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

Creating new columns

Usually it's necessary to create some new transformation based on existing variables which will improve a prediction. We have already seen that binning a variable is often done to create a nominal variable from a quantitative one.

Let's create a new column, called agecat, which divides age into two segments. To keep things simple, we will start off by rounding the age to the nearest integer.

filtered <- SparkR::filter(out_sd, "age > 0 AND insulin > 0") 
filtered$age <- round(filtered$age,0) 
filtered$agecat <- ifelse(filtered$age <= 35,"<= 35","35 Or Older") 
SparkR::head(SparkR::select(filtered, "age","agecat")) 

In the code which you just ran, you may notice that some commands are prefaced by SparkR::

This is done to let the program know which version of the function we wish to apply, and it is always good practice to preface commands in this way, in order to avoid syntax errors and misapplying identically named functions which occur between SparkR...