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


In this chapter, we learned all about getting data prepared for analysis so that you can start to run models. It starts with inputting external data in raw form, and we saw that there are several ways you can accomplish these available methods. You also learned how to generate your own data and two different ways you can use to join, or munge data together, one using SQL and the other using dplyr function.

We later proceeded to cover some basic data cleaning and data exploration techniques that are sometimes needed after your data is input, such as standardizing and transposing the data, changing the variables type, creating dummy variables, binning, and eliminating redundant data. You now know about the key R functions that are used to take a first glance at the contents of the data, as well as its structure.

We then covered the important concepts of analyzing missing values and outliers, and how to handle them.

We saw a few ways to decrease the number of variables to a manageable...