Search icon Close icon
Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Checkout

Change country

Modal Close icon
Country selected Country selected
country flag United States
Country selected
country flag Great Britain
Country selected
country flag India
Country selected
country flag Germany
Country selected
country flag France
Country selected
country flag Canada
Country selected
country flag Russia
Country selected
country flag Spain
Country selected
country flag Brazil
Country selected
country flag Australia
Country selected
country flag Singapore
Country selected
country flag Hungary
Country selected
country flag Ukraine
Country selected
country flag Luxembourg
Country selected
country flag Estonia
Country selected
country flag Lithuania
Country selected
country flag South Korea
Country selected
country flag Turkey
Country selected
country flag Switzerland
Country selected
country flag Colombia
Country selected
country flag Taiwan
Country selected
country flag Chile
Country selected
country flag Norway
Country selected
country flag Ecuador
Country selected
country flag Indonesia
Country selected
country flag New Zealand
Country selected
country flag Cyprus
Country selected
country flag Denmark
Country selected
country flag Finland
Country selected
country flag Poland
Country selected
country flag Malta
Country selected
country flag Czechia
Country selected
country flag Austria
Country selected
country flag Sweden
Country selected
country flag Italy
Country selected
country flag Egypt
Country selected
country flag Belgium
Country selected
country flag Portugal
Country selected
country flag Slovenia
Country selected
country flag Ireland
Country selected
country flag Romania
Country selected
country flag Greece
Country selected
country flag Argentina
Country selected
country flag Netherlands
Country selected
country flag Bulgaria
Country selected
country flag Latvia
Country selected
country flag South Africa
Country selected
country flag Malaysia
Country selected
country flag Japan
Country selected
country flag Slovakia
Country selected
country flag Philippines
Country selected
country flag Mexico
Country selected
country flag Thailand
Country selected
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Home > Data > Machine Learning > Machine Learning with R Cookbook, Second Edition
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

Arrow left icon
Profile Icon Yu-Wei, Chiu (David Chiu) Profile Icon Yu-Wei, Chiu (David Chiu)
Arrow right icon
$54.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Oct 2017 572 pages 2nd Edition
eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Yu-Wei, Chiu (David Chiu) Profile Icon Yu-Wei, Chiu (David Chiu)
Arrow right icon
$54.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
Paperback Oct 2017 572 pages 2nd Edition
eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Table of content icon View table of contents Preview book icon Preview Book

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

Page 1 of 8

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
Estimated delivery fee Deliver to Egypt

Standard delivery 10 - 13 business days

$12.95

Premium delivery 3 - 6 business days

$34.95
(Includes tracking information)

Product Details

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 23, 2017
Length: 572 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787284395
Category :
Data
Languages :
R
Concepts :
Machine Learning
Tools :
Hadoop

What do you get with Print?

Product feature icon Instant access to your digital copy whilst your Print order is Shipped
Product feature icon Paperback book shipped to your preferred address
Product feature icon Redeem a companion digital copy on all Print orders
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
OR

Contact Details

Modal Close icon
Payment Processing...
tick Completed

Shipping Address

Billing Address

Shipping Methods
Estimated delivery fee Deliver to Egypt

Standard delivery 10 - 13 business days

$12.95

Premium delivery 3 - 6 business days

$34.95
(Includes tracking information)

Product Details

Publication date : Oct 23, 2017
Length: 572 pages
Edition : 2nd
Language : English
ISBN-13 : 9781787284395
Category :
Data
Languages :
R
Concepts :
Machine Learning
Tools :
Hadoop

Packt Subscriptions

See our plans and pricing
Modal Close icon
$19.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
START FREE TRIAL
BUY NOW
$199.99 billed annually
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW
$279.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 7,000+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just $5 each
Feature tick icon Exclusive print discounts
START FREE TRIAL
BUY NOW

