#### 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

## Combining the training and test dataset

Next, we will combine the training (`grp=1`) and testing (`grp=0`) datasets into one dataframe and manually calculate some accuracy statistics:

• `preds\$error`: this is the absolute difference between the outcome (0,1) and the prediction. Recall that for a binary regression model, the prediction represents the probability that the event (diabetes) will occur.
• `preds\$errorsqr`: this is the calculated squared error. This is done in order to remove the sign.
• `preds\$correct`: in order to classify the probability into correct or not correct, we will compare the error to a `.5` cutoff. If the error was small (<- `.5`) we will call it correct, otherwise it will be considered not correct. This is a somewhat arbitrary cutoff, and it is used to determine which category to place the prediction in.

As a final step, we will once again separate the data back into test and training based upon the `grp` flag:

```#classify 'correct' prediction if error is less than or equal to .5

preds...```