Prediction of Credit Card Approval
Harsha Vardhan Peela1, Tanuj Gupta2, Nishit Rathod3, Tushar Bose4, Neha Sharma5

1Harsha Vardhan Peela, Department of Information Technology, Manipal University, Jaipur (Rajasthan), India. 
2Tanuj Gupta, Department of Information Technology, Manipal University, Jaipur (Rajasthan), India.
3Nishit Rathod, Department of Information Technology, Manipal University, Jaipur (Rajasthan), India.
4Tushar Bose, Department of Information Technology, Manipal University, Jaipur (Rajasthan), India. 
5Neha Sharma*, Department of Information Technology, Manipal University, Jaipur (Rajasthan), India.
Manuscript received on November 24, 2021. | Revised Manuscript received on December 02, 2021. | Manuscript published on January 30, 2022. | PP: 1-6 | Volume-11 Issue-2, January 2022. | Retrieval Number: 100.1/ijsce.B35350111222 | DOI: 10.35940/ijsce.B3535.0111222
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Credit risk as the board in banks basically centers around deciding the probability of a customer’s default or credit decay and how expensive it will end up being assuming it happens. It is important to consider major factors and predict beforehand the probability of consumers defaulting given their conditions. Which is where a machine learning model comes in handy and allows the banks and major financial institutions to predict whether the customer, they are giving the loan to, will default or not. This project builds a machine learning model with the best accuracy possible using python. First we load and view the dataset. The dataset has a combination of both mathematical and non-mathematical elements, that it contains values from various reaches, in addition to that it contains a few missing passages. We preprocess the dataset to guarantee the AI model we pick can make great expectations. After the information is looking great, some exploratory information examination is done to assemble our instincts. Finally, we will build a machine learning model that can predict if an individual’s application for a credit card will be accepted. Using various tools and techniques we then try to improve the accuracy of the model. This project uses Jupyter notebook for python programming to build the machine learning model. Using Data Analysis and Machine Learning, we attempted to determine the most essential parameters for obtaining credit card acceptance in this project. The machine learning model we built gave an 86 % accuracy for predicting whether the credit card will be approved or not, considering the various factors mentioned in the application of the credit card holder. Even though we achieved an accuracy of 86%, we conducted a grid search to see if we could increase the performance even further. However, using both the machine learning models: random forest and logistic regression, the best we could get from this data was 86 percent.
Keywords: First we load and view the dataset.