Book Image

Python Machine Learning Cookbook

By : Prateek Joshi, Vahid Mirjalili
Book Image

Python Machine Learning Cookbook

By: Prateek Joshi, Vahid Mirjalili

Overview of this book

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We’ll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you’ll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You’ll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.
Table of Contents (19 chapters)
Python Machine Learning Cookbook
About the Author
About the Reviewer

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...