| Chase Goldberg | Jorge A. Lima | Jud Taylor |
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Project completed in four days on a crossfunctional team of eight that included data science and web development, students.
Recommend Five based on strain, effects, and flavors proposed by app user.
Documents: are constructed by string representations of Strain,Effects, and Flavors.
- Extract usable features from high-dimensional data via Basilica
- Train our embeddings on data points
- The docs are vectorized
- A function is defined to take a text, searches the space, and returns three closest matches.
The data was trained on a K Nearest Neighbors model. After trying various word-vectorization techniques, We found the Basilica embeddings to be the most accurate.
The user has access to the recommender, where they can enter in a text box what they are looking for, whether it be for medical or recreational use. Five recommended strains will be returned, and the user can then read additional information about the strain and then save various strains to their med cabinet. The user may also search the database for strains based on different features.
Python
Flask, Heroku
K-Nearest Neighbors, Natural Language Processing
- Chase Goldberg: Data Engineer
- Jorge A. Lima: Machine Learning Engineer
- Judd Taylor: Machine Learning Engineer
- MIT license
- Copyright 2020 © Jorge A. Lima.