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

Exploring the hospital dataset

Exploratory data analysis is a preliminary step prior to data modeling in which you look at all of the characteristics of data in order t0 get a sense of data distribution, correlation, missing values, outliers, and any other factors that might impact future analyses. It is a very important step, and if performed diligently, will save you a lot of time later on.

For the following examples, we will read the NYC hospital discharges dataset (hospital inpatient discharges (SPARCS De-Identified): 2012, n.d.). This example uses the read.csv function to input the delimited file, and then uses the View function to graphically display the output. Then the str function is used to display the contents of the df dataframe that was just created, and then finally, the summary() function displays all of the relevant statistics on all of the variables. These are all typical first steps to perform when looking at data for the first time:

df <-read.csv("C:/PracticalPredictiveAnalytics...