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

Step 4 modeling

In the modeling stage, you will pick an appropriate predictive modeling technique that fits your problem and apply it to your data. There are several factors which influence the selection of a model:

  1. Who will use the model?
  2. How will the model be used?
  3. What are the assumptions of the model?
  4. How much data do I have?
  5. How many variables do I need to use?
  6. What is the accuracy level needed by the model?
  7. Am I willing to trade some accuracy for interpretability?

Particularly related to the last point is the concept of bias and variance.

Bias is related to the ability of a model to approximate the data. Low bias algorithms are able to fit the data with little error. While this may seem to an advantage all of the time, it can result in a complex model which is unstable, and difficult to explain. On the other hand, a high bias model is relatively simple to explain (like linear regression), but may sacrifice some accuracy for explanability, and stability. You will usually start by looking at...