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

Normalizing the data

We now have all the needed statistics to normalize the data. Recall that the formula for normalizing a variable x is as follows:

In order to implement this, we will wrap the needed computations into a function and invoke it for both the training and test datasets:

  • Use the SparkR selectExpr expression to calculate the normalized version of each variable using the formula above.
  • Also, create a new variable with old appended to the name, which preserves the original value of the variable. After testing, you should remove these extra variables to save space, but it is good to retain them while debugging:
         normalize_it <- function (x) { 
                 "age as ageold","(age-age_mean)/ age_std as age", 
                 "mass as massold","(mass-mass_mean)/ mass_std as mass", 
                 "triceps as tricepsold",
                 "(triceps-triceps_mean)/ triceps_std as     triceps", 
                 "pressure as pressureold",