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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

Frequent itemset generation

We will now look at a better technique to find patterns and detect frequently bought products. For this, we will be using the frequent itemset generation technique. We will be implementing this algorithm from scratch because, even though when we solve any machine learning or optimization problem we usually use readymade machine learning algorithms out of the box which are optimized and available in various R packages, one of the main objectives of this book is to make sure we understand what exactly goes on behind the scenes of a machine learning algorithm. Thus, we will see how we can build some of these algorithms ourselves using the principles of mathematics, statistics, and logic.

Getting started

The data we will be using for this is the shopping_transaction_log.csv dataset which we used to explain the concepts of market basket analysis at the beginning of the chapter. The code we will be using for this section is available in the ch3_frequent itemset generation...

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