Frequently bought together

Python Machine Learning, Second Edition
Python Machine Learning, Second Edition
Read more
Sep 2017 622 pages
Full star icon 4.3 (89)
eBook
eBook
  • eBook eBook $24.99
  • Paperback Paperback $43.99
$24.99 $35.99
$43.99
Machine Learning with R Cookbook, Second Edition
Machine Learning with R Cookbook, Second Edition
Read more
Oct 2017 572 pages
Full star icon 2 (1)
eBook
eBook
  • eBook eBook $29.99
  • Paperback Paperback $54.99
$29.99 $43.99
$54.99
Mastering Machine Learning with R, Second Edition
Mastering Machine Learning with R, Second Edition
Read more
Apr 2017 420 pages
Full star icon 2.8 (4)
eBook
eBook
  • eBook eBook $29.99
  • Paperback Paperback $54.99
$29.99 $43.99
$54.99
Stars icon
Total $ 153.97
Python Machine Learning, Second Edition
$43.99
Machine Learning with R Cookbook, Second Edition
$54.99
Mastering Machine Learning with R, Second Edition
$54.99
Total $ 153.97 Stars icon

Table of Contents

14 Chapters
Practical Machine Learning with R Chevron down icon Chevron up icon
Practical Machine Learning with R
Introduction
Downloading and installing R
Downloading and installing RStudio
Installing and loading packages
Understanding of basic data structures
Basic commands for subsetting
Reading and writing data
Manipulating data
Applying basic statistics
Visualizing data
Getting a dataset for machine learning
Data Exploration with Air Quality Datasets Chevron down icon Chevron up icon
Data Exploration with Air Quality Datasets
Introduction
Using air quality dataset
Converting attributes to factor
Detecting missing values
Imputing missing values
Exploring and visualizing data
Predicting values from datasets
Analyzing Time Series Data Chevron down icon Chevron up icon
Analyzing Time Series Data
Introduction
Looking at time series data
Plotting and forecasting time series data
Extracting, subsetting, merging, filling, and padding
Successive differences and moving averages
Exponential smoothing
Plotting the autocorrelation function
R and Statistics Chevron down icon Chevron up icon
R and Statistics
Introduction
Understanding data sampling in R
Operating a probability distribution in R
Working with univariate descriptive statistics in R
Performing correlations and multivariate analysis
Conducting an exact binomial test
Performing a student's t-test
Performing the Kolmogorov-Smirnov test
Understanding the Wilcoxon Rank Sum and Signed Rank test
Working with Pearson's Chi-squared test
Conducting a one-way ANOVA
Performing a two-way ANOVA
Understanding Regression Analysis Chevron down icon Chevron up icon
Understanding Regression Analysis
Introduction
Different types of regression
Fitting a linear regression model with lm
Summarizing linear model fits
Using linear regression to predict unknown values
Generating a diagnostic plot of a fitted model
Fitting multiple regression
Summarizing multiple regression
Using multiple regression to predict unknown values
Fitting a polynomial regression model with lm
Fitting a robust linear regression model with rlm
Studying a case of linear regression on SLID data
Applying the Gaussian model for generalized linear regression
Applying the Poisson model for generalized linear regression
Applying the Binomial model for generalized linear regression
Fitting a generalized additive model to data
Visualizing a generalized additive model
Diagnosing a generalized additive model
Survival Analysis Chevron down icon Chevron up icon
Survival Analysis
Introduction
Loading and observing data
Viewing the summary of survival analysis
Visualizing the Survival Curve
Using the log-rank test
Using the COX proportional hazard model
Nelson-Aalen Estimator of cumulative hazard
Classification 1 - Tree, Lazy, and Probabilistic Chevron down icon Chevron up icon
Classification 1 - Tree, Lazy, and Probabilistic
Introduction
Preparing the training and testing datasets
Building a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring the prediction performance of a recursive partitioning tree
Pruning a recursive partitioning tree
Handling missing data and split and surrogate variables
Building a classification model with a conditional inference tree
Control parameters in conditional inference trees
Visualizing a conditional inference tree
Measuring the prediction performance of a conditional inference tree
Classifying data with the k-nearest neighbor classifier
Classifying data with logistic regression
Classifying data with the Naïve Bayes classifier
Classification 2 - Neural Network and SVM Chevron down icon Chevron up icon
Classification 2 - Neural Network and SVM
Introduction
Classifying data with a support vector machine
Choosing the cost of a support vector machine
Visualizing an SVM fit
Predicting labels based on a model trained by a support vector machine
Tuning a support vector machine
The basics of neural network
Training a neural network with neuralnet
Visualizing a neural network trained by neuralnet
Predicting labels based on a model trained by neuralnet
Training a neural network with nnet
Predicting labels based on a model trained by nnet
Model Evaluation Chevron down icon Chevron up icon
Model Evaluation
Introduction
Estimating model performance with k-fold cross-validation
Estimating model performance with Leave One Out Cross Validation
Performing cross-validation with the e1071 package
Performing cross-validation with the caret package
Ranking the variable importance with the caret package
Ranking the variable importance with the rminer package
Finding highly correlated features with the caret package
Selecting features using the caret package
Measuring the performance of the regression model
Measuring prediction performance with a confusion matrix
Measuring prediction performance using ROCR
Comparing an ROC curve using the caret package
Measuring performance differences between models with the caret package
Ensemble Learning Chevron down icon Chevron up icon
Ensemble Learning
Introduction
Using the Super Learner algorithm
Using ensemble to train and test
Classifying data with the bagging method
Performing cross-validation with the bagging method
Classifying data with the boosting method
Performing cross-validation with the boosting method
Classifying data with gradient boosting
Calculating the margins of a classifier
Calculating the error evolution of the ensemble method
Classifying data with random forest
Estimating the prediction errors of different classifiers
Clustering Chevron down icon Chevron up icon
Clustering
Introduction
Clustering data with hierarchical clustering
Cutting trees into clusters
Clustering data with the k-means method
Drawing a bivariate cluster plot
Comparing clustering methods
Extracting silhouette information from clustering
Obtaining the optimum number of clusters for k-means
Clustering data with the density-based method
Clustering data with the model-based method
Visualizing a dissimilarity matrix
Validating clusters externally
Association Analysis and Sequence Mining Chevron down icon Chevron up icon
Association Analysis and Sequence Mining
Introduction
Transforming data into transactions
Displaying transactions and associations
Mining associations with the Apriori rule
Pruning redundant rules
Visualizing association rules
Mining frequent itemsets with Eclat
Creating transactions with temporal information
Mining frequent sequential patterns with cSPADE
Using the TraMineR package for sequence analysis
Visualizing sequence, Chronogram, and Traversal Statistics
Dimension Reduction Chevron down icon Chevron up icon
Dimension Reduction
Introduction
Why to reduce the dimension?
Performing feature selection with FSelector
Performing dimension reduction with PCA
Determining the number of principal components using the scree test
Determining the number of principal components using the Kaiser method
Visualizing multivariate data using biplot
Performing dimension reduction with MDS
Reducing dimensions with SVD
Compressing images with SVD
Performing nonlinear dimension reduction with ISOMAP
Performing nonlinear dimension reduction with Local Linear Embedding
Big Data Analysis (R and Hadoop) Chevron down icon Chevron up icon
Big Data Analysis (R and Hadoop)
Introduction
Preparing the RHadoop environment
Installing rmr2
Installing rhdfs
Operating HDFS with rhdfs
Implementing a word count problem with RHadoop
Comparing the performance between an R MapReduce program and a standard R program
Testing and debugging the rmr2 program
Installing plyrmr
Manipulating data with plyrmr
Conducting machine learning with RHadoop
Configuring RHadoop clusters on Amazon EMR

Recommendations for you

Left arrow icon
LLM Engineer's Handbook
LLM Engineer's Handbook
Read more
Oct 2024 522 pages
Full star icon 4.8 (25)
eBook
eBook
  • eBook eBook $47.99
  • Paperback Paperback $59.99
$47.99
$59.99
Getting Started with Tableau 2018.x
Getting Started with Tableau 2018.x
Read more
Sep 2018 396 pages
Full star icon 4 (3)
eBook
eBook
  • eBook eBook $29.99
  • Paperback Paperback $54.99
$29.99 $43.99
$54.99
Semantic Kernel SDK for Intelligent Applications
Semantic Kernel SDK for Intelligent Applications
Read more
Dec 2024 2hrs 9mins
Video
Video
  • Video Video $109.99
$109.99
AI Ecosystem for the Absolute Beginners - Hands-On
AI Ecosystem for the Absolute Beginners - Hands-On
Read more
Dec 2024 4hrs 56mins
Full star icon 2 (1)
Video
Video
  • Video Video $108.99
$108.99
Python for Algorithmic Trading Cookbook
Python for Algorithmic Trading Cookbook
Read more
Aug 2024 404 pages
Full star icon 4.4 (18)
eBook
eBook
  • eBook eBook $32.99
  • Paperback Paperback $59.99
$32.99 $47.99
$59.99
RAG-Driven Generative AI
RAG-Driven Generative AI
Read more
Sep 2024 334 pages
Full star icon 4.3 (18)
eBook
eBook
  • eBook eBook $24.99
  • Paperback Paperback $43.99
$24.99 $35.99
$43.99
Machine Learning with PyTorch and Scikit-Learn
Machine Learning with PyTorch and Scikit-Learn
Read more
Feb 2022 774 pages
Full star icon 4.4 (95)
eBook
eBook
  • eBook eBook $29.99
  • Paperback Paperback $54.99
$29.99 $43.99
$54.99
Building LLM Powered Applications
Building LLM Powered Applications
Read more
May 2024 342 pages
Full star icon 4.2 (22)
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Python Machine Learning By Example
Python Machine Learning By Example
Read more
Jul 2024 518 pages
Full star icon 4.9 (9)
eBook
eBook
  • eBook eBook $24.99
  • Paperback Paperback $45.99
$24.99 $36.99
$45.99
AI Product Manager's Handbook
AI Product Manager's Handbook
Read more
Nov 2024 484 pages
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Right arrow icon

Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(1 Ratings)
5 star 0%
4 star 0%
3 star 0%
2 star 100%
1 star 0%
Chris H Mar 23, 2018
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
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 Amazon

People who bought this also bought

Left arrow icon
Causal Inference and Discovery in Python
Causal Inference and Discovery in Python
Read more
May 2023 456 pages
Full star icon 4.5 (49)
eBook
eBook
  • eBook eBook $21.99
  • Paperback Paperback $39.99
$21.99 $31.99
$39.99
Generative AI with LangChain
Generative AI with LangChain
Read more
Dec 2023 368 pages
Full star icon 4 (34)
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Modern Generative AI with ChatGPT and OpenAI Models
Modern Generative AI with ChatGPT and OpenAI Models
Read more
May 2023 286 pages
Full star icon 4.2 (35)
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Deep Learning with TensorFlow and Keras – 3rd edition
Deep Learning with TensorFlow and Keras – 3rd edition
Read more
Oct 2022 698 pages
Full star icon 4.6 (45)
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Machine Learning Engineering with Python
Machine Learning Engineering with Python
Read more
Aug 2023 462 pages
Full star icon 4.6 (37)
eBook
eBook
  • eBook eBook $27.98
  • Paperback Paperback $49.99
$27.98 $39.99
$49.99
Right arrow icon

About the authors

Left arrow icon
Profile icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.
Read more
See other products by Yu-Wei, Chiu (David Chiu)
Profile icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.
Read more
See other products by Yu-Wei, Chiu (David Chiu)
Right arrow icon
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is the digital copy I get with my Print order? Chevron down icon Chevron up icon

When you buy any Print edition of our Books, you can redeem (for free) the eBook edition of the Print Book you’ve purchased. This gives you instant access to your book when you make an order via PDF, EPUB or our online Reader experience.

What is the delivery time and cost of print book? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela