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

Performing some exploratory analysis on positives

Before we move on to exploring the entire Spark dataframe, we can look at some of the data already generated for positive cases. As you may recall from the prior chapter, this is stored in the Spark dataframe out_sd1.

We have generated some random sample bins specifically so that we can do some exploratory analysis.

We can use the filter command to extract random sample 1, and take the first 1,000 records:

  • The filter is a SparkR command that allows you to subset a Spark dataframe
  • The display command is a databricks command that is equivalent to the View command we have previously used and you can also use the head function as well to limit the number of rows that are displayed:

This code chunk extracts 1000 records from the positives and displays them:

        small_pos <- head(SparkR::filter(out_sd1,out_sd1$sample_bin==1),1000) 


The data appears in tabular form, and you can scroll up/down...