| Description |
1 online resource (1 volume) : illustrations |
| Summary |
Create data mining algorithms About This Book Develop a strong strategy to solve predictive modeling problems using the most popular data mining algorithms Real-world case studies will take you from novice to intermediate to apply data mining techniques Deploy cutting-edge sentiment analysis techniques to real-world social media data using R Who This Book Is For This Learning Path is for R developers who are looking to making a career in data analysis or data mining. Those who come across data mining problems of different complexities from web, text, numerical, political, and social media domains will find all information in this single learning path. What You Will Learn Discover how to manipulate data in R Get to know top classification algorithms written in R Explore solutions written in R based on R Hadoop projects Apply data management skills in handling large data sets Acquire knowledge about neural network concepts and their applications in data mining Create predictive models for classification, prediction, and recommendation Use various libraries on R CRAN for data mining Discover more about data potential, the pitfalls, and inferencial gotchas Gain an insight into the concepts of supervised and unsupervised learning Delve into exploratory data analysis Understand the minute details of sentiment analysis In Detail Data mining is the first step to understanding data and making sense of heaps of data. Properly mined data forms the basis of all data analysis and computing performed on it. This learning path will take you from the very basics of data mining to advanced data mining techniques, and will end up with a specialized branch of data mining - social media mining. You will learn how to manipulate data with R using code snippets and how to mine frequent patterns, association, and correlation while working with R programs. You will discover how to write code for various predication models, stream data, and time-series data. You will also be introduced to solutions written in R based on R Hadoop projects. Now that you are comfortable with data mining with R, you will move on to implementing your knowledge with the help of end-to-end data mining projects. You will learn how to apply different mining concepts to various statistical and data applications in a wide range of fields. At this stage, you will be able to complete complex data mining cases and handle any issues you might encounter during projects. After this, you will gain hands.. |
| Notes |
"Bater Makhabel, Pradeepta Mishra, Nathan Danneman, Richard Heimann."--Cover |
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"Learning path"--Cover |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Description based on online resource; title from title page (viewed July 6, 2017) |
| Subject |
Data mining.
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R (Computer program language)
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Data Mining
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MATHEMATICS / Applied
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MATHEMATICS / Probability & Statistics / General
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Data mining
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R (Computer program language)
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| Form |
Electronic book
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| Author |
Mishra, Pradeepta, author
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Danneman, Nathan, author
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Heimann, Richard, author
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| ISBN |
9781788290814 |
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178829081X |
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1788293746 |
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9781788293747 |
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