JimKing100 / strains-live

Content-based Recommendation Using TF-IDF and k-NN

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strains-live

Content-based Recommendation Using TF-IDF and k-NN

The contents include the following:

data - The data directory

  • cannabis.csv - The Cannabis Strains dataset posted on Kaggle by LiamLarsen consisting of 2,350 unique cannabis strains.

model - The model directory

  • dtm.pkl - The pickled document-term matrix
  • tf.pkl - The pickled tf-idf vectorizer
  • tf_knn.ipynb - The Jupyter notebook containing the TF-IDF and k-NN code

tabs - The tabs directory

  • about.py - The about tab Dash code
  • intro.py - The intro tab Dash code
  • recommend.py - The recommend tab Dash code and recommendation code

The main app

  • Procfile - The Procfile for Heroku
  • app.py - Initiates the Dash app
  • index.py - The main Dash code with the layout and callback
  • requirements.txt - The requirements.txt file for Heroku

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Content-based Recommendation Using TF-IDF and k-NN

License:MIT License


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Language:Jupyter Notebook 81.1%Language:Python 18.9%