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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Decision tree algorithms


Decision trees are considered a good predictive model to start with, and have many advantages. Interpretability, variable selection, variable interaction, and the flexibility to choose the level of complexity for a decision tree all come into play.

Decision trees methods are considered classification methods, so the typical use case for a decision tree is predicting a class or category. However, there are also certain types of decision trees, which are known as regression trees, where the output is a continuous variable. In this way, we can begin development models that are a mix of numeric and categorical variables.

Decision trees are heavily used in marketing and advertising, and in any industry where there is a need to segment customers into different groups. They are also used in healthcare for disease and risk classification.

Advantages of decision trees

Decision trees have many advantages. They can be easily understood by both technical and business people and...