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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Confusion matrix for test group


The results for the test group are similar to those of the training group. Any discrepancies between test and training would warrant looking more closely at the model and observing how the data was sampled or split:

#Confusion matrix for TEST group 
result <- sql("select outcome,correct, count(*) as k, avg(totrows) as totrows from preds_tbl where grp=0 group by 1,2 order by 1,2") 
result$classify_pct <- result$k/result$totrows 
display(result)

Add up the correct calculation is a similar way to the training group. The results are slightly less, which is normal when comparing test to training results :

Summary of Correct Predictions for Test Group:

Correctly predicted outcome=1

22%

Correctly predicted outcome=0

52%

Total Correct Percentage

74%

Distribution of average errors by group

Distribution of errors is another that you can look at how well a model has fit the data. In this analysis, we look at the distribution of errors for all four combinations of the following...