#### 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.
Title Page
Credits
www.PacktPub.com
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Preface
Free Chapter
Getting Started with Predictive Analytics
The Modeling Process
Inputting and Exploring Data
Introduction to Regression Algorithms
Introduction to Decision Trees, Clustering, and SVM
Using Survival Analysis to Predict and Analyze Customer Churn
Introduction to Spark Using R
Exploring Large Datasets Using Spark
Spark Machine Learning - Regression and Cluster Models
Spark Models – Rule-Based Learning

## Ranking the coefficients

Now that we have the coefficients, we can begin to rank each of the categories by increasing trend. Since the results we have obtained so far are contained in embedded lists, which are a bit difficult to work with, we can perform some code manipulation to transform them into a regular data frame, with one row per category, consisting of the category name, coefficient, and coefficient rank:

```library(dplyr)
# extract the coefficients part from the model list, and then transpose the
# data frame so that the coefficient appear one per row, rather than 1 per
# column.

xx <- as.data.frame(fitted_models\$model)
xx2 <- as.data.frame(t(xx[2, ]))

# The output does not contain the category name, so we will merge it back
# from the original data frame.

xx4 <- cbind(xx2, as.data.frame(fitted_models))[, c(1, 2)]  #only keep the first two columns

# rank the coefficients from lowest to highest. Force the format of the rank
# as length 2, with leading zero's

tmp <-...```