text.skipToContent text.skipToNavigation

Applied Predictive Analytics Principles and Techniques for the Professional Data Analyst von Abbott, Dean (eBook)

  • Erscheinungsdatum: 31.03.2014
  • Verlag: Wiley
eBook (ePUB)
38,99 €
inkl. gesetzl. MwSt.
Sofort per Download lieferbar

Online verfügbar

Applied Predictive Analytics

Learn the art and science of predictive analytics - techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics. It clearly explains the theory behind predictive analytics, teaches the methods, principles, and techniques for conducting predictive analytics projects, and offers tips and tricks that are essential for successful predictive modeling. Hands-on examples and case studies are included. The ability to successfully apply predictive analytics enables businesses to effectively interpret big data; essential for competition today This guide teaches not only the principles of predictive analytics, but also how to apply them to achieve real, pragmatic solutions Explains methods, principles, and techniques for conducting predictive analytics projects from start to finish Illustrates each technique with hands-on examples and includes as series of in-depth case studies that apply predictive analytics to common business scenarios A companion website provides all the data sets used to generate the examples as well as a free trial version of software
Applied Predictive Analytics arms data and business analysts and business managers with the tools they need to interpret and capitalize on big data.


    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 456
    Erscheinungsdatum: 31.03.2014
    Sprache: Englisch
    ISBN: 9781118727690
    Verlag: Wiley
    Größe: 14252 kBytes
Weiterlesen weniger lesen

Applied Predictive Analytics

Chapter 1
Overview of Predictive Analytics

A small direct response company had developed dozens of programs in cooperation with major brands to sell books and DVDs. These affinity programs were very successful, but required considerable up-front work to develop the creative content and determine which customers, already engaged with the brand, were worth the significant marketing spend to purchase the books or DVDs on subscription. Typically, they first developed test mailings on a moderately sized sample to determine if the expected response rates were high enough to justify a larger program.

One analyst with the company identified a way to help the company become more profitable. What if one could identify the key characteristics of those who responded to the test mailing? Furthermore, what if one could generate a score for these customers and determine what minimum score would result in a high enough response rate to make the campaign profitable? The analyst discovered predictive analytics techniques that could be used for both purposes, finding key customer characteristics and using those characteristics to generate a score that could be used to determine which customers to mail.

Two decades before, the owner of a small company in Virginia had a compelling idea: Improve the accuracy and flexibility of guided munitions using optimal control. The owner and president, Roger Barron, began the process of deriving the complex mathematics behind optimal control using a technique known as variational calculus and hired a graduate student to assist him in the task. Programmers then implemented the mathematics in computer code so they could simulate thousands of scenarios. For each trajectory, the variational calculus minimized the miss distance while maximizing speed at impact as well as the angle of impact.

The variational calculus algorithm succeeded in identifying the optimal sequence of commands: how much the fins (control surfaces) needed to change the path of the munition to follow the optimal path to the target. The concept worked in simulation in the thousands of optimal trajectories that were run. Moreover, the mathematics worked on several munitions, one of which was the MK82 glide bomb, fitted (in simulation) with an inertial guidance unit to control the fins: an early smart-bomb.

There was a problem, however. The variational calculus was so computationally complex that the small computers on-board could not solve the problem in real time. But what if one could estimate the optimal guidance commands at any time during the flight from observable characteristics of the flight? After all, the guidance unit can compute where the bomb is in space, how fast it is going, and the distance of the target that was programmed into the unit when it was launched. If the estimates of the optimum guidance commands were close enough to the actual optimal path, it would be near optimal and still succeed. Predictive models were built to do exactly this. The system was called Optimal Path - to - Go guidance.

These two programs designed by two different companies seemingly could not be more different. One program knows characteristics of people, such as demographics and their level of engagement with a brand, and tries to predict a human decision. The second program knows locations of a bomb in space and tries to predict the best physical action for it to hit a target.

But they share something in common: They both need to estimate values that are unknown but tremendously useful. For the affinity programs, the models estimate whether or not an individual will respond to a campaign, and for the guidance program, the models estimate the best guidance command. In this sense, these two programs are very similar because they both involve predicting a value or values that are known historically, but

Weiterlesen weniger lesen