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

Predictive analytics software

Originally, predictive analytics was performed by hand, by statisticians on mainframe computers using a progression of various language such as FORTRAN. Some of these languages are still very much in use today. FORTRAN, for example, is still one of the fastest-performing languages around, and operates with very little memory. So, although it may no longer be as widespread in predictive model development as other languages, it certain can be used to implement models in a production environment.

Nowadays, there are many choices about which software to use, and many loyalists remain true to their chosen software. The reality is that for solving a specific type of predictive analytics problem, there exists a certain amount of overlap, and certainly the goal is the same. Once you get the hang of the methodologies used for predictive analytics in one software package, it should be fairly easy to translate your skills to another package.

Open source software

Open source emphasizes agile development and community sharing. Of course, open source software is free, but free must also be balanced in the context of Total Cost Of Ownership (TCO). TCO costs include everything that is factored into a softwares cost over a period of time: that not only includes the cost of the software itself, but includes training, infrastructure setup, maintenance, people costs, as well as other expenses associated with the quick upgrade and development cycles which exist in some products.

Closed source software

Closed source (or proprietary) software such as SAS and SPSS was at the forefront of predictive analytics, and has continued to this day to extend its reach beyond the traditional realm of statistics and machine learning. Closed source software emphasizes stability, better support, and security, with better memory management, which are important factors for some companies.

Peaceful coexistence

There is much debate nowadays regarding which one is better. My prediction is that they both will coexist peacefully, with one not replacing the other. Data sharing and common APIs will become more common. Each has its place within the data architecture and ecosystem that are deemed correct for a company. Each company will emphasize certain factors, and both open and closed software systems are constantly improving themselves. So, in terms of learning one or the other, it is not an either/or decision. Predictive analytics, per second does not care what software you use. Please be open to the advantages offered by both open and closed software. If you do, that will certainly open up possibilities for working for different kinds of companies and technologies