Mastering Predictive Analytics with R - Second Edition

More Information
  • 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

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.

  • 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
Page Count 448
Course Length 13 hours 26 minutes
ISBN 9781787121393
Date Of Publication 17 Aug 2017


James D. Miller

James D. Miller is an innovator and accomplished senior project lead and solution architect with 37 years' experience of extensive design and development across multiple platforms and technologies. Roles include leveraging his consulting experience to provide hands-on leadership in all phases of advanced analytics and related technology projects, providing recommendations for process improvement, report accuracy, the adoption of disruptive technologies, enablement, and insight identification. He has also written a number of books, including Statistics for Data Science; Mastering Predictive Analytics with R, Second Edition; Big Data Visualization; Learning Watson Analytics; and many more.

Rui Miguel Forte

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.