R: Predictive Analysis

Master the art of predictive modeling

R: Predictive Analysis

Tony Fischetti, Eric Mayor, Rui Miguel Forte

2 customer reviews
Master the art of predictive modeling
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Book Details

ISBN 139781788290371
Paperback1065 pages

Book Description

Predictive analytics is a field that uses data to build models that predict a future outcome of interest. It can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.

The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. This Learning Path will provide you with all the steps you need to master the art of predictive modeling with R.

We start with an introduction to data analysis with R, and then gradually you’ll get your feet wet with predictive modeling. You will get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. You will be able to solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. You will then perform key predictive analytics tasks using R, such as train and test predictive models for classification and regression tasks, score new data sets and so on. By the end of this Learning Path, 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.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

  • Data Analysis with R, Tony Fischetti
  • Learning Predictive Analytics with R, Eric Mayor
  • Mastering Predictive Analytics with R, Rui Miguel Forte

Table of Contents

Chapter 18: Dimensionality Reduction with Principal Component Analysis
Chapter 20: Probability Distributions, Covariance, and Correlation
Chapter 26: Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML

What You Will Learn

  • Get to know the basics of R’s syntax and major data structures
  • Write functions, load data, and install packages
  • Use different data sources in R and know how to interface with databases, and request and load JSON and XML
  • Identify the challenges and apply your knowledge about data analysis in R to imperfect real-world data
  • Predict the future with reasonably simple algorithms
  • Understand key data visualization and predictive analytic skills using R
  • Understand the language of models and the predictive modeling process

Authors

Table of Contents

Chapter 18: Dimensionality Reduction with Principal Component Analysis
Chapter 20: Probability Distributions, Covariance, and Correlation
Chapter 26: Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML

Book Details

ISBN 139781788290371
Paperback1065 pages
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