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Machine Learning with R Cookbook, Second Edition
Machine Learning with R Cookbook, Second Edition

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

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Profile Icon Yu-Wei, Chiu (David Chiu) Profile Icon Yu-Wei, Chiu (David Chiu)
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Paperback Oct 2017 572 pages 2nd Edition
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Profile Icon Yu-Wei, Chiu (David Chiu) Profile Icon Yu-Wei, Chiu (David Chiu)
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Paperback Oct 2017 572 pages 2nd Edition
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Paperback
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Machine Learning with R Cookbook, Second Edition

Data Exploration with Air Quality Datasets

In this chapter, we will cover the following recipes:

  • Accessing air quality datasets in R
  • Converting attributes to factors
  • Detecting missing values
  • Imputing missing values
  • Exploring and visualizing data
  • Predicting temperature using available data

Introduction

As a data consumer, the focus is always on searching for right information to form a true analysis from the information gathered over time. Data Exploration is a term used for finding insightful information from data. To find insights from data various steps such as data munging, data analysis, data modeling, and model evaluation are taken.

In any real data exploration project, commonly six steps are involved in the exploration process. They are as follows:

  • Asking the right questions: Asking the right questions will help in understanding the objective and target information sought from the data. Questions can be asked such as What are my expected findings after the exploration is finished?, or What kind of information can I extract through the exploration?.
  • Data collection: Once the right questions have been asked the target of exploration is cleared. Data collected...

Using air quality dataset

Air quality datasets come bundled with R. They contain data about the New York Air Quality Measurements of 1973 for five months from May to September recorded daily. To view all the available datasets use the data() function, it will display all the datasets available with R installation.

Getting ready

In this recipe, we will use air quality datasets, which are available with R.

How to do it...

Perform the following step to see all the datasets in R and using airquality:

> data()
> str(airquality)
Output
'data.frame': 153 obs. of...

Converting attributes to factor

In R, since nominal, ordinal, interval, and ratio variable are treated differently in statistical modeling, we have to convert a nominal variable from a character or int into a factor.

Getting ready

You need to have completed the previous recipe, that is, viewing the airquality dataset using the str and head commands, to get an idea about what it contains.

How to do it...

Perform the following steps to convert the types of character variables:

  1. Assign the airquality dataset to mydata:
        > mydata <- airquality  
  1. To transform...

Detecting missing values

Missing values reduce the representativeness of the sample, and furthermore, might distort inferences about the population. This recipe will focus on detecting missing values within the airquality dataset.

Getting ready

You need to have completed the previous recipes, which involve converting Month into a factor type.

In R, a missing value is noted with the symbol NA (not available), and an impossible value is NaN (not a number).

How to do it...

Perform the following steps to detect the missing value:

  1. The is.na function is used to denote which...

Imputing missing values

After detecting the number of missing values within each attribute, we have to impute them since they might have a significant effect on the conclusions that can be drawn from the data.

Getting ready

This recipe will require mydata to be loaded in the R session and have the previous recipe completed.

How to do it...

Perform the following steps to impute the missing values:

  1. List the Ozone attribute:
        > mydata$Ozone
Output
[1] 41 36 12 18 NA 28 23 19 8 NA 7 16 11 14 18 14 34 6 30 11
[21] 1 11 4 32 NA NA NA 23 45 115 37...

Exploring and visualizing data

After imputing the missing values, one should perform an exploratory analysis, which involves using a visualization plot and an aggregation method to summarize the data characteristics. The result helps the user gain a better understanding of the data in use. The following recipe will introduce how to use basic plotting techniques with a view to help the user with exploratory analysis.

Getting ready

This recipe needs the previous recipe to be completed by imputing the missing value in the Ozone and Solar.R attribute.

How to do it...

Perform...

Predicting values from datasets

The exploratory analysis helps users gain insights into single or multiple variables. However, it does not determine what combinations may generate a prediction model, so as to predict the temperature. On the other hand, machine learning can generate a prediction model from a training dataset, so that the user can apply the model to predict the possible labels from the given attributes. In this recipe, we will introduce how to predict the temperature and find the correlation between attributes.

Getting ready

We will use the data, mydata, which we have already used in previous recipes.

How to do it...

Left arrow icon Right arrow icon
Download code icon Download Code

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

Product Details

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Publication date : Oct 23, 2017
Length: 572 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787284395
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Product Details

Publication date : Oct 23, 2017
Length: 572 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787284395
Category :
Languages :
Tools :

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Table of Contents

14 Chapters
Practical Machine Learning with R Chevron down icon Chevron up icon
Data Exploration with Air Quality Datasets Chevron down icon Chevron up icon
Analyzing Time Series Data Chevron down icon Chevron up icon
R and Statistics Chevron down icon Chevron up icon
Understanding Regression Analysis Chevron down icon Chevron up icon
Survival Analysis Chevron down icon Chevron up icon
Classification 1 - Tree, Lazy, and Probabilistic Chevron down icon Chevron up icon
Classification 2 - Neural Network and SVM Chevron down icon Chevron up icon
Model Evaluation Chevron down icon Chevron up icon
Ensemble Learning Chevron down icon Chevron up icon
Clustering Chevron down icon Chevron up icon
Association Analysis and Sequence Mining Chevron down icon Chevron up icon
Dimension Reduction Chevron down icon Chevron up icon
Big Data Analysis (R and Hadoop) Chevron down icon Chevron up icon

Customer reviews

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Chris H Mar 23, 2018
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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.)
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