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

Time series data

Time series data is usually a set of ordered data collected over equally spaced intervals. Time series data occurs in most business and scientific disciplines, and the data is closely tied to the concept of forecasting, which uses previously measured data points to predict future data points based upon a specific statistical model.

Time series data differs from the kind of data that we have been looking at previously; because it is a set of ordered data points, it can contain components such as trend, seasonality, and autocorrelation, which have little meaning in other types of analysis, such as "Cross-sectional" analysis, which looks at data collected at a static point in time.

Usually, time series data is collected in equally spaced intervals, such as days, weeks, quarters, or years, but that is not always the case. Measurement of events such as natural disasters is a prime example. In some cases, you can transform uneven data into equally spaced data. In other cases, you...