Grasping the major methods of predictive modeling and moving beyond black box thinking to a deeper level of understanding
Leveraging 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

Although budding data scientists, predictive modelers, or quantitative analysts with only basic exposure to R and statistics will find this book to be useful, the experienced data scientist professional wishing to attain master level status , will also find this book extremely valuable.. This book assumes familiarity with the fundamentals of R, such as the main data types, simple functions, and how to move data around. Although no prior experience with machine learning or predictive modeling is required, there are some advanced topics provided that will require more than novice exposure.

What You Will Learn

Master the steps involved in the predictive modeling process
Grow your expertise in using R and its diverse range of packages
Learn how to classify predictive models and distinguish which models are suitable for a particular problem
Understand steps for tidying data and improving the performing metrics
Recognize the assumptions, strengths, and weaknesses of a predictive model
Understand how and why each predictive model works in R
Select appropriate metrics to assess the performance of different types of predictive model
Explore word embedding and recurrent neural networks in R
Train models in R that can work on very large datasets

In Detail

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.

The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. 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 datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks.

By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.

Style and approach

This book takes a step-by-step approach in explaining the intermediate to advanced concepts in predictive analytics. Every concept is explained in depth, supplemented with practical examples applicable in a real-world setting.

About the Authors

James D. Miller is an IBM-certified expert, creative innovator, accomplished director, senior project leader, and application/system architect. He has over 35 years of extensive experience in application and system design and development across multiple platforms and technologies. His experience includes introducing customers to new technologies and platforms, integrating with IBM Watson Analytics, Cognos BI, and TM1. He has worked in web architecture design, systems analysis, GUI design and testing, database modeling, systems analysis, design and development of OLAP, web and mainframe applications and systems utilization, IBM Watson Analytics, IBM Cognos BI and TM1 (TM1 rules, TI, TM1Web, and Planning Manager), Cognos Framework Manager, dynaSight - ArcPlan, ASP, DHTML, XML, IIS, MS Visual Basic and VBA, Visual Studio, PERL, SPLUNK, WebSuite, MS SQL Server, ORACLE, SYBASE Server, and so on. James's responsibilities have also included all aspects of Windows and SQL solution development and design, such as analysis; GUI (and website) design; data modeling; table, screen/form, and script development; SQL (and remote stored procedures and triggers) development/testing; test preparation; and management and training of programming staff. His other experience includes the development of ETL infrastructure, such as data transfer automation between mainframe (DB2, Lawson, Great Plains, and so on) system and client/server SQL Server, web-based applications, and the integration of enterprise applications and data sources. James has been a web application development manager responsible for the design, development, QA, and delivery of multiple websites, including online trading applications and warehouse process control and scheduling systems, as well as administrative and control applications. He was also responsible for the design, development, and administration of a web-based financial reporting system for a 450-million dollar organization, reporting directly to the CFO and his executive team. Furthermore, he has been responsible for managing and directing multiple resources in various management roles, including as project and team leader, lead developer, and application development director. James has authored Cognos TM1 Developers Certification Guide, Mastering Splunk, and a number of white papers on best practices, including Establishing a Center of Excellence. He continues to post blogs on a number of relevant topics based on personal experiences and industry best practices. James is a perpetual learner, continuing to pursue new experiences and certifications. He currently holds the following technical certifications: IBM Certified Business Analyst - Cognos TM1 IBM Cognos TM1 Master 385 Certification (perfect score of 100%), IBM Certified Advanced Solution Expert - Cognos TM1, IBM Cognos TM1 10.1 Administrator Certification C2020-703 (perfect score of 100%), IBM OpenPages Developer Fundamentals C2020-001-ENU (98% in exam), IBM Cognos 10 BI Administrator C2020-622 (98% in exam), and IBM Cognos 10 BI Professional C2020-180. He specializes in the evaluation and introduction of innovative and disruptive technologies, cloud migration, IBM Watson Analytics, Cognos BI and TM1 application design and development, OLAP, Visual Basic, SQL Server, forecasting and planning, international application development, business intelligence, project development and delivery, and process improvement.

Rui Miguel Forte is currently the chief data scientist at Workable. He was born and raised in Greece and studied in the UK. He is an experienced data scientist, having over 10 years of work experience in a diverse array of industries spanning mobile marketing, health informatics, education technology, and human resources technology. His projects have included predictive modeling of user behavior in mobile marketing promotions, speaker intent identification in an intelligent tutor, information extraction techniques for job applicant resumes and fraud detection for job scams. He currently teaches R, MongoDB, and other data science technologies to graduate students in the Business Analytics MSc program at the Athens University of Economics and Business. In addition, he has lectured in a number of seminars, specialization programs, and R schools for working data science professionals in Athens. His core programming knowledge is in R and Java, and he has extensive experience working with a variety of database technologies such as Oracle, PostgreSQL, MongoDB, and HBase. He holds a Master's degree in Electrical and Electronic Engineering from Imperial College London and is currently researching machine learning applications in information extraction and natural language processing.

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