Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Association rule mining


Association rule mining is finding associations or patterns among a collection of items which occur frequently. It is also known as Market basket analysis.

Its main aim is to understand the buying habits of the customer, which is done by finding the correlations and patterns among the items that customers intended to buy or actually bought. For example, a customer who buys a computer keyboard is also likely to buy a computer mouse or a pen drive.

The rule is given by:

  • Antecedent → Consequent [support, confidence]

Measures of association rules

Let A, B, C, D, and E .... represent different items.

Then we need to generate association rules, for example:

  • {A, J} → {C}

  • {M, D, J} → {X}

The first rule here means that when A and J are bought together then there is a high probability of the customer buying C too.

Similarly, the second rule means that when M, D, and J are bought together there is a high probability of the customer buying X too.

These rules are measured by:

  • Support...