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Python Machine Learning Cookbook

You're reading from   Python Machine Learning Cookbook 100 recipes that teach you how to perform various machine learning tasks in the real world

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781786464477
Length 304 pages
Edition 1st Edition
Languages
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Realm of Supervised Learning FREE CHAPTER 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Building Recommendation Engines 6. Analyzing Text Data 7. Speech Recognition 8. Dissecting Time Series and Sequential Data 9. Image Content Analysis 10. Biometric Face Recognition 11. Deep Neural Networks 12. Visualizing Data Index

Computing the Euclidean distance score


Now that we have sufficient background in machine learning pipelines and nearest neighbors classifier, let's start the discussion on recommendation engines. In order to build a recommendation engine, we need to define a similarity metric so that we can find users in the database who are similar to a given user. Euclidean distance score is one such metric that we can use to compute the distance between datapoints. We will focus the discussion towards movie recommendation engines. Let's see how to compute the Euclidean score between two users.

How to do it…

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

    import json
    import numpy as np
  2. We will now define a function to compute the Euclidean score between two users. The first step is to check whether the users are present in the database:

    # Returns the Euclidean distance score between user1 and user2 
    def euclidean_score(dataset, user1, user2):
        if user1 not in dataset:
            raise TypeError...
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