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taken from: https://www.kaggle.com/datasets/crawford/80-cereals?resource=download

Content Fields in the dataset:

Name: Name of cereal mfr: Manufacturer of cereal A = American Home Food Products; G = General Mills K = Kelloggs N = Nabisco P = Post Q = Quaker Oats R = Ralston Purina type: cold hot calories: calories per serving protein: grams of protein fat: grams of fat sodium: milligrams of sodium fiber: grams of dietary fiber carbo: grams of complex carbohydrates sugars: grams of sugars potass: milligrams of potassium vitamins: vitamins and minerals - 0, 25, or 100, indicating the typical percentage of FDA recommended shelf: display shelf (1, 2, or 3, counting from the floor) weight: weight in ounces of one serving cups: number of cups in one serving rating: a rating of the cereals (Possibly from Consumer Reports?)

About

I used a cereal database to answer the question some basic nutritional questions. I used jupyter notebook, pandas for exploring data, seaborn and matplotlib for visualization. This was my first personal project to be added to my data analysis portfolio

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