Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph 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 (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Spark machine learning using logistic regression

Now that we have constructed our test and training datasets, we will begin by building a logistic regression model which will predict the outcome 1 or 0. As you will recall, 1 designates diabetes detected, while 0 designates diabetes not detected.

The syntax of a Spark glm is very similar to a normal glm. Specify the model using formula notation. Be sure to specify family = "binomial" to indicate that the outcome variable has only two outcomes:

# run glm model on Training dataset and assign it to object named "model"

model <- spark.glm(outcome ~ pregnant + glucose + pressure + triceps + insulin + pedigree + age,family = "binomial", maxIter=100, data = df) 

Examining the output:

You can observe the coefficients of the model in the Estimate column. You can also see that the residuals range from -2.54 to +2.40, which encompasses about 2.5 standard devations, and that the median value (not the mean) is supplied, which is -.326....