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

Contrasting histograms

Histograms are also a quick way to visually inspect and compare outcome variables.

Here is another example of using the Spark histogram function to contrast the mean values of body mass index for diabetic versus non-diabetic patients in the study. For the first bar chart, we can see a peak bar of about 38.9 BMI, versus a peak bar of 29.8 for non-diabetic patients. This suggests that BMI will be an important variable in any model we develop:

This code uses the SparkR histogram function to compute a histogram with 10 bins. The centroids gives the center value for each of the 10 bins. The most frequently occurring bar is the bar with a center value of 38.9 with a count of about 50,000. This type of histogram is useful for quickly getting a sense of the distribution of variables, but is somewhat lacking in labeling, and controlling various elements since as scales and ranges. If you wish to fine tune some of the elements you may want to start by using a collect() function...