Market basket analysis consists of some modeling techniques which are typically used by retailers and e-commerce marketplaces to analyze shopping carts and transactions to find out what customers buy the most, what kind of items they buy, what the peak season is for specific items to be sold the most, and so on. We will be focusing on item based transactional patterns in this chapter for detecting and predicting what items people are buying and are most likely to buy. Let us first look at the formal definition of market basket analysis and then we will look at core concepts, metrics, and techniques tied to it. Finally, we will conclude with how to actually use these results to make data driven decisions.
R Machine Learning By Example
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R Machine Learning By Example
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Overview of this book
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them data-driven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve real-world data problems.
This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world problems.
You’ll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of e-commerce, finance, and social-media, which are at the very core of this data-driven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.
Through this book, you will learn to apply the concepts of machine learning to deal with data-related problems and solve them using the powerful yet simple language, R.
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Table of Contents (15 chapters)
R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Getting Started with R and Machine Learning
Let's Help Machines Learn
Predicting Customer Shopping Trends with Market Basket Analysis
Building a Product Recommendation System
Credit Risk Detection and Prediction – Descriptive Analytics
Credit Risk Detection and Prediction – Predictive Analytics
Social Media Analysis – Analyzing Twitter Data
Sentiment Analysis of Twitter Data
Index
Customer Reviews