This chapter introduced time series analysis by reading in and exploring Health Care Enrollment Data from the CMS website. Then we moved on to defining some basic Time Series concepts such as Simple and Exponential Moving Averages. Finally we worked with the R "forecast" package to work with some exponential smoothed state space models, and showed you one way to produce automated forecasts for your data. We also showed various plotting methods using the ggplot, lattice package, as well as native R graphics.
Practical Predictive Analytics
By :
Practical Predictive Analytics
By:
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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
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
Using Market Basket Analysis as a Recommender Engine
Exploring Health Care Enrollment Data as a Time Series
Introduction to Spark Using R
Exploring Large Datasets Using Spark
Spark Machine Learning - Regression and Cluster Models
Spark Models – Rule-Based Learning
Customer Reviews