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

Clustering data using the k-means algorithm

The k-means algorithm is one of the most popular clustering algorithms. This algorithm is used to divide the input data into k subgroups using various attributes of the data. Grouping is achieved using an optimization technique where we try to minimize the sum of squares of distances between the datapoints and the corresponding centroid of the cluster. If you need a quick refresher, you can learn more about k-means at

How to do it…

  1. The full code for this recipe is given in the file already provided to you. Let's look at how it's built. Create a new Python file, and import the following packages:

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import metrics
    from sklearn.cluster import KMeans
    import utilities
  2. Let's load the input data and define the number of clusters. We will use the data_multivar.txt file that's already provided to you:

    data = utilities.load_data...