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 are in so many industries

We have come a long way since then, and practical analytics solutions have furthered growth in so many different industries. The internet has had a profound effect on this; it has enabled every click to be stored and analyzed. More data is being collected and stored, some with very little effort, than ever before. That in itself has enabled more industries to enter predictive analytics.

Predictive Analytics in marketing

One industry that has embraced PA for quite a long time is marketing. Marketing has always been concerned with customer acquisition and retention, and has developed predictive models involving various promotional offers and customer touch points, all geared to keeping customers and acquiring new ones. This is very pronounced in certain segments of marking, such as wireless and online shopping cards, in which customers are always searching for the best deal.

Specifically, advanced analytics can help answer questions such as, If I offer a customer 10% off with free shipping, will that yield more revenue than 15% off with no free shipping? The 360-degree view of the customer has expanded the number of ways one can engage with the customer, therefore enabling marketing mix and attribution modeling to become increasingly important. Location-based devices have enabled marketing predictive applications to incorporate real-time data to issue recommendation to the customer while in the store.

Predictive Analytics in healthcare

Predictive analytics in healthcare has its roots in clinical trials, which use carefully selected samples to test the efficacy of drugs and treatments. However, healthcare has been going beyond this. With the advent of sensors, data can be incorporated into predictive analytics to monitor patients with critical illness, and to send alerts to the patient when he is at risk. Healthcare companies can now predict which individual patients will comply with courses of treatment advocated by health providers. This will send early warning signs to all parties, which will prevent future complications, as well as lower the total cost of treatment.

Predictive Analytics in other industries

Other examples can be found in just about every other industry. Here are just a few:

  • Finance:
    • Fraud detection is a huge area. Financial institutions are able to monitor client's internal and external transactions for fraud, through pattern recognition and other machine learning algorithms, and then alert a customer concerning suspicious activity. Analytics are often performed in real time. This is a big advantage, as criminals can be very sophisticated and be one step ahead of the previous analysis.
    • Wall street program trading. Trading algorithms will predict intraday highs and lows, and will decide when to buy and sell securities.
  • Sports management:
    • Sports management analytics is able to predict which sports events will yield the greatest attendance and institute variable ticket pricing based upon audience interest.
    • In baseball, a pitcher's entire game can be recorded and then digitally analyzed. Sensors can also be attached to their arm, to alert when future injury might occur.
  • Higher education:
    • Colleges can predict how many, and which kind of, students are likely to attend the next semester, and be able to plan resources accordingly. This is a challenge which is beginning to surface now, many schools may be looking at how scoring changes made to the SAT in 2016 are affecting admissions.
    • Time-based assessments of online modules can enable professors to identify students' potential problems areas, and tailor individual instruction.
  • Government:
    • Federal and State Governments have embraced the open data concept and have made more data available to the public, which has empowered Citizen Data Scientists to help solve critical social and governmental problems.
    • The potential use of data for the purpose of emergency services, traffic safety, and healthcare use is overwhelmingly positive.

Although these industries can be quite different, the goals of predictive analytics are typically implemented to increase revenue, decrease costs, or alter outcomes for the better.