Authors :
Mohammad Haseeb Dar; Neerendra Kumar
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3A0g1NE
DOI :
https://doi.org/10.5281/zenodo.6987858
Abstract :
As we all know, Every day, commercial banks get
a large number of credit card applications. Many of them are
turned down for a variety of reasons, including large loan
amounts, insufficient income, or too many queries on a
person's credit record. Manually assessing these programs is
tedious, time-consuming, and error-prone. Fortunately,
machine learning can automate this operation, and almost
every commercial bank does it nowadays. In this paper, we
have used machine learning techniques to create a prediction
system for automated credit card approvals, much like actual
banks do.
Keywords :
Machine Learning, Credit Cards, Logistic Regression
As we all know, Every day, commercial banks get
a large number of credit card applications. Many of them are
turned down for a variety of reasons, including large loan
amounts, insufficient income, or too many queries on a
person's credit record. Manually assessing these programs is
tedious, time-consuming, and error-prone. Fortunately,
machine learning can automate this operation, and almost
every commercial bank does it nowadays. In this paper, we
have used machine learning techniques to create a prediction
system for automated credit card approvals, much like actual
banks do.
Keywords :
Machine Learning, Credit Cards, Logistic Regression
