Book Image

Mastering Predictive Analytics with R - Second Edition

By : James D. Miller, Rui Miguel Forte
Book Image

Mastering Predictive Analytics with R - Second Edition

By: James D. Miller, Rui Miguel Forte

Overview of this book

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. The book begins with a dedicated chapter on the language of models and the predictive modeling process. You will understand the learning curve and the process of tidying data. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real-world datasets. How do you train models that can handle really large datasets? This book will also show you just that. Finally, you will tackle the really important topic of deep learning by implementing applications on word embedding and recurrent neural networks. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real- world datasets and mastered a diverse range of techniques in predictive analytics using R.
Table of Contents (22 chapters)
Mastering Predictive Analytics with R Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Dimensionality Reduction
Index

Predicting credit scores


In this section, we will explore another dataset, this time in the field of banking and finance. The particular dataset in question is known as the German Credit Dataset and is also hosted by the UCI Machine Learning Repository. The link to the data is https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29.

The observations in the dataset are loan applications made by individuals at a bank. The goal of the data is to determine whether an application constitutes a high credit risk.

Column name

Type

Definition

checking

Categorical

The status of the existing checking account

duration

Numerical

The duration in months

creditHistory

Categorical

The applicant's credit history

purpose

Categorical

The purpose of the loan

credit

Numerical

The credit amount

savings

Categorical

Savings account/bonds

employment

Categorical

Present employment since

installmentRate

Numerical

The installment rate (as a percentage of disposable...