Building a Recommendation System with R : Learn the Art of Building Robust and Powerful Recommendation Engines Using R

Packt Publishing, 2015. — 135 p. — ISBN: 1783554495, 9781783554492 Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

Learn the art of building robust and powerful recommendation engines using R

About This Book

• Learn to exploit various data mining techniques
• Understand some of the most popular recommendation techniques
• This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines

Who This Book Is For

If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.

What You Will Learn

• Get to grips with the most important branches of recommendation
• Understand various data processing and data mining techniques
• Evaluate and optimize the recommendation algorithms
• Prepare and structure the data before building models
• Discover different recommender systems along with their implementation in R
• Explore various evaluation techniques used in recommender systems
• Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems

In Detail

A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems.

The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system.

Style and approach

This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

•Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understanding •Leverage the flexibility and modularity of R to experiment with a range of different techniques and data types •Packed with practical advice and tips explaining important concepts and best practices to help you understand quickly and easily Who This Book Is For 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. 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 Key FeaturesBook DescriptionWhat you will learnMaster the steps involved in the predictive modeling processLearn how to classify predictive models and distinguish which models are suitable for a particular problemUnderstand how and why each predictive model worksRecognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problemSelect appropriate metrics to assess the performance of different types of predictive modelDiagnose performance and accuracy problems when they arise and learn how to deal with themGrow your expertise in using R and its diverse range of packagesWho this book is forThis 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. Key FeaturesBook DescriptionWhat you will learnMaster data management in RPerform hypothesis tests using both parametric and nonparametric methodsUnderstand how to perform statistical modeling using linear methodsModel nonlinear relationships in data with kernel density methodsUse matrix operations to improve coding productivityUtilize the observed data to model unobserved variablesDeal with missing data using multiple imputationsSimplify highdimensional data using principal components, singular value decomposition, and factor analysisWho this book is forIf you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.-- Provided by Publisher Being able to deal with the array of problems that you may encounter during complex statistical projects can be difficult. If you have only a basic knowledge of R, this book will provide you with the skills and knowledge to successfully create and customize the most popular data mining algorithms to overcome these difficulties. You will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. Discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on RHadoop projects. You will finish this book feeling confident in your ability to know which data mining algorithm to apply in any situation.-- Provided by publisher Learn the art of building robust and powerful recommendation engines using RKey FeaturesBook DescriptionWhat you will learnGet to grips with the most important branches of recommendationUnderstand various data processing and data mining techniquesEvaluate and optimize the recommendation algorithmsPrepare and structure the data before building modelsDiscover different recommender systems along with their implementation in RExplore various evaluation techniques used in recommender systemsGet to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systemsWho this book is forIf you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.

R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands.

The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available R functions and packages. In many cases, the resultant data can be displayed in a graphical form that is more intuitively understood. You will also learn about the often needed and frequently used analysis techniques in the industry.

By the end of the book, you will know how to go about adopting a range of data science techniques with R.

R is a powerful, open source, functional programming language. It can be used for a wide range of programming tasks and is best suited to produce data and visual analytics through customizable scripts and commands. The purpose of the book is to explore the core topics that data scientists are interested in. This book draws from a wide variety of data sources and evaluates this data using existing publicly available R functions and packages. In many cases, the resultant data can be displayed in a graphical form that is more intuitively understood. You will also learn about the often needed and frequently used analysis techniques in the industry. By the end of the book, you will know how to go about adopting a range of data science techniques with R

About This Book

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• Create your own Qlik Sense applications, and administer server architecture
• Explore practical demonstrations for utilizing Qlik Sense to discover data for sales, human resources, and more

Who This Book Is For

Learning Qlik® Sense is for anyone seeking to understand and utilize the revolutionary new approach to business intelligence offered by Qlik Sense. Familiarity with the basics of business intelligence will be helpful when picking up this book, but not essential.

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Who This Book Is For

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This book is intended for the budding data scientist or quantitative analyst with only a basic exposure to R and statistics. This book assumes familiarity with only the very basics of R, such as the main data types, simple functions, and how to move data around. No prior experience with data mining packages is necessary; however, you should have a basic understanding of data mining concepts and processes. If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics. Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.