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

Running an alternative model in Python

In this example, we ran a decision tree in R by extracting a sample from the Spark dataframe and running the tree model using base R. While that is perfectly acceptable (since it forced you to think about sampling), in many instances it would be more efficient to run the models directly on the Spark dataframe using a MLlib package or equivalent.

For the version of Spark, you should be working with (2.1); decision tree algorithms are not available to be run under R. Fortunately, native Spark decision trees are already implemented in Python and Scala. We will illustrate the example using Python so that you can see that there are options available. If you will be following algorithm development in Spark you will find that often algorithms are written first in Scala, since that is the native Spark language.

Running a Python Decision Tree

Here are some notes on the Python decision tree code which appears below.

For the first code chunk, notice the "magic" directive...