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

Discovering the important features

We will now introduce the OneR package to discover some of the important features of the dataset. The OneR package will produce a single decision rule for each of the features and then rank them in terms of accuracy. Accuracy is defined as the probability of classifying the outcome correctly and can be expressed as a confusion or error matrix, which we have seen before in the previous chapters. The OneR package has some other nice features, such as the ability to bin integer variables optimally in order to yield the best predictor.

The OneR package does not run natively on Spark, so we first need to use the collect() and sample() functions to perform a 95% sample of the Spark dataframe and then move it to a local R dataframe via the collect() function.

Although this Spark dataframe is small enough to perform the example without the sampling, it is important to know how to sample from a dataframe, since if you are using Spark as intended, your dataframes will...