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

About this chapter/what you will learn

In the previous chapters, we introduced Spark and SparkR, with the emphasis on exploring data using SQL. In this chapter, we will begin to look at the machine learning capabilities of Spark using MLlib, which is the native machine learning library which is packaged with Spark.

In this chapter we will cover logistic regression, and clustering algorithms. In the next chapter we will cover rule based algorithm, which include decision trees. Some of the material has already been discussed in prior chapters using PC versions of R. In this chapter, as well as the next, we will focus predominantly on how to prepare your data and apply these techniques using the MLLib algorihms which exist in Spark.

Reading the data

In the last chapter, we saved the out_sd to an external parquet file. In the real world, you will be faced with analyzing multiple data sources. Often, these data sources will have similar schemas but will differ by the time period that they were written...