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Practical Predictive Analytics

Practical Predictive Analytics

By : Winters
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Practical Predictive Analytics

Practical Predictive Analytics

By: Winters

Overview of this book

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.
Table of Contents (13 chapters)
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Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows:

"Save all output to the /PracticalPredictiveAnalytics/Outputs directory."

A block of code is set as follows:

#run the model
model <- OneR(train_data, frisked ~ ., verbose = TRUE)
#summarize the model
summary(model)
#run the sql function from the SparkR package
SparkR::sql("SELECT sample_bin , count(*)
\FROM out_tbl group by sample_bin")

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

#note we are specifing the SparkR filter, not the dplyr filer
head(SparkR::filter(out_sd1,out_sd1$sample_bin==1),1000)

Any command-line, (including commands at the R console) input or output is written as follows:

> summary(xchurn)

New terms and important words are shown in bold. Words that you see on the screen, in menus or dialog boxes for example, appear in the text like this: "Clicking the Next button moves you to the next screen."

Warnings or important notes appear in a box like this.
Tips and tricks appear like this.
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Practical Predictive Analytics
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