Hosted at Heroku
The Epicuriousity project analyzes the epirecipes dataset found on Kaggle that contains over 20k Epicurious recipes with their ratings, categories and nutritional information. We used this dataset to understand the popularity of recipes in relation to nutritional information, number of ingredients and categories.
Project Members Amy Koldeway, Sarah Cross, Alli Vaughn
Data Set Link to dataset: https://www.kaggle.com/hugodarwood/epirecipes
EPI-FACTOR (Normalized Data)
XNORMAL = (XAVG – XMIN) / (XMAX – XMIN)
Where, data is binned for each star rating (0 to < 1, 1 to < 2, etc.)):
XNORMAL = normalized average calories (per rating bin) XAVG = average calories (per rating bin) XMIN = minimum overall calories XMAX = maximum overall calories
Each Radar Chart represents the data grouped by average star rating. This allows you to quickly compare how each value changes with star rating.
Calories (normalized, cal) Fat (g) Protein(g) Number of Ingredients
Data Cleaning
def get_epifactor(min_val, max_val, mean_val): return ((mean_val - min_val) / (max_val - min_val)) * 100