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Eric Mayor - Learning Predictive Analytics with R [2015, PDF/EPUB, ENG]

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Post 16-Jul-2016 01:00

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Learning Predictive Analytics with R
Год издания: 2015
Автор: Eric Mayor
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781782169352
Язык: Английский
Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 332
Описание: R is statistical software that is used for data analysis. There are two main types of learning from data: unsupervised learning, where the structure of data is extracted automatically; and supervised learning, where a labeled part of the data is used to learn the relationship or scores in a target attribute. As important information is often hidden in a lot of data, R helps to extract that information with its many standard and cutting-edge statistical functions.
This 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 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 Naïve Bayes, k-NN, and decision trees
- Reduce the dimensionality of your data using principal component analysis
- Discover association rules using Apriori
- 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

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Table of Contents
1: Setting GNU R for Predictive Analytics
2: Visualizing and Manipulating Data Using R
3: Data Visualization with Lattice
4: Cluster Analysis
5: Agglomerative Clustering Using hclust()
6: Dimensionality Reduction with Principal Component Analysis
7: Exploring Association Rules with Apriori
8: Probability Distributions, Covariance, and Correlation
9: Linear Regression
10: Classification with k-Nearest Neighbors and Naïve Bayes
11: Classification Trees
12: Multilevel Analyses
13: Text Analytics with R
14: Cross-validation and Bootstrapping Using Caret and Exporting Predictive Models Using PMML
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