- Preface
- Introduction
- 1. Set up
-
2. Preliminary Analysis
- 2.1 Clean Data
- 2.2 Descriptive Analysis
- 2.3 Weight of Evidence and Information Value
- 2.4 Simple Logit Models
- 2.5 Pre-select Predictors
- 2.6 Correlations Amongst Predictors
-
3. Predictive Analysis
- 3.1 Model Selection Using the Best Subsets Algorithm
- 3.2 Backtesting
- 3.3 Performance Curves
- 3.4 Cross Validation
- 3.5 Final Model
- Conclusion
Score Personal Loan Applicants using R
A Step-by-Step Guide to Doing Predictive Analysis Using Logistic Regression and R
This book teaches you how to use data analysis and machine learning to predict bad loan customers based on their applications and demographic data. It is a step-by-step guide, starting from data cleaning, descriptive and exploratory analysis, data visualization, and finishing at model building and backtesting. Each step is accompanied by a set of R code that are ready to use in your own projects with no or little modifications.
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About
About the Book
This book is for you if
- you've been building scorecards using SAS or SPSS and want to do the same thing in R, or
- you want a step-by-step guide to building and backtesting a binary classifier using logistic regression in R, or
- you want to learn how to automate the model selection process using the best subset mehtod, or
- you want to understand the differences amongst the 6 performance measures: Accuracy, Sensitivity, False Positive Rate, Specificity, Precision, and the F-measure, and when to use which.
- you want to learn how to make the ROC curve and calculate the Aear Under the Curve (AUC) in R.
This book is not for you if
- you're looking for thorough understanding of the relavent statistical theories. This is a how-to book, not a why book, though intuitive reasons are pointed out whenever possible.
- you need to see math formulas. All formulas are presented directly in the form of R code.
- you're looking for an introductory R book. This book doesn't explain how R works.
Author
About the Author
Guangming Lang
Guangming Lang is the founder and Chief Data Scientist at Cabaceo LLC. He also teaches people how to do data analysis using the R programming language at MasterR.org, where you can receive high quality training materials for free. He used to work for FICO. He has a masters degree in biostatistics from the University of Michigan and a bachelor's degree in mathematics from the New College of Florida.
Contents
Table of Contents
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