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Machine Learning Logistic Regression for Credit Modelling in R

DataScience+

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Kristian Larsen

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Economic data scientist

Machine learning logistic regression is a widely popular method to model credit. There are excellent and efficient packages in R that can perform these types of analysis. Typically, you will first create different machine learning visualizations before you perform the machine learning logistic regression analysis.

Visit DataScience+ Blog to read the Introduction to Credit Modelling in this article. The post here will continue with Credit Modelling in R and the sample R code.

Now let us start using R for Credit Modelling. The first thing we need to do is to load the R packages into the library. (To download the R code visit the author’s blog here.)

#Load R packages into the library
#Data management packages
library(DescTools)
library(skimr)
library(plyr)
library(dplyr)
library(aod)
library(readxl)

#Visualization packages
library(Deducer)
library(ggplot2)

#Machine learnning method packages
library(ROCR)
library(pROC)
library(caret)
library(MASS)

Now it is time to load the dataset and do some data management. We will work with the loan lending club dataset. The below coding is the data management:

#Import dataset
loan_data <- read.csv(“/loan.csv”)

#Selecting the relevant variables in the dataset:

loan_data <- loan_data[,c(“grade”,”sub_grade”,”term”,”loan_amnt”,”issue_d”,”loan_status”,”emp_length”,
“home_ownership”, “annual_inc”,”verification_status”,”purpose”,”dti”,
“delinq_2yrs”,”addr_state”,”int_rate”, “inq_last_6mths”,”mths_since_last_delinq”,
“mths_since_last_record”,”open_acc”,”pub_rec”,”revol_bal”,”revol_util”,”total_acc”)]

#Data management for missing observations
loan_data$mths_since_last_delinq[is.na(loan_data$mths_since_last_delinq)] <- 0
loan_data$mths_since_last_record[is.na(loan_data$mths_since_last_record)] <- 0
var.has.na <- lapply(loan_data, function(x){any(is.na(x))})
num_na <- which( var.has.na == TRUE )
per_na <- num_na/dim(loan_data)[1]
loan_data <- loan_data[complete.cases(loan_data),]

Although this is the second step of a credit modelling analysis, the visualization step can be found in my previous article. The below code is the visualization:

#Visualization of the data
#Bar chart of the loan amount

loanamount_barchart <- ggplot(data=loan_data, aes(loan_data$loan_amnt)) +
geom_histogram(breaks=seq(0, 35000, by=1000),
col=”black”, aes(fill=..count..)) +
scale_fill_gradient(“Count”, low=”green1″, high=”yellowgreen”)+
labs(title=”Loan Amount”, x=”Amount”, y=”Number of Loans”)
loanamount_barchart
ggplotly(p = ggplot2::last_plot())

#Box plot of loan amount
box_plot_stat <- ggplot(loan_data, aes(loan_status, loan_amnt))
box_plot_stat + geom_boxplot(aes(fill = loan_status)) +
theme(axis.text.x = element_blank()) +
labs(list(title = “Loan amount by status”, x = “Loan Status”, y = “Amount”))
ggplotly(p = ggplot2::last_plot())

The above coding gives us the following two visualizations:

To see some descriptive statistics of the data, see the full article here:
https://datascienceplus.com/machine-learning-logistic-regression-for-credit-modelling-in-r/

About the Author:
Kristian Larsen is a passionate economic data scientist with an expertise in R, Excel, VBA, SQL, STATA, SAS and Python. He creates Automated dashboards, business intelligence, machine learning, data analysis, AI, deep learning, data management, statistical analysis and programming.

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