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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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Table of Contents (15) Chapters Close

R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
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

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We have analyzed our dataset, performed necessary feature engineering and statistical tests, built visualizations, and gained substantial domain knowledge about credit risk analysis and what kind of features are considered by banks when they analyze customers. The reason why we analyzed each feature in the dataset in detail was to give you an idea about each feature that is considered by banks when analyzing credit rating for customers. This was to give you good domain knowledge understanding and also to help you get familiar with the techniques of performing an exploratory and descriptive analysis of any dataset in the future. So, what next? Now comes the really interesting part of using this dataset; building feature sets from this data and feeding them into predictive models to predict which customers can be potential credit risks and which of them are not. As mentioned previously, there are two steps to this: data and algorithms. In fact, we will go a step further and say...

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