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

Subsetting the columns

For this exercise, we will be using a restricted set of columns from the CSV file. We can either select the specific columns from the dataframe just read in (if we just read in the whole file), or reread the csv file using the colClasses parameter to only read the columns that are required. Often, this method is preferable when you are reading a large file, and will instruct read.csv to only retain the first three and the last two columns, and ignore the columns priemp through govmilitary.

After rereading in the file, with a subset of the columns, we print a few records from the beginning and end of the file. We can do this using a combination of the rbind(), head(), and tail() functions. This will give us all of the columns we will be using for this chapter, except for some columns, which we will derive in the next section:

x <- read.csv("hihist2bedit.csv", colClasses = c(NA,NA, NA, NA, rep("NULL", 7)))

 rbind(head(x), tail(x)) 
>          Year Year.1 Total.People...