#### 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

## Calculating goodness of fit measures

### Confusion matrix

We can compute the confusion, or error, matrix in order to determine how our manual calculation performed, when we classified the prediction outcomes as correct or not:

```#Confusion matrix
result <- sql("select outcome,correct, count(*) as k, avg(totrows) as totrows from preds_tbl where grp=1 group by 1,2 order by 1,2")
result\$classify_pct <- result\$k/result\$totrows

display(result) ```

To determine the grand total correct model prediction, sum the correct=Y columns previously:

Summary of correct predictions for training group:

 Correctly predicted outcome=1 20% Correctly predicted outcome=0 59% Total Correct Percentage 79%

You can see that there is much more predictive power in predicting outcome=0 than there is outcome=1.