL Fork)

* **Description**: The Python Imaging Library adds image processing capabilities to Python.
* **Use Case**: Image processing for culinary presentations, cookbook layouts, and food photography enhancement.
* **Documentation**: [Pillow Documentation](https://pillow.readthedocs.io/en/stable/)
* **GitHub Repository**: [Pillow GitHub](https://github.com/python-pillow/Pillow)

### Plotly

* **Description**: An interactive graphing library.
* **Use Case**: Creating interactive visualizations for culinary data, such as customer preference data or ingredient nutritional content.
* **Documentation**: [Plotly Documentation](https://plotly.com/python/)
* **GitHub Repository**: [Plotly GitHub](https://github.com/plotly/plotly.py)

### Scikit-learn

* **Description**: Machine learning in Python.
* **Use Case**: Predictive modeling for culinary trends, diet customization, and recipe recommendation systems.
* **Documentation**: [Scikit-learn Documentation](https://scikit-learn.org/stable/)
* **GitHub Repository**: [Scikit-learn GitHub](https://github.com/scikit-learn/scikit-learn)

### Seaborn

* **Description**: A Python data visualization library based on Matplotlib.
* **Use Case**: Creating attractive and informative statistical graphics in culinary studies and food science.
* **Documentation**: [Seaborn Documentation](https://seaborn.pydata.org/)
* **GitHub Repository**: [Seaborn GitHub](https://github.com/mwaskom/seaborn)

### spaCy

* **Description**: An open-source software library for advanced natural language processing.
* **Use Case**: Analyzing culinary text data, such as recipes and reviews, for insights into flavor profiles and culinary trends.
* **Documentation**: [spaCy Documentation](https://spacy.io/)
* **GitHub Repository**: [spaCy GitHub](https://github.com/explosion/spaCy)