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Description

This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.

Who is this book for?

What you will learn

Master the steps involved in the predictive modeling process

Learn how to classify predictive models and distinguish which models are suitable for a particular problem

Understand how and why each predictive model works

Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem

Select appropriate metrics to assess the performance of different types of predictive model

Diagnose performance and accuracy problems when they arise and learn how to deal with them

Grow your expertise in using R and its diverse range of packages

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

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Amazon verified reviews

Amazon Customer Jun 09, 2017

well , I like this book . It teach me the basic of modelling as I am shifting from simple programmer to machine learning

Amazon Verified review

PCTriangle Oct 23, 2015

I received this book a few days ago and I'm only through 3 chapters but I'm already very impressed with the clarity of the real-world examples it contains. You should know some basic statistics to help you understand various concepts. If you are into seeing mathematical proofs behind algorithms, this book is not for you. However, it gives you enough information to know how to pre-process the data (cleansing, transformations) and items you make assumptions behind analyses. The R codes are well documented and the outputs are well explained. It is one of the best hands-on and concrete PA + R books I've seen.

MomOfTwo Apr 24, 2016

I never thought I'd feel so strongly for an academic textbook, but I do. I am currently working on my master's degree at the Harvard Extension School and the assigned book (STAT!) is unnecessarily. I bought the kindle version to help me through the chapters of multiple regression and logistic regression - it was a breath of fresh air. The author has done an excellent job keeping it simple and helping you understand pretty tough concepts. Can't recommend it enough - Kudos.

AS Aug 20, 2015

This is a practical application of R and the exercises give you a good sense of the different methods to leverage are with visualizations that extend the predictive capabilities of R. I would highly recommend this book if you are taking a course that leverages R and your textbook does not offer an introduction to the capabilities of R with predictive analytics.

Riadh Aug 17, 2015

I do not agree with the book title which sounds as intended for people of intermediate level who want to move forward to master predictive analytics field. A beginner can absolutely find a great material to take advantage of this book to reach indeed an advanced analytics level, even if prior experience in R programming language can be very helpful but not essential. The author treated a huge number of subject in this ~400 pages book which starts from the fundamental principles of predictive analytics (including the typical project life cycle) to advanced methods (neural networks, support vector machines, ensemble methods, probabilistic graphical models …) and subjects (time series analysis, topic modeling, recommendation systems…). The most valuable point of this book is that the main methods and models from the simplest to the most advanced ones are treated very well, the book is balanced and all the covered subjects include numerical examples with perfectly clear and working code resulting in a much larger number of cases studied than the common books treating the same subject ... really much more! One lowlight that I note in this book is the absence of “deep learning” algorithm to complete this important collection of methods especially with the excellent H20 framework from 0xdata, but this is not very weighty as the subject is rather complicated and there is absolutely no R book treating this until today… So 5 stars well deserved!

Mastering Predictive Analytics with R: Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

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