To get the most out of this book

To get the most out of this book:

• Prior knowledge of Python is assumed at a basic level.
• Jupyter Notebook as a development environment is preferred but not necessary.

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 and 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/Machine-Learning-with-scikit-learn-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!

Code in action

Visit the following link to check out videos of the code being run:
http://bit.ly/2OcWIGH

Conventions used

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

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

from sklearn.naive_bayes import GaussianNB

#Initializing an NB classifier

nb_classifier = GaussianNB()

#Fitting the classifier into the training data

nb_classifier.fit(X_train, y_train)

#Extracting the accuracy score from the NB classifier

nb_classifier.score(X_test, y_test)

Warnings or important notes appear like this.

Tips and tricks appear like this.