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.
Title Page
Credits
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Preface
Free Chapter
Getting Started with Predictive Analytics
The Modeling Process
Inputting and Exploring Data
Introduction to Regression Algorithms
Introduction to Decision Trees, Clustering, and SVM
Using Survival Analysis to Predict and Analyze Customer Churn
Introduction to Spark Using R
Exploring Large Datasets Using Spark
Spark Machine Learning - Regression and Cluster Models
Spark Models – Rule-Based Learning

Simple moving average

A simple moving average will simply take the sum of the time series variable for the last k periods and then will divide it by the number of periods. In this sense, it is identical to the calculation for the mean. However, what makes it different from a simple mean is the following:

• The average will shift for every additional time period. Moving averages are backward-looking, and every time a time period shifts, so will the average. That is why they are called moving. Moving averages are sometimes called rolling averages.
• The look backwards period can shift. That is the second characteristic of a moving average. A 10-period moving average will take the average of the last 10 data elements, while a 20-period moving average will take the sum of the last 20 data points, and then divide by 20.

Computing the SMA using a function

To compute a rolling five-period moving average for our data, we will use the simple moving average (`SMA`) function from the `TTR` package, and then display...