Machine learning with R cookbook : analyze data and build predictive models / AshishSingh Bhatia, Yu-Wei Chiu.

Format:

Book

Author/Creator:

Bhatia, AshishSingh, author.

Chiu, Yu-Wei, author.

Language:

English

Subjects (All):

R (Computer program language).

Mathematical statistics--Data processing.

Mathematical statistics.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

Second edition.

Other Title:

Analyze data and build predictive models

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Summary:

Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book 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. Who This Book Is 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 In Detail 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. Yo...

Contents:

Cover

Copyright

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Practical Machine Learning with R

Introduction

Downloading and installing R

Getting ready

How to do it...

How it works...

See also

Downloading and installing RStudio

Installing and loading packages

Understanding of basic data structures

Data types

Data structures

Vectors

Lists

Array

Matrix

DataFrame

Basic commands for subsetting

Data input

Reading and writing data

There's more...

Manipulating data

Applying basic statistics

Visualizing data

Getting a dataset for machine learning

Chapter 2: Data Exploration with Air Quality Datasets

Using air quality dataset

Converting attributes to factor

Detecting missing values

Imputing missing values

Exploring and visualizing data

Getting ready.

How to do it...

Predicting values from datasets

Chapter 3: Analyzing Time Series Data

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

Chapter 4: R and Statistics

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

How it works.

There's more...

Conducting a one-way ANOVA

Performing a two-way ANOVA

Chapter 5: Understanding Regression Analysis

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

See also.

Fitting a generalized additive model to data

Visualizing a generalized additive model

Diagnosing a generalized additive model

Chapter 6: Survival Analysis

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

Chapter 7: Classification 1 - Tree, Lazy, and Probabilistic

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.

Getting ready

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

Chapter 8: Classification 2 - Neural Network and SVM

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.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed December 2, 2017).

OCLC:

1011524936