Learning Predictive Analytics with R ePub (Adobe DRM) download by Mayor   Eric

Learning Predictive Analytics with R

Packt Publishing
Publication date: September 2015
ISBN: 9781782169369
Digital Book format: ePub (Adobe DRM)

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About This Book
  • Acquire predictive analytic skills using various tools of R
  • Make predictions about future events by discovering valuable information from data using R
  • Comprehensible guidelines that focus on predictive model design with real-world data
Who This Book Is For

If you are a statistician, chief information officer, business analyst, data scientist, ML engineer, ML practitioner, quantitative analyst, or student of machine learning, this is the book for you.

What You Will Learn
  • Customize R by installing and loading new packages
  • Explore the structure of data using clustering algorithms
  • Turn unstructured text into ordered data and acquire knowledge from the data
  • Classify your observations using Naive Bayes, k-NN, and decision trees
  • Reduce the dimensionality of your data by using principal component analysis
  • Discover association rules by using Apriori
  • Learn how cross-validation helps you ensure classification quality
  • Understand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regression
  • Use PMML to deploy the models generated in R
In Detail

R is statistical software that is used for data analysis. It helps to extract information with its many standard and cutting-edge statistical functions.

You will learn how to 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. All chapters will guide you in acquiring the skills in a practical way.

Most chapters also include a theoretical introduction that will sharpen your understanding of the subject matter and invite you to go further.

This book familiarizes you with the most common data mining tools in R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naive Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools in R as well as lattice for visualizing patterns into data organized in groups.

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