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

Running summary statistics

One of the first things I do upon creating a new data object, is to run summary statistics. There is a Spark-specific function of the R summary function known as describe(). You can the specific function summary(); however, if you do this instead of using describe(), I would preface it with SparkR:: in order to specify which version of summary you are using:


The output appears in a slightly different format than if you ran a summary on a native R dataframe, but contains the basic measures that you are looking for, count, mean, stddev, min, and max:

We can also compare this summary with the summary of the original Pima Indians dataframe, and see that the simulation has done a pretty good job of estimating the means. The number of observations is approximately 1,000 times the original size and the ratio of diabetes to nondiabetes patients has been preserved:

#compare with original dataset