This book presents some of the most advanced predictive analytics tools, models, and techniques. The main goal is to show the viewer how to improve the performance of predictive models, firstly, by showing how to build more complex models, and secondly by showing how to use related techniques that dramatically improve the quality of predictive models.

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/Mastering-Predictive-Analytics-with-scikit-learn-and-TensorFlow. 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!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781789617740_ColorImages.pdf.

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: "The following screenshot shows the lines of code used for importing the train_test_split function and the RobustScaler method."

A block of code is set as follows:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline

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: "The method used to choose the best estimators for a particular dataset or choosing the best values for all hyperparameters is called hyperparameter tuning."