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Aim: Performing data analysis and visualization on a small dataset,
80 Cereals. -
Aim: Getting insights and asking/answering questions.
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Aim: Familiarizing myself with seaborn (a statistical plotting library based on matplotlib)
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Tools used:
- Numpy
- Pandas
- Matplotlib
- Seaborn
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Analyzing the cereal products sold in a supermarket store is crucial for the store's success and profitability.
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By conducting a comprehensive analysis, the store can identify which cereal products are the most popular and profitable.
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This information can help the store to optimize its inventory, ensure that the most in-demand products are always in stock, and eliminate low-performing products.
- 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 (aka, percentage daily value)
- 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?)
The dataset can be found here.