Mastering Predictive Analytics with R, by Rui Miguel Forte
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Mastering Predictive Analytics with R, by Rui Miguel Forte
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Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
About This Book
- 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
In Detail
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.
Mastering Predictive Analytics with R, by Rui Miguel Forte- Amazon Sales Rank: #331900 in Books
- Published on: 2015-06-30
- Released on: 2015-06-17
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .94" w x 7.50" l, 1.56 pounds
- Binding: Paperback
- 300 pages
About the Author
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 who has 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 include the 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. Currently, he 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 at 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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Most helpful customer reviews
3 of 3 people found the following review helpful. I love that the author has filled the book with interesting ... By Panagiotis Angelopoulos As a data scientist lead, I found this to be a book that is full of clear explanations on the important topics you need to master predictive modeling. I love that the author has filled the book with interesting and valuable references that go beyond just citing a classic textbook and the fact that he gets his hands dirty and writes some R code that isn't just a three line execution of someone else's package. The code samples provided with the book are very well organized and make it trivial to pick up and execute examples from anywhere in the book. I'm currently recommending this to everyone who joins my team.
2 of 2 people found the following review helpful. Very poor, convoluted, verbose explanations in spite of good examples By Paulo C. Rios Jr. The choice of subjects and of examples is excellent, but this text suffers from very poor explanations. Much of the text is a kind of summary and rewriting of some chapters of the great book "Introduction to Statistical Learning" (ISL). But it isn't done consistenly well. The chapter on linear regression has good explanations, although not stellar (the author doesn't seem to care about using partial derivatives from calculus without first mentioning to readers about the advanced treatment). The chapters on logistic regression, neural networks and support vector machines have very poor explanations and suffer from the lack of a deeper conceptual framework and understanding. The chapter on decision tree has such a poor text that it makes a simple model look rather complicated. Most of the other chapters suffer from the same, especially the chapter on time series analysis that is also very poor.The examples in the text are interesting. The choice of the package caret for data preprocessing makes the code much more readable. The constant assessment tests are another great aspect of this book. But the hidden placement of cross-validation inside the chapter on support vector machines isn't a good idea. Cross-validation merits an earlier and more relevant treatment. This is an area where the author doesn't follow ISL that has a chapter specially devoted to resampling methods where cross-validation receives a deserved, distinguished treatment. The order of the chapters isn't good either. Neural networks and support vector machines should have come later, in any case after decision trees (as ISL did too). Apart from the poor and verbose explanations, it seems that this book also suffers where it departed from ISL, that is, where it treats subjects not covered there (neural networks and time series analysis, for example). Its explanations are sincerely bad and such a pain that its great examples aren't enough to sustain them.
2 of 3 people found the following review helpful. Learn by doing By Riadh I do not agree with the book title which sounds as intended for people of intermediate level who want to move forward to master predictive analytics field. A beginner can absolutely find a great material to take advantage of this book to reach indeed an advanced analytics level, even if prior experience in R programming language can be very helpful but not essential. The author treated a huge number of subject in this ~400 pages book which starts from the fundamental principles of predictive analytics (including the typical project life cycle) to advanced methods (neural networks, support vector machines, ensemble methods, probabilistic graphical models …) and subjects (time series analysis, topic modeling, recommendation systems…). The most valuable point of this book is that the main methods and models from the simplest to the most advanced ones are treated very well, the book is balanced and all the covered subjects include numerical examples with perfectly clear and working code resulting in a much larger number of cases studied than the common books treating the same subject ... really much more! One lowlight that I note in this book is the absence of “deep learning” algorithm to complete this important collection of methods especially with the excellent H20 framework from 0xdata, but this is not very weighty as the subject is rather complicated and there is absolutely no R book treating this until today… So 5 stars well deserved!
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