There is a main goal of this research, which is the application of machine learning tools and methods with the aim of correctly predicting the likelihood of credit cards’ applications approval. Therefore, managing and analyzing the applicant data should result in the development of a model that helps to make efficient credit decisions.
Our methodology includes:
- Data Collection: Doing a survey and compiling a set of credit card applications and other attributes of the applicants as well as the outcome of the approval for the application.
- Data Preprocessing: Managing missing values for arrays, Nameless data and converting categorical information into numerical form, standardizing numerical variables.
- Model Development:Creating a predictive model of the logistic regression type for carrying out a forecast of credit card application approvals.
- Model Optimization:Optimising parameters of the model using the methods such as grid search and cross-validation.
- Model Evaluation: To optimize the use of the K-nearest neighbor model, cross- validation and grid search were used to obtain the best parameters of the model
It is apparent from the outcomes that the overall accuracