Suppose you have the following data set, which is a list of 80 cereals that contains the following fields:
- 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 per serving
- fat: grams of fat per serving
- sodium: milligrams of sodium
- fiber: grams of dietary fiber
- carbs: 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?)
Given the above, can you build a model using Python to predict cereal rating?