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

Extracting the Pima Indians diabetes dataset

After running the following code, we will have the PimaIndiansDiabetes R dataframe loaded and we will run the usual str() and summary() functions. Note that we need to first install the mlbench package to retrieve the data that is contained within the package.

At this point, no Spark directives are being introduced. Even though we are running in a databricks environment, the code is pure R, and you can replicate this code in your regular R environment as well.

# load the library 

Examining the output

As usual, the str() and summary() functions will give you your first insights into the data. The outputs will appear in the console pane, which is typically right below the coding pane.

Note: not all output is shown.

Output from the str() function

  • The str() function tells us that there are 768 observations and 9 variables...