To get the most out of this book

You should have basic understanding of the mathematical principles taught in American primary and high schools. The most complex math required is the understanding of the contents of a matrix and the relation implied by the sigma (sum) symbol. You should have some rudimentary knowledge of Python, including lists, dictionaries, and functions. If you feel deficient in any of these prerequisites, a quick internet search to brush up on the concepts prior to reading should get you ready quickly.

This book is meant as a beginner's text, so the most important prerequisite is an open mind and the drive to learn.

Download the example code files

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for Mac
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Python-Data-Mining-Quick-Start-Guide. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this
book. You can download it here: https:/​/​www.​packtpub.​com/​sites/​default/​files/
downloads/9781789800265_​ColorImages.​pdf.

Conventions used

There are a number of text conventions used throughout this book:

A block of code is set as follows, with # used for comment lines:

from sklearn.cluster import Method
clus = Method(args*)
# fit to input data
clus.fit(X_input)
# get cluster assignments of X_input
X_assigned = clus.labels_

Any command-line input or output is written as follows:

(base) $ spyder 

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Select System info from the Administration panel."

Warnings or important notes appear like this.

Tips and tricks appear like this.