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Predictive Analytics For Dummies, by Anasse Bari, Mohamed Chaouchi, Tommy Jung
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Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data
Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.
Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.
- Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses
- Helps readers see how to shepherd predictive analytics projects through their companies
- Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more
- Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data
- Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere
Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.
- Sales Rank: #232049 in Books
- Published on: 2014-03-24
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .70" w x 7.40" l, 1.06 pounds
- Binding: Paperback
- 360 pages
From the Back Cover
Learn to:
- Analyze structured and unstructured data
- Use algorithms and data analysis techniques
- Build clustering, classification and statistical models
- Apply predictive analytics to your website and marketing efforts
A practical guide to using Big Data and technology to discover real-world insights
Predict the future! Data is growing exponentially and predictive analytics is your organization’s key to making use of it to create a competitive advantage. This comprehensive resource will help you define real-world projects and takes a step-by-step approach to the technical aspects of predictive analytics so you can get up and running right away.
- Enter the arena — jump into predictive analytics by discovering how data can translate to a competitive advantage
- Incorporating algorithms — discover data models, how to identify similarities and relationships, and how to predict the future through data classification
- Developing a roadmap — prepare your data, create goals, structure and process your data, and build a predictive model that will get stakeholder buy-in
- Programming predictive analytics — use in-depth tips to install software, modules, and libraries to get going with prediction models
- Making predictive analytics work — gain an understanding of the typical pushback on predictive analytics adoption and how to overcome it
Open the book and find:
- Real-world tips for creating business value
- Common use cases to help you get started
- Details on modeling, k-means clustering, and more
- How you can predict the future with classification
- Information on structuring your data
- Methods for testing models
- Hands-on guides to software installation
- Tips on outlining business goals and approaches
About the Author
Dr. Anasse Bari is a Fulbright scholar, a software engineer, and a data mining expert. Mohamed Chaouchi has conducted extensive research using data mining methods in both health and financial domains. Tommy Jung has worked extensively on natural language processing and algorithmic trading using machine learning.
Most helpful customer reviews
33 of 37 people found the following review helpful.
I think this is one of the best books I read so far about predictive analytics
By sardar
I am a very experienced data scientist and I was pleased to learn that predictive analytics for dummies introduces, for the first time, biologically inspired algorithms that can be applied to make predictions. I found that the visualization chapter was very helpful. The authors introduced innovative ways to visualize analytics results, especially when it comes to forward insights. This book covers how to do predictive analytics; the authors explain in a step by step the roadmap to do so. Over all, I think this is one of the best books I have read so far about predictive analytics.
71 of 87 people found the following review helpful.
Irritating errors
By Dan Nuttle
There is good content in this book, but its reliability is called into serious question by some very irritating and careless errors.
In more than one place, the book gives a link for "extras" that are allegedly available. This link is invalid; there is no such page at the dummies.com site. This is so egregiously over the line that I am being generous in giving this book more than one star. How careless can you be not to ensure that a link on your own site works? What does that carelessness say about all of the content in the book?
In the section on k-means, the tables are so filled with errors that it is impossible to try to tell what the point should be. At one point, 2+3/5 is listed as 2.4 rather than 2.5. 2+3+6/3 is 3.6 rather than 3.7. But in another place, 10+14+10/3=11.4. So numbers are rounded down them they shouldn't be, and rounded up when they shouldn't be. It is obvious that numbers were typed in, and then not even glanced at casually for accuracy.
Even worse is table 6-7, where, we're told, "every element" is iterated. It lists items 1 through 4, and 6 and 7, but 5 is missing, for no apparent reason. All in all, this section almost seems designed to create maximum confusion. I cannot glean how a k-means is calculated. The text is not entirely clear, and the numbers in table 6-7 are junk, so I can not try to reverse-engineer the process.
There is a section on vector support machines that does not explain what a support vector machine is. We are told that it is a binary classifier. We are told that it uses a function. We are told that it is robust. We are not told anything about what distinguishes SVM from any other binary classifier. We have no idea of how to construct an SVM.
The next section, on naive Bayes says at one point, "P(X|Y) is the probability that event X will happen, given that event Y has happened." This is correct. Shortly after, "P(CR_fair|X) is the probability that a customer's credit rating is fair and the customer purchases product X." This is not correct. It is the probability that the customer's credit rating is fair GIVEN that the customer purchases product X. So I have to scrap the section on naive Bayes as well. There are numerous other problems with the notation in this section, including the use in places of "/" in place of "|". Sometimes P is italic, sometimes it is not.
These errors and deficiencies need to be fixed and a new version of the e-book made available, or at the very least, an errata section needs to be established on dummies.com, the entire book should be carefully reviewed, and the dead link needs to be fixed. The book never should have been published with such sloppy errors. If the purchase price had not been as low as it is, I would be demanding my money back. As it is, I will give the authors a limited period of time to make this good. If they do, I will revise this review.
These errors are serious enough that I have a hard time imagining how anyone could post a five-star review, if you get my drift.
13 of 14 people found the following review helpful.
Clean up the errors! Edit the book! WARNING to readers--wait for the revised edition.
By Lineman
This is a good introduction to the topic, but unfortunately contains numerous errors in the examples given. This leaves the reader struggling..."Did the author make a mistake? Or do I not understand it?" It's a major distraction and completely frustrating, especially if someone is new to the topic (or math phobic). The rest of the content is good and worthy of a look.
If you can't wait for the second edition, and buy this one, don't get hung up if an example doesn't match your expectation. It's probably the author who is wrong.
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