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https://github.com/PacktPublishing/Practical-Predictive-Analytics
This is the code repository for Practical Predictive Analytics, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
About the BookThis 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.
Instructions and NavigationAll of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
#run the modelmodel <- OneR(train_data, frisked ~ ., verbose = TRUE)#summarize the modelsummary(model)#run the sql function from the SparkR packageSparkR::sql("SELECT sample_bin , count(*)\FROM out_tbl group by sample_bin")This is neither an introductory predictive analytics book, not an introductory book for learning R or Spark. Some knowledge of base R data manipulation techniques is expected. Some prior knowledge of predictive analytics is useful. As mentioned earlier, knowledge of basic statistical concepts such as hypothesis testing, correlation, means, standard deviations, and p-values will also help you navigate this book.
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