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

Supervised versus unsupervised learning models

We have already discussed the concept of target (dependent) variables and independent variables, or features. Features (or independent variables) are used to describe the relationship with, or to predict values of, a target variable. After defining your independent and depending variables, you will formulate your model. One way to characterize the way in which a model learns from the data, is by classifying it into either a supervised or unsupervised learning model.

Supervised learning models

When the possible values of a target variable are specified and labeled, a model is considered supervised, that is, we know what we want to predict, and the goal is to find the most appropriate predictive model which will predict the outcome.

As an example, if we are predicting the approval rating for a product, we know what we are predicting (approval rating of a product), and we also usually know the range of possible outcomes. It could be a percentage from...