Ashish Kumar - Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python
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- Book:Learning predictive analytics with Python : gain practical insights into predictive modelling by implementing predictive analytics algorithms on public datasets with Python
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Copyright 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: February 2016
Production reference: 1050216
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78398-326-1
www.packtpub.com
Author
Ashish Kumar
Reviewer
Matt Hollingsworth
Commissioning Editor
Kartikey Pandey
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Cover Work
Shantanu N. Zagade
Data science is changing the way we go about our daily lives at an unprecedented pace. The recommendations you see on e-commerce websites, the technologies that prevent credit card fraud, the logic behind airline itinerary and route selections, the products and discounts you see in retail stores, and many more decisions are largely powered by data science. Futuristic sounding applications like self-driving cars, robots to do household chores, smart wearable technologies, and so on are becoming a reality, thanks to innovations in data science.
Predictive analytics is a branch of data science, used to predict unknown future events based on historical data. It uses a number of techniques from data mining, statistical modelling and machine learning to help make forecasts with an acceptable level of reliability.
Python is a high-level, object-oriented programming language. It has gained popularity because of its clear syntax and readability, and beginners can pick up the language easily. It comes with a large library of modules that can be used to do a multitude of tasks ranging from data cleaning to building complex predictive modelling algorithms.
I'm a co-founder at Tiger Analytics, a firm specializing in providing data science and predictive analytics solutions to businesses. Over the last decade, I have worked with clients at numerous Fortune 100 companies and start-ups alike, and architected a variety of data science solution frameworks. Ashish Kumar, the author of this book, is currently a budding data scientist at our company. He has worked on several predictive analytics engagements, and understands how businesses are using data to bring in scientific decision making to their organizations. Being a young practitioner, Ashish relates to someone who wants to learn predictive analytics from scratch. This is clearly reflected in the way he presents several concepts in the book.
Whether you are a beginner in data science looking to build a career in this area, or a weekend enthusiast curious to explore predictive analytics in a hands-on manner, you will need to start from the basics and get a good handle on the building blocks. This book helps you take the first steps in this brave new world; it teaches you how to use and implement predictive modelling algorithms using Python. The book does not assume prior knowledge in analytics or programming. It differentiates itself from other such programming cookbooks as it uses publicly available datasets that closely represent data encountered in business scenarios, and walks you through the analysis steps in a clear manner.
There are nine chapters in the book. The first few chapters focus on data exploration and cleaning. It is written keeping beginners to programming in mindby explaining different data structures and then going deeper into various methods of data processing and cleaning. Subsequent chapters cover the popular predictive modelling algorithms like linear regression, logistic regression, clustering, decision trees, and so on. Each chapter broadly covers four aspects of the particular modelmath behind the model, different types of the model, implementing the model in Python, and interpreting the results.
Statistics/math involved in the model is clearly explained. Understanding this helps one implement the model in any other programming language. The book also teaches you how to interpret the results from the predictive model and suggests different techniques to fine tune the model for better results. Wherever required, the author compares two different models and explains the benefits of each of the models. It will help a data scientist narrow down to the right algorithm that can be used to solve a specific problem. In addition, this book exposes the readers to various Python libraries and guides them with the best practices while handling different datasets in Python.
I am confident that this book will guide you to implement predictive modelling algorithms using Python and prepare you to work on challenging business problems involving data. I wish this book and its author Ashish Kumar every success.
Pradeep Gulipalli
Co-founder and Head of India Operations - Tiger Analytics
Ashish Kumar has a B. Tech from IIT Madras and is a Young India Fellow from the batch of 2012-13. He is a data science enthusiast with extensive work experience in the field. As a part of his work experience, he has worked with tools, such as Python, R, and SAS. He has also implemented predictive algorithms to glean actionable insights for clients from transport and logistics, online payment, and healthcare industries. Apart from the data sciences, he is enthused by and adept at financial modelling and operational research. He is a prolific writer and has authored several online articles and short stories apart from running his own analytics blog. He also works pro-bono for a couple of social enterprises and freelances his data science skills.
He can be contacted on LinkedIn at https://goo.gl/yqrfo4, and on Twitter at https://twitter.com/asis64.
I dedicate this book to my beloved grandfather who is the prime reason behind whatever I am today. He is my source of inspiration and he is the one I want to be like. Not a single line of this book was written without thinking about him; may you stay strong and healthy.
I want to acknowledge the support of my family, especially my parents and siblings. My conversations with them were the power source, which kept me going.
I want to acknowledge the guidance and support of my friends for insisting that I should do this when I was skeptical about taking this up. I would like to thank Ajit and Pranav for being the best friends one could ask for and always being there for me. A special mention to Vijayaraghavan for lending his garden for me to work in and relax post the long writing sessions. I would like to thank my college friends, especially my wing mates, Zenithers, who have always been pillars of support. My friends at the Young India Fellowship have made me evolve as a person and I am grateful to all of them.
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