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 2 data understanding

Once an objective is established and data sources have been identified, you can begin looking at the data in order to understand how each data element behaves individually, as well as how it interacts in combination with other variables. But even before you start looking at the values of variables, it is important to understand the different types of data levels of measurement and the kind of analyses you can perform with them.

Levels of measurement

Levels of measurement is a classification system for classifying data into 4 different categories which is discussed as follows (ratio, ordinal, interval, and nominal). It is an important aspect of the project or studies metadata.

Levels of measurement is important in the world of predictive analytics since the specific measurements will often dictate which algorithm or techniques can be applied. For example k-means clustering does work if you want to incorporate nominal data, and logistic regression can not use ratio data...