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

Partitioning into training and test data

Next, we will generate test and training datasets so that we can validate any models produced. There are many ways of generating test and training sets.

In earlier chapters, we used the createDataPartition function. For this example, we will generate the test and training data using native R functions. Please refer to the outline of the code here, and then run the code that follows:

  • Set a variable corresponding to the percentage of the data to designate as training data (TrainingRows). In this example, we will use 75%.
  • Use the sample() function to randomize the rows and assign to a new dataframe named ChurnStudy.
  • Then select the first TrainingRows rows. Since the df dataframe has already been sampled, selecting a percentage of rows sequentially from a random sample is a convenient and valid way to select a training sample.
  • The remaining rows (TrainingRows+1 to the end) will be the testing dataset. Assign it to ChurnStudy.test.

Once we have generated the...