OESON - Case Study

Objectives

1. Provide the learner some more practice for exploratory data analysis.

2. Equip the learner to fit and evaluate a linear regression model.

Questions

1. Load the data from "cereal.csv" and plot histograms of sugar and vitamin content accross different cereals. [Hint: Extract values of a specific column using their labels and use hist method of pyplot]

2. The names of the manufacturers are coded using alphabets, create a new column with their full name using the below mapping.

'N': 'Nabisco' 'Q': 'Quaker Oats' 'K': 'Kelloggs' 'R': 'Raslston Purina' 'G': 'General Mills' 'P': 'Post' 'A': 'American Home Foods Products'

Create a bar plot where each manufacturer is only on the y axis and the height of the bars depict the number of cereals manufactured by them. [Hint: Try using countplot this time or bar method of pyplot]

3. Extract the rating as your target variable 'y' and all numberical parameters as your predictors 'x'.

4. Fit a linear regression module.