Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Method two Creating a physical transactions file

Now that you know how to run association rules using the coerce to dataframe method, we will now illustrate the write to file method:

  • In the write to file method, each item is written to a separate line, along with the identifying key, which in our case is the InvoiceId

  • The advantage to the write to file method is that very large data files can be accumulated separately, and then combined together if needed

  • You can use the function to display the contents of the file that will be input to the association rules algorithm:

OnlineRetail <- OnlineRetail[1:100,]
> [1] 268034 
> InvoiceNo StockCode  Description                Quantity
 > 5   6365     71053  METAL LANTERN                     6
 > 6   536365   21730  GLASS STAR FROSTED T-LIGHT HOLDER 6
 > 2   536365   22752  SET 7 BABUSHKA NESTING BOXES      2...