Anasse Bari, Mohamed Chaouchi, Tommy Jung's Predictive Analytics For Dummies (For Dummies) PDF

By Anasse Bari, Mohamed Chaouchi, Tommy Jung

ISBN-10: 1118728963

ISBN-13: 9781118728963

Predictive analytics is a department of information mining that is helping expect percentages and developments. Predictive Analytics For Dummies explores the facility of predictive analytics and the way you should use it to make necessary predictions to your enterprise, or in fields reminiscent of advertisements, fraud detection, politics, and others. This useful e-book doesn't lavatory you down with a great deal of mathematical or medical thought, yet as an alternative is helping you quick see tips on how to use the best algorithms and instruments to gather and examine information and use it on make predictions.

Topics comprise utilizing established and unstructured information, construction types, making a predictive research roadmap, environment real looking pursuits, budgeting, and masses more.

- indicates readers how you can use great information and information mining to find styles and make predictions for tech-savvy businesses
- is helping readers see find out how to shepherd predictive analytics initiatives via their companies
- Explains simply enough of the technological know-how and math, but additionally specializes in sensible matters reminiscent of keeping undertaking budgets, making solid displays, and more
- Covers nuts-and-bolts subject matters together with predictive analytics fundamentals, utilizing based and unstructured facts, info mining, and algorithms and strategies for reading data
- additionally covers clustering, organization, and statistical types; making a predictive analytics roadmap; and using predictions to the net, advertising, finance, healthiness care, and elsewhere

Propose, produce, and defend predictive analytics initiatives via your organization with Predictive Analytics For Dummies.

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Extra info for Predictive Analytics For Dummies (For Dummies)

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More complex systems could go into greater detail by ✓ Using profiles created by users that represent their tastes ✓ Factoring in how much s user likes (or highly rates) an item ✓ Weighing how many items the user purchased that are similar to the potential recommended item(s) ✓ Making assumptions about whether a user likes an item on the basis of whether the user has simply viewed the item, even though no purchase was made Here are two common ways you could use this recommender system: ✓ Offline via an e-mail marketing campaign or if the user is on the website while logged in.

Setting aside such sci-fi scenarios, you have to make some concessions to reality and employ some alternative (more difficult) methods to get a useful estimate of the model’s success. Chapter 2: Predictive Analytics in the Wild Even with these difficulties, some modelers argue that uplift modeling provides true marketing impact. They consider it more efficient than response modeling because it doesn’t include the Sure Things in the targeting (which artificially inflates response rates). For that reason, they feel uplift modeling is the choice for target marketing using predictive analytics.

With that much online data, it was only natural that companies would start seeing it as a resource to be mined and refined, seeking patterns in our online behavior and exploiting what they find in hopes to capitalizing on this new opportunity. Amazon (see the accompanying sidebar) is a famous example. In short, this is only the beginning. 17 18 Part I: Getting Started with Predictive Analytics Amazing Growth Throughout this book, we highlight several case studies that illustrate the successful use of predictive analytics.

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Predictive Analytics For Dummies (For Dummies) by Anasse Bari, Mohamed Chaouchi, Tommy Jung


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