{"id":11666,"date":"2019-07-31T10:27:53","date_gmt":"2019-07-31T14:27:53","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=11666"},"modified":"2023-04-05T12:19:48","modified_gmt":"2023-04-05T16:19:48","slug":"machine-learning-credit-modelling-in-r","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/machine-learning-credit-modelling-in-r\/","title":{"rendered":"Machine Learning Logistic Regression for Credit Modelling in R"},"content":{"rendered":"\n<p>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.<\/p>\n\n\n\n<p><em>Visit DataScience+ Blog to read the <a href=\"https:\/\/datascienceplus.com\/\">Introduction to Credit Modelling<\/a> in this article. The post here will continue with Credit Modelling in R and the sample R code.<\/em><\/p>\n\n\n\n<p>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. (<em>To<\/em> d<em>ownload the R code visit the author&#8217;s blog<\/em> here: https:\/\/datascienceplus.com\/machine-learning-logistic-regression-for-credit-modelling-in-r\/)<\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#fcfcdb;font-size:10px\">#Load R packages into the library<br>#Data management packages<br> library(DescTools)<br> library(skimr)<br> library(plyr)<br> library(dplyr)<br> library(aod)<br> library(readxl)<br><br>#Visualization packages<br>library(Deducer)<br>library(ggplot2)<br><br>#Machine learnning method packages<br>library(ROCR)<br>library(pROC)<br>library(caret)<br>library(MASS)<\/p>\n\n\n\n<p>Now it is time to load the dataset and do some data management. We will work with the&nbsp;<a href=\"https:\/\/www.kaggle.com\/wendykan\">loan lending club dataset<\/a>. The below coding is the data management:<\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#fcfcdb;font-size:10px\">#Import dataset<br>loan_data &lt;- read.csv(&#8220;\/loan.csv&#8221;)<br><br>#Selecting the relevant variables in the dataset:<br><br>loan_data &lt;- loan_data[,c(&#8220;grade&#8221;,&#8221;sub_grade&#8221;,&#8221;term&#8221;,&#8221;loan_amnt&#8221;,&#8221;issue_d&#8221;,&#8221;loan_status&#8221;,&#8221;emp_length&#8221;,<br>                           &#8220;home_ownership&#8221;, &#8220;annual_inc&#8221;,&#8221;verification_status&#8221;,&#8221;purpose&#8221;,&#8221;dti&#8221;,<br>                           &#8220;delinq_2yrs&#8221;,&#8221;addr_state&#8221;,&#8221;int_rate&#8221;, &#8220;inq_last_6mths&#8221;,&#8221;mths_since_last_delinq&#8221;,<br>                           &#8220;mths_since_last_record&#8221;,&#8221;open_acc&#8221;,&#8221;pub_rec&#8221;,&#8221;revol_bal&#8221;,&#8221;revol_util&#8221;,&#8221;total_acc&#8221;)]<br><br>#Data management for missing observations<br>loan_data$mths_since_last_delinq[is.na(loan_data$mths_since_last_delinq)] &lt;- 0<br> loan_data$mths_since_last_record[is.na(loan_data$mths_since_last_record)] &lt;- 0<br> var.has.na &lt;- lapply(loan_data, function(x){any(is.na(x))})<br> num_na &lt;- which( var.has.na == TRUE )    <br> per_na &lt;- num_na\/dim(loan_data)[1] <br> loan_data &lt;- loan_data[complete.cases(loan_data),]<\/p>\n\n\n\n<p>Although this is the second step of a credit modelling analysis, the visualization step can be found in&nbsp;<a href=\"https:\/\/datascienceplus.com\/\">my previous article<\/a>.  The below code is the visualization: <\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#fcfcdb;font-size:10px\">#Visualization of the data <br>#Bar chart of the loan amount<br><br>loanamount_barchart &lt;- ggplot(data=loan_data, aes(loan_data$loan_amnt)) + <br>   geom_histogram(breaks=seq(0, 35000, by=1000), <br>                  col=&#8221;black&#8221;, aes(fill=..count..)) +<br>   scale_fill_gradient(&#8220;Count&#8221;, low=&#8221;green1&#8243;, high=&#8221;yellowgreen&#8221;)+<br>   labs(title=&#8221;Loan Amount&#8221;, x=&#8221;Amount&#8221;, y=&#8221;Number of Loans&#8221;)<br> loanamount_barchart<br> ggplotly(p = ggplot2::last_plot())<br><br>#Box plot of loan amount<br>box_plot_stat &lt;- ggplot(loan_data, aes(loan_status, loan_amnt))<br> box_plot_stat + geom_boxplot(aes(fill = loan_status)) +<br>   theme(axis.text.x = element_blank()) +<br>   labs(list(title = &#8220;Loan amount by status&#8221;, x = &#8220;Loan Status&#8221;, y = &#8220;Amount&#8221;))<br> ggplotly(p = ggplot2::last_plot())<\/p>\n\n\n\n<p> The above coding gives us the following two visualizations:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"554\" height=\"439\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-1.png\" alt=\"\" class=\"wp-image-11669 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-1.png 554w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-1-400x317.png 400w\" data-sizes=\"(max-width: 554px) 100vw, 554px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 554px; aspect-ratio: 554\/439;\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"554\" height=\"439\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-2.png\" alt=\"\" class=\"wp-image-11670 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-2.png 554w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2019\/07\/Kristian-Larsen-Machine-Learning-2-400x317.png 400w\" data-sizes=\"(max-width: 554px) 100vw, 554px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 554px; aspect-ratio: 554\/439;\" \/><\/figure>\n\n\n\n<p><em>To see some descriptive statistics of the data, see the full article here: <br><\/em>https:\/\/datascienceplus.com\/machine-learning-logistic-regression-for-credit-modelling-in-r\/  <\/p>\n\n\n\n<p><em>About the Author:<br>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.<\/em> <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kristian Larsen walks us through a machine learning logistic regression for credit modelling. He codes in R using data management, visualization and machine learning packages.<\/p>\n","protected":false},"author":163,"featured_media":11671,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,341,351,344,342],"tags":[2532,806,2535,2533,852,2534,487,508,2536],"contributors-categories":[13666],"class_list":{"0":"post-11666","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-quant-development","11":"category-quant-europe","12":"category-quant-regions","13":"category-r-development","14":"tag-caret","15":"tag-data-science","16":"tag-dplyr","17":"tag-ggplot2","18":"tag-machine-learning","19":"tag-plyr","20":"tag-r","21":"tag-rstudio","22":"tag-visualization","23":"contributors-categories-datascience"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- 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