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

Missing values

Missing values denote the absence of a value for a variable. Since data can never be collected in a perfect manner, many missing values can appear due to human oversight, or can be introduced via any systematic process that touches a data element. It can be due to a survey respondent not completing a question, or, as we have seen, it can be created from joining a membership file with a transaction file. In this case, if a member did not have a purchase in a particular year, it might end up as NA or missing.

The first course of action for handling missing values is to understand why they are occurring. In the course of plotting missing values, you not only want to produce counts of missing values, but you want to determine which sub-segments may be responsible for the missing values.

To research this, attempt to break out your initial analysis by time periods and other attributes using some of the bivariate analysis techniques that have been mentioned. This will help you to identify...