What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Key benefits

A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices

Get to grips with the basics of Predictive Analytics with Python

Learn how to use the popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering

Description

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.

Who is this book for?

If you wish to learn how to implement Predictive Analytics algorithms using Python libraries, then this is the book for you. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about Predictive Analytics algorithms, this book will also help you. The book will be beneficial to and can be read by any Data Science enthusiasts. Some familiarity with Python will be useful to get the most out of this book, but it is certainly not a prerequisite.

What you will learn

Understand the statistical and mathematical concepts behind Predictive Analytics algorithms and implement Predictive Analytics algorithms using Python libraries

Analyze the result parameters arising from the implementation of Predictive Analytics algorithms

Write Python modules/functions from scratch to execute segments or the whole of these algorithms

Recognize and mitigate various contingencies and issues related to the implementation of Predictive Analytics algorithms

Get to know various methods of importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and numpy

Create dummy datasets and simple mathematical simulations using the Python numpy and pandas libraries

Understand the best practices while handling datasets in Python and creating predictive models out of them

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Learning Predictive Analytics with Python

$54.99

$48.99

Designing Machine Learning Systems with Python

$48.99

Total $ 152.97

adnan baloch Mar 28, 2016

You don't have to be married to a physicist to appreciate the role of the team at CERN that confirmed the existence of the Higgs Boson. Who better to be a reviewer of this book than a member of that team? That fact itself should inspire confidence in the utility of this book. The author uses interesting analogies to explain the different aspects of predictive analytics and even goes so far as to present comparison tables, serving to drive home his points. The ease and power of the Python programming language is put to good use in explaining the process of data cleaning and wrangling. The better part of the first half of the book is dedicated to exploring the various aspects of these two critical processes with easy to follow examples and code. A whole chapter is devoted to laying out the statistical concepts that are integral to getting the most out of the remainder of the book. The latter part of the book details supervised and unsupervised predictive modelling algorithms, shows how to implement them in Python and furthermore, delves deep into the mathematics of these widely used algorithms so that readers become well equipped to tackle real world challenges of predictive analytics in ANY programming language of their choice. In my opinion, the author really succeeded in making the serious subject matter of this book sound cool and exciting.

Amazon Verified review

A. Zubarev Apr 18, 2016

In my view Learning Predictive Analytics with Python is one of the most successful publications on such a difficult to initially grasp subject as Machine Learning. Yes, despite the name of the book does not imply so, it is in fact a gentle submersion into the Machine Learning, a so highly praised Data Science topic. Luckily, learning it would be much easier with Learning Predictive Analytics with Python from such a talented author. It is the most exciting yet easy to follow, logical and at the same time entertaining material I ever read so far. Tasteful, relevant examples, based on free software and datasets anyone can obtain. And the book also has several gems, these are the coverage of the ID3 algorithm (based on my observation looks like totally omitted in the most modern literature, but undeservedly), building various regressions and testing your model. One small advice to the reader: get familiarized yourself with iPython, and perhaps read some theory on statistics, not really necessary, but if you are going to apply the newly acquired knowledge at work or study then it could be a great deal of steering you into the right direction.

Amazon Verified review

Julian Cook Mar 13, 2016

If you are familiar with Packt (the publisher), you will know that they tend to carpet bomb particular areas, with multiple overlapping titles. This makes it difficult to recommend just one title if anyone asks you, since different books have different strengths.The strength of this book is that the author really does explain how to use PANDAS (python data analysis library) and statistical analysis from the ground up. Most pandas users will be familiar with pd.read_csv, but he covered a lot of options that I had never really understood properly, because I chiefly learnt from examples that don't really give you the 'why' of things.You might say, why not read the original book by Wes McKinney? I would have to say that this is a much more interesting read and has better flow. The Wes McKinney book sometimes reads like documentation and you are not sure what to really focus on.The coverage of statistical learning is also good, for instance he does a nice explanation of logistic regression and the underlying methodology with just enough math to properly explain the distinction between linear regression and logistic regression.I think the book is thorough enough that you could actually use it as a coursebook for statistical learning w/python, which a high praise for a book with a fairly generic title.

Amazon Verified review

a reader Sep 26, 2020

This is a good book. I do not understand why there are bad reviews for it. I would like to thank the author for the good job! Well done! Unfortunately, the author deleted the datasets the book uses from the Google drive.

Amazon Verified review

Jeremie Oct 04, 2017

Book deserves three to four stars max. It is ok and interesting. It is introduces a lot of concepts but shame it doesn't go a little bit more into details especially in the end of the book when talking about clustering and regression. It is one thing to talk about clustering but there is nothing about what to do with it once it is done.there isnt much discussion about regression tree and random forest algorithms which deserve more such as for example what can one do to improve the algos if thstbdont work well or what other algos are available.perhaps simply the book needs to advise on further reading

Amazon Verified review

Learning Predictive Analytics with Python: Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

What do you get with eBook?