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Take recipes scraped from Food52, perform tf-idf and K-Means clustering, deploy some basic visualizations on a Flask web app.

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elisabeth-berg/trending-food

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Trending Foods

Plan of Action:

  • Scrape recipe information (title, ingredient list) from Food52
  • Perform NLP on the text data
  • Extract themes and trends from vectorized text

Load the Recipes:

import pandas as pd  
df = pd.read_pickle('data/featured_recipes.pkl')  

Plot Ingredient Usage Over Time:

from src.plotters import time_food_plot, yearly_food_plot

Pick any ingredient, and see how its use in recipes has changed over time!

time_food_plot(df, food='Basil', n_months=1, save=False) 

For a given ingredient, plot the monthly frequency of use for each year, along with the average monthly use.

yearly_food_plot(df, food='Basil', save=False)

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Take recipes scraped from Food52, perform tf-idf and K-Means clustering, deploy some basic visualizations on a Flask web app.

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