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R Machine Learning By Example

You're reading from   R Machine Learning By Example Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784390846
Length 340 pages
Edition 1st Edition
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Author (1):
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Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
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Toc

Table of Contents (10) Chapters Close

Preface 1. Getting Started with R and Machine Learning FREE CHAPTER 2. Let's Help Machines Learn 3. Predicting Customer Shopping Trends with Market Basket Analysis 4. Building a Product Recommendation System 5. Credit Risk Detection and Prediction – Descriptive Analytics 6. Credit Risk Detection and Prediction – Predictive Analytics 7. Social Media Analysis – Analyzing Twitter Data 8. Sentiment Analysis of Twitter Data Index

Getting the data


The first step in our data analysis pipeline is to get the dataset. We have actually cleaned the data and provided meaningful names to the data attributes and you can check that out by opening the german_credit_dataset.csv file. You can also get the actual dataset from the source which is from the Department of Statistics, University of Munich through the following URL: http://www.statistik.lmu.de/service/datenarchiv/kredit/kredit_e.html.

You can download the data and then run the following commands by firing up R in the same directory with the data file, to get a feel of the data we will be dealing with in the following sections:

> # load in the data and attach the data frame
> credit.df <- read.csv("german_credit_dataset.csv", header = TRUE, sep = ",") 
> # class should be data.frame
> class(credit.df)
[1] "data.frame"
> 
> # get a quick peek at the data
> head(credit.df)

The following figure shows the first six rows of the data. Each column indicates...

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