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- A unique book that centers around develop six key practical skills needed to develop and implement predictive analytics
- Apply the principles and techniques of predictive analytics to effectively interpret big data
- Solve real-world analytical problems with the help of practical case studies and real-world scenarios taken from the world of healthcare, marketing, and other business domains

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.

[*] Master the core predictive analytics algorithm which are used today in business
[*] Learn to implement the six steps for a successful analytics project
[*] Classify the right algorithm for your requirements
[*] Use and apply predictive analytics to research problems in healthcare
[*] Implement predictive analytics to retain and acquire your customers
[*] Use text mining to understand unstructured data
[*] Develop models on your own PC or in Spark/Hadoop environments
[*] Implement predictive analytics products for customers

Download this book in **EPUB** and **PDF** formats

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Publication date :
Jun 30, 2017

Length
576 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781785886188

Category :

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Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

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Preface

1. Getting Started with Predictive Analytics

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

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