To get the most out of this book

To get the most out of this book, these are the prerequisites:

Fluency in Python programming
Knowledge of basic statistical concepts

Knowledge of Python's data science stack is an advantage but is not essential. We will also be using Python 3.6 and many of the main analytical libraries. The easiest way to get them is by installing the Anaconda distribution. This is not required, but it will make your life easier. Go to https://www.anaconda.com/download/ to learn more about this software.

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:

Log in or register at www.packt.com.
Select the SUPPORT tab.
Click on Code Downloads & Errata.
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/Hands-On-Predictive-Analytics-with-Python. In case there's an update to the code, it will be updated https://github.com/PacktPublishing 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: http://www.packtpub.com/sites/default/files/downloads/9781789138719_ColorImages.pdf.

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:

carat_values = np.arange(0.5, 5.5, 0.5)
preds = first_ml_model(carat_values)
pd.DataFrame({"Carat": carat_values, "Predicted price":preds})

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

numerator = ((ccd['default']==1) & (ccd['male']==1)).sum()/N
denominator = Prob_B
Prob_A_given_B = numerator/denominator
print("P(A|B) = {:0.4f}".format(Prob_A_given_B))

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

dim_features.corr()

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.