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

Filtering out single item transactions

Since we will want to have a basket of items to perform some association rules on, we will want to filter out the transactions that only have one item per invoice. That might be useful for a separate analysis of customers who only purchased one item, but it does not help with finding associations between multiple items, which is the goal of this exercise.

  • Let's use sqldf to find all of the single item transactions, and then we will create a separate dataframe consisting of the number of items per customer invoice:
  • First construct a query: How many distinct invoices were there? We see that there were 25900 separate invoices:
        sqldf("select count(distinct InvoiceNo) from   
        > Loading required package: tcltk 
        >   count(distinct InvoiceNo)
        > 1                     25900 
  • How many invoices contain only single transactions? First, extract the single item invoices: