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

Exponential moving average

For a simple moving average (SMA), equal weight is given to all data points, regardless of how old they are or how recently they occurred. An exponential moving average (EMA) gives more weight to recent data, under the assumption that the future is more likely to look like the recent past, rather than the older past.

The EMA is actually a much simpler calculation. An EMA begins by calculating a simple moving average. When it reaches the specified number of lookback periods (n), it computes the current value by assigning different weights to the current value,and to the previous value.

This weighting is specified by the smoothing (or ratio) factor. When ratio=1, the predicted value is entirely based upon the last time value. For ratios b=0, the prediction is based upon the average of the entire lookback period. Therefore, the closer the smoothing factor is to 1, the more weight it will give to recent data. If you want to give additional weight to older data, decrease...