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Key benefits

Apply R to simplify predictive modeling with short and simple code

Use machine learning to solve problems ranging from small to big data

Build a training and testing dataset, applying different classification methods.

Description

Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier.

Who is this book for?

This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful.

What you will learn

Create and inspect transaction datasets and perform association analysis with the Apriori algorithm

Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm

Compare differences between each regression method to discover how they solve problems

Detect and impute missing values in air quality data

Predict possible churn users with the classification approach

Plot the autocorrelation function with time series analysis

Use the Cox proportional hazards model for survival analysis

Implement the clustering method to segment customer data

Compress images with the dimension reduction method

Incorporate R and Hadoop to solve machine learning problems on big data

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

$43.99

Machine Learning with R Cookbook, Second Edition

$54.99

Mastering Machine Learning with R, Second Edition

$54.99

Total $ 153.97

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Chris H Mar 23, 2018

With better editing and some more meat in the discussion of techniques this could be a useful resource. As such it still may be one for some readers, but the errors and poor editing in general make it hard to recommend this over other similar books. For example, the one sample t-test incorrectly states the null hypothesis (as the sample mean being less than the population mean, which is actually the correct alternative hypothesis) and therefore comes to the wrong conclusion. It is one thing to have a bad typo or omission, but to get the main point backwards in a discussion of the statistical test? How this could make it into a 2nd edition may be hard to fathom, but Packt books tend to be plagued by such poor editing. (However, I do recommend R learners look at the Packt titles by Fischetti, Lantz, Cirillo. There are some good ones.)

Amazon Verified review

Machine Learning with R Cookbook, Second Edition: Analyze data and build predictive models , Second Edition

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