#### 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
Customer Feedback
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

## Data transformations

When you are dealing with continuous skewed data, consider applying a data transformation, which can conform the data to a specific statistical distribution with certain properties. Once you have forced the data to a certain shape, you will find it easier to work with certain models. A simple transformation usually involves applying a mathematical function to the data.

Some of the typical data transformations used are `log`, `exp`, and `sqrt`. Some work better for different kinds of skewed data, but they are not always guaranteed to work, so it is always best practice to try out several basic ones and determine if the transformation becomes workable within the modeling context. As always, the simplest transformation is the best transformation, and do some research on how transformations work, and which ones are best for certain kinds of data.

To illustrate the concept of a transformation, we will start by first generating an exponential distribution, which is an example of a...