Python Machine Learning Cookbook

Computing the Pearson correlation score

The Euclidean distance score is a good metric, but it has some shortcomings. Hence, Pearson correlation score is frequently used in recommendation engines. Let's see how to compute it.

How to do it…

1. Create a new Python file, and import the following packages:

   import json
   import numpy as np

2. We will define a function to compute the Pearson correlation score between two users in the database. Our first step is to confirm that these users exist in the database:

   # Returns the Pearson correlation score between user1 and user2 def pearson_score(dataset, user1, user2): if user1 not in dataset: raise TypeError('User ' + user1 + ' not present in the dataset') if user2 not in dataset: raise TypeError('User ' + user2 + ' ...