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

Finding similar users in the dataset

One of the most important tasks in building a recommendation engine is finding users that are similar. This guides in creating the recommendations that will be provided to these users. Let's see how to build this.

How to do it…

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

    import json
    import numpy as np
    from pearson_score import pearson_score
  2. Let's define a function to find similar users to the input user. It takes three input arguments: the database, input user, and the number of similar users that we are looking for. Our first step is to check whether the user is present in the database. If the user exists, we need to compute the Pearson correlation score between this user and all the other users in the database:

    # Finds a specified number of users who are similar to the input user
    def find_similar_users(dataset, user, num_users):
        if user not in dataset:
            raise TypeError('User ' + user + ' not present in the dataset')
        # Compute...