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

Indexing the classification features

Indexing is used to optimize data access and supply the parameters to specific machine learning algorithms in an acceptable format.

We will be incorporating the race variable into the decision tree model, so the first step is to determine what the different values of race are. We will do this by again using SQL to count the frequency by race. Notice we can say either "Group by Race" or "Group by 1" which is a shorthand reference to the first column specified in the select statement (which is race):

dfx = spark.sql("SELECT race,count(*) FROM stopfrisk group by 1")  

Observe that there are eight values, Q, B, U, Z, A, W, I, and P:

Next, use transform. This will map a string factor (race) to a numeric index (race_indexed):

indexer = StringIndexer(inputCol="race", outputCol="race_indexed") 
df3 = 
#drop race for the final dataframe
df4 = df3.drop("race") 

Look at the pairs...