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🍳 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.
  • GitHub Repository: Beautiful Soup GitHub

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.

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
  • GitHub Repository: Matplotlib GitHub

NumPy

  • Description: Fundamental package for scientific computing with Python.
  • Use Case: Numerical computations for nutritional data analysis and culinary science research.
  • Documentation: NumPy Documentation
  • GitHub Repository: NumPy GitHub

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
  • GitHub Repository: Pandas GitHub

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
  • GitHub Repository: Pillow GitHub

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
  • GitHub Repository: Plotly GitHub

Scikit-learn

  • Description: Machine learning in Python.
  • Use Case: Predictive modeling for culinary trends, diet customization, and recipe recommendation systems.
  • Documentation: Scikit-learn Documentation
  • GitHub Repository: Scikit-learn GitHub

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
  • GitHub Repository: Seaborn GitHub

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
  • GitHub Repository: spaCy GitHub