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Statistics for Data Science

You're reading from   Statistics for Data Science Leverage the power of statistics for Data Analysis, Classification, Regression, Machine Learning, and Neural Networks

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781788290678
Length 286 pages
Edition 1st Edition
Languages
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Author (1):
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James D. Miller James D. Miller
Author Profile Icon James D. Miller
James D. Miller
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Table of Contents (13) Chapters Close

Preface 1. Transitioning from Data Developer to Data Scientist 2. Declaring the Objectives FREE CHAPTER 3. A Developer's Approach to Data Cleaning 4. Data Mining and the Database Developer 5. Statistical Analysis for the Database Developer 6. Database Progression to Database Regression 7. Regularization for Database Improvement 8. Database Development and Assessment 9. Databases and Neural Networks 10. Boosting your Database 11. Database Classification using Support Vector Machines 12. Database Structures and Machine Learning

Using R to apply machine learning techniques to a database

We've used the R programming language pretty much throughout this book since it is used by most data scientists and is very easy for people just getting started in statistics to comprehend. In this chapter, we'll again use R, this time to suggest how machine learning techniques might be applicable to a data or database developer.

We'll use a post offered by Will Stanton, a data scientist, to get us started. In his post, he offers a clever example of creating a simple classification model in R, using the caret package.

The R caret package Will uses in his example is very easy to use, containing wrapper functions that allow you to use the exact same functions for training and predicting with dozens of different algorithms. On top of that, it includes sophisticated, built-in methods for evaluating the effectiveness...

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