Learning predictive analytics with R : get to grips with key data visualization and predictive analytic skills using R / Eric Mayor.

Format:

Book

Author/Creator:

Mayor, Eric, author.

Series:

Community experience distilled.

Community experience distilled

Language:

English

Subjects (All):

R (Computer program language).

R (Computer program language)--Statistical methods.

Prediction theory.

Data mining.

Information visualization.

Forecasting--Mathematical models.

Forecasting.

Physical Description:

1 online resource (333 p.)

Place of Publication:

Birmingham [United Kingdom] : Packt Publishing, 2015.

Language Note:

English

Biography/History:

Mayor Eric: Eric Mayor is a senior researcher and lecturer at the University of Neuchatel, Switzerland. He is an enthusiastic user of open source and proprietary predictive analytics software packages, such as R, Rapidminer, and Weka. He analyzes data on a daily basis and is keen to share his knowledge in a simple way.

Summary:

Get to grips with key data visualization and predictive analytic skills using R Key FeaturesAcquire predictive analytic skills using various tools of RMake predictions about future events by discovering valuable information from data using RComprehensible guidelines that focus on predictive model design with real-world dataBook DescriptionThis book is packed with easy-to-follow guidelines that explain the workings of the many key data mining tools of R, which are used to discover knowledge from your data. 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. The book familiarizes you with the most common data mining tools of R, such as k-means, hierarchical regression, linear regression, association rules, principal component analysis, multilevel modeling, k-NN, Naïve Bayes, decision trees, and text mining. It also provides a description of visualization techniques using the basic visualization tools of R as well as lattice for visualizing patterns in data organized in groups. This book is invaluable for anyone fascinated by the data mining opportunities offered by GNU R and its packages. What you will learnCustomize R by installing and loading new packagesExplore the structure of data using clustering algorithmsTurn unstructured text into ordered data, and acquire knowledge from the dataClassify your observations using Naïve Bayes, k-NN, and decision treesReduce the dimensionality of your data using principal component analysisDiscover association rules using AprioriUnderstand how statistical distributions can help retrieve information from data using correlations, linear regression, and multilevel regressionUse PMML to deploy the models generated in RWho this book is forIf you are a statistician, chief information officer, data scientist, ML engineer, ML practitioner, quantitative analyst, and student of machine learning, this is the book for you. You should have basic knowledge of the use of R. Readers without previous experience of programming in R will also be able to use the tools in the book.

Contents:

Learning Predictive Analytics with R: Get to grips with key data visualization and predictive analytic skills using R

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed December 31, 2015).

ISBN:

9781782169369

1782169369

OCLC:

1491311934