Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
A List of Links
Index

Best practices for coding


When one uses Python for predictive modelling, one needs to write small snippets of code. To ensure that one gets the maximum out of their code snippets and that the work is reproducible, one should be aware of and aspire to follow the best practices in coding. Some of the best practices for coding are as follows.

Commenting the codes

There is a tradeoff between the elegance and understandability of a code snippet. As a code snippet becomes more elegant, its understandability by a new user (other than the author of the snippet) decreases. Some of the users are interested only in the end results, but most of the users like to understand what is going on behind the hood and want to have a good understanding of the code.

For the code snippet to be understandable by a new person or the user of the code, it is a common practice to comment on the important lines, if not all the lines, and write the headings for the major chunks of the code. Some of the properties of a comment...