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

Joining data

If you need to bring together different data sources, SQL is one method for bringing data together. As mentioned, SQL syntax is common to a lot of environments, so if you learn SQL syntax in R, you have started to learn how data is processed in other environments. But do not restrict yourself to just SQL. Other options exist for joining data, such as using the merge statement. Merge is a native function that accomplishes the same objective. And some other packages handle data integration fairly well. I will also be using the dplyr package to perform some of the same tasks as could be done in SQL.

The sqldf package is a standard R package that uses standard SQL syntax to merge, or join, two tables together. For relational data, this is accomplished by associating a variable on one table (primary key) with a similar variable on another associated table. Note that I am using the term table in the context of a relational database environment. In the R environment, an SQL table is...