Pradeepta Mishra - R Data Mining Blueprints [2016, PDF/EPUB, ENG] torrent

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R Data Mining Blueprints

Год издания: 2016
Автор: Pradeepta Mishra
Жанр или тематика: Программирование

Издательство: Packt Publishing
ISBN: 9781783989683
Язык: Английский

Формат: PDF/EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 260

Описание: The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.

This book explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

What You Will Learn

- Make use of statistics and programming to learn data mining concepts and its applications
- Use R Programming to apply statistical models on data
- Create predictive models to be applied for performing classification, prediction, and recommendation
- Use of various libraries available on R CRAN (comprehensive R archives network) in data mining
- Apply data management steps in handling large datasets
- Learn various data visualization libraries available in R for representing data
- Implement various dimension reduction techniques to handle large datasets
- Acquire knowledge about neural network concept drawn from computer science and its applications in data mining
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Table of Contents

1: Data Manipulation Using In-built R Data
2: Exploratory Data Analysis with Automobile Data
3: Visualize Diamond Dataset
4: Regression with Automobile Data
5: Market Basket Analysis with Groceries Data
6: Clustering with E-commerce Data
7: Building a Retail Recommendation Engine
8: Dimensionality Reduction
9: Applying Neural Network to Healthcare Data
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