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Mastering Predictive Analytics with R

You're reading from   Mastering Predictive Analytics with R Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts

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Product type Paperback
Published in Jun 2015
Publisher
ISBN-13 9781783982806
Length 414 pages
Edition 1st Edition
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Toc

Table of Contents (13) Chapters Close

Preface 1. Gearing Up for Predictive Modeling 2. Linear Regression FREE CHAPTER 3. Logistic Regression 4. Neural Networks 5. Support Vector Machines 6. Tree-based Methods 7. Ensemble Methods 8. Probabilistic Graphical Models 9. Time Series Analysis 10. Topic Modeling 11. Recommendation Systems Index

Predicting credit scores


In this section, we will explore another data set; this time, in the field of banking and finance. The particular data set 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 data set 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 income...

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