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

Becoming large by starting small

The strategy we will use in this chapter is to first retrieve a small existing publicly available dataset (Pima Indians diabetes). Then we will perform some basic exploratory analysis, compute some key statistical properties, and then use those properties to simulate a much larger dataset that we will use to input into Spark. The key characteristics that we will use to generate this 'big data' will be:

  • The means/standard deviations of the variables: the goal will be to generate means and standard deviations for the large dataset, which are close to the equivalent means and standard deviations of the small dataset.
  • The correlations of the variables: since statistical modeling and analysis is largely based upon the association among the variables, the goal of the simulation will be to preserve all of the 2-way correlation numbers for the large dataset which exist in the small dataset.
  • The underlying distribution of the variables: we will assume normal distributions...