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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

An analytics methodology outline specific steps


This section will look at each of the analytics methodology steps individually. I will use CRISP-DM as the template, because it covers model deployment, and we have already mentioned the benefits of sampling (which is the first step in SEMMA).

Step 1 business understanding

Many predictive modelers assume that the actual modeling phase is where the most insightful model development takes place. However, much of the groundwork and insight can be discovered early on, and a good understanding of business objectives can avoid pitfalls later on.

Communicating business goals the feedback loop

I must admit, business people and technical people can be better at communicating with each other. How business goals are communicated can run the gamut. It can be anything from a business partner stating, "Tell me how sales need to be increased" or "Tell me something I don't know."

So, it really starts with understanding what the specific business objectives are...

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