Make sense of your data and predict the unpredictable
Key Features
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
Book 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.
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
Who this book is 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.
Table of Contents
Getting Started with Predictive Analytics
The Modeling process
Inputting and Exploring Data
Introduction to Basic Algorithms
Introduction to Decision trees, Clustering, and SVM
Using Survival Analysis to Predict and Analyze Customer Churn
Using Market Basket Analysis as a Recommender Engine
Exploring Health Care Enrollment Data as a Time Series