Make sense of your data and predict the unpredictable About This Book * 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 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. 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 In Detail 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. Well 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. Style and Approach This book takes a practical hands-on approach wherein the algorithms will be explained with the help of real-world use cases. It is written in a well-researched academic style which is a great mix of theoretical and practical information. Code examples are supplied for both theoretical concepts as well as for the case studies. Key references and summaries will be provided at the end of each chapter so that you can explore those topics on their own.

About the Author

Ralph Winters is an experienced hands-on Data Scientist, Project Manager, Data Analyst, Data Miner, and Applications Programmer/Developer with great combination of technical and business skills. He has a strong background in BI, OLAP, data analytics, Data Science, Data and Text mining, SAS, the R programming language, forecasting, data visualization, relational databases, statistical procedures, logistic regression, predictive data modeling, and database marketing. His specialties include: Healthcare Data Analytics , ETL, SAS, Data Mining, Big Data, Text Analytics, BI, Business Intelligence, OLAP, Statistics, Database, and Complex SQL Querying. He has worked in various industries including Credit Card, Insurance, Pharmaceutical/Healthcare, Banking, Finance, Direct Mail, Direct Marketing, Cellular, and Trading Markets. He also has extensive knowledge of databases including SQL Server 2000/2005, SAS, SSIS, SSAS, DB/2, ORACLE, Sybase, and MySQL. He has worked on Operating Systems such as Windows, UNIX and LINUX. His software skills include SAS, C++,Business Objects, COGNOS, Tableau, MS BI Stack, Excel, @Risk, Dataflux, UNIX, Shell Scripting, SPLUS, R, Perl, Clarabridge, Sharepoint and Agile Tools.