Python Machine Learning Blueprints - Second Edition

Predicting the rating for the product

Let's now take this framework and use it to predict the rating for a product. We'll limit our example to three users, person X, person Y, and person Z. We'll predict the rating of a product that person X hasn't rated, but that persons Y and Z, who are very similar to X, have rated.

We'll start with our base ratings for each user, as shown in the following table:

Customers	Snarky's Potato Chips	SoSo Smooth Lotion	Duffly Beer	BetterTap Water	XXLargeLivin' Football Jersey	Snowy Cotton Balls	Disposos' Diapers
X	4	3	4
Y	3.5	2.5	4	4
Z	4	3.5	4.5	4.5

Next, we'll center the ratings:

Customers

Snarky's Potato Chips

SoSo Smooth Lotion