Key FeaturesA unique book that centers around develop six key practical skills needed to develop and implement predictive analyticsApply the principles and techniques of predictive analytics to effectively interpret big dataSolve 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 domainsBook DescriptionThis 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.What you will learnMaster the core predictive analytics algorithm which are used today in businessLearn to implement the six steps for a successful analytics projectClassify the right algorithm for your requirementsUse and apply predictive analytics to research problems in healthcareImplement predictive analytics to retain and acquire your customersUse text mining to understand unstructured dataDevelop models on your own PC or in Spark/Hadoop environmentsImplement predictive analytics products for customersAbout the AuthorRalph Winters started his career as a database researcher for a music performing rights organization (he composed as well!), and then branched out into healthcare survey research, finally landing in the Analytics and Information technology world. He has provided his statistical and analytics expertise to many large fortune 500 companies in the financial, direct marketing, insurance, healthcare, and pharmaceutical industries. He has worked on many diverse types of predictive analytics projects involving customer retention, anti-money laundering, voice of the customer text mining analytics, and health care risk and customer choice models.He is currently data architect for a healthcare services company working in the data and advanced analytics group. He enjoys working collaboratively with a smart team of business analysts, technologists, actuaries as well as with other data scientists.Ralph considered himself a practical person. In addition to authoring Practical Predictive Analytics for Packt Publishing, he has also contributed two tutorials illustrating the use of predictive analytics in Medicine and Healthcare in Practical Predictive Analytics and Decisioning Systems for Medicine: Miner et al., Elsevier September, 2014, and also presented Practical Text Mining with SQL using Relational Databases, at the 2013 11th Annual Text and Social Analytics Summit in Cambridge, MA.Ralph resides in New Jersey with his loving wife Katherine, amazing daughters Claire and Anna, and his four-legged friends, Bubba and Phoebe, who can be unpredictable.Ralphs web site can be found at ralphwinters.comTable of ContentsGetting Started with Predictive AnalyticsThe Modeling processInputting and Exploring DataIntroduction to Basic AlgorithmsIntroduction to Decision trees, Clustering, and SVMUsing Survival Analysis to Predict and Analyze Customer ChurnUsing Market Basket Analysis as a Recommender EngineExploring Health Care Enrollment Data as a Time SeriesIntroduction to Spark Using RExploring Large Datasets Using SparkSpark Machine Learning – Regression and Cluster ModelsSpark Models – Rule-Based Learning