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

Reading the table

Once you have completed the preceding steps, the table you have just created will be registered in the databricks system and will remain persistent across sessions, i.e you will not need to reload the data every time you login.

Running the first cell

Begin by running the first cell (also referred to as a code chunk), which simply will get a count of the number of records by year. You can access the code in this chapter by downloading it from the book's site. Alternatively, you can copy each section of the following code into a new cell and create your own notebook that way.

Since stop frisk has been imported and has already been registered as a table, we can begin to use SQL to read it some of the counts in order to see how large the file is:

#embed all SQL within the sql() function

yr <- sql("SELECT year,frisked,count(*) as year_cnt FROM stopfrisk group by year,frisked") 

After a few seconds, the output will appear as a simple formatted table. A simple calculation...