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Practical Predictive Analytics
Practical Predictive Analytics

Practical Predictive Analytics: Analyse current and historical data to predict future trends using R, Spark, and more

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$38.99 $43.99
eBook Jun 2017 576 pages 1st Edition
eBook
$38.99 $43.99
Paperback
$54.99
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Renews at $19.99p/m
Arrow left icon
Profile Icon Winters
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$38.99 $43.99
eBook Jun 2017 576 pages 1st Edition
eBook
$38.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$38.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m

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Practical Predictive Analytics

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Key benefits

  • 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

Description

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.

Who is this book for?

This book is for those with a mathematical/statistics background who wish to understand the concepts, techniques, and implementation of predictive analytics to resolve complex analytical issues. Basic familiarity with a programming language of R is expected.

What you will learn

  • 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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 30, 2017
Length: 576 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880469
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Product Details

Publication date : Jun 30, 2017
Length: 576 pages
Edition : 1st
Language : English
ISBN-13 : 9781785880469
Category :
Languages :
Tools :

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Frequently bought together


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Practical Data Science Cookbook, Second Edition
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Table of Contents

12 Chapters
Getting Started with Predictive Analytics Chevron down icon Chevron up icon
The Modeling Process Chevron down icon Chevron up icon
Inputting and Exploring Data Chevron down icon Chevron up icon
Introduction to Regression Algorithms Chevron down icon Chevron up icon
Introduction to Decision Trees, Clustering, and SVM Chevron down icon Chevron up icon
Using Survival Analysis to Predict and Analyze Customer Churn Chevron down icon Chevron up icon
Using Market Basket Analysis as a Recommender Engine Chevron down icon Chevron up icon
Exploring Health Care Enrollment Data as a Time Series Chevron down icon Chevron up icon
Introduction to Spark Using R Chevron down icon Chevron up icon
Exploring Large Datasets Using Spark Chevron down icon Chevron up icon
Spark Machine Learning - Regression and Cluster Models Chevron down icon Chevron up icon
Spark Models – Rule-Based Learning Chevron down icon Chevron up icon
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