Description
Multiple Linear Regression
1. An online retailer like Amazon wants to determine which products to promote based on
reviews. They only want to promote products that are likely to sell.
For each product, they
have past sales as well as reviews. The reviews have both a numeric score (from 1 to 5) and
text.
(a) To formulate this as a machine learning problem, suggest a target variable that the online
retailer could use.
(b) For the predictors of the target variable, a data scientist suggests to combine the numeric
score with frequency of occurrence of words that convey judgement like “bad”, “good”,
and “doesn’t work.” Describe a possible linear model for this relation.
(c) Now, suppose that some reviews have a numeric score from 1 to 5 and others have a score
from 1 to 10. How would change your features?
(d) Now suppose the reviews have either (a) a score from 1 to 5; (b) a rating that is simply
good or bad; or (c) no numeric rating at all. How would you change your features?
(e) For the frequency of occurrence of a word such as “good”, which variable would you
suggest to use as a predictor:
(a) total number of reviews with the word “good”; or (b)
fraction of reviews with the word “good”?
2. Suppose we are given dat
