Research paper on The Lasso and Ridge Regression Models for Machine Learning for Credit Risk in the Reactive Peru Program

Description

Title: The Lasso and Ridge Regression Models for Machine Learning for Credit Risk in the Reactive Peru Program

Abstract: Due to COVID-19, there are now restrictions on the continuity of the payment chain, which has forced businesses to turn to credit access. This study sought to identify the most effective machine learning predictive model for the COVID-19-related credit risk of businesses participating in the Reactiva Peru Program. With a population of 501,298 companies receiving benefits from the program, a multivariate regression analysis was conducted using four regressor variables (economic sector, granting entity, amount covered, and department) and one predictor (risk level). This analysis was conducted using the CRISP-DM methodology, which is specifically designed for data mining projects. Artificial intelligence techniques were used under the machine learning Lasso and Ridge regression models. In comparison to the Ridge regression model (100 = 0.00910; RMSE = 0.3573812) and the least squares model using algebraic mathematics, the results showed that the Lasso regression model (60 = 0.00038; RMSE = 0.3573685) predicted the level of risk with the best accuracy. This confirms that the Lasso regression model is the best predictive model to detect the level of credit risk of the Reactiva Peru Program. The Lasso regression model is the most effective predictive model for determining the degree of corporate credit risk.

Keywords: Lasso model; Ridge model; credits; machine learning; credit risk

Paper Quality: SCOPUS / Web of Science Level Research Paper

Subject: Economics

Writer Experience: 20+ Years

Plagiarism Report: Turnitin Plagiarism Report will be less than 10%

Restriction: Only one author may purchase a single paper. The paper will then indicate that it is out of stock.

What will I get after the purchase?

A turnitin plagiarism report of less than 10% in a pdf file and a full research paper in a word document.

In case you have any questions related to this research paper, please feel free to call/ WhatsApp on +919726999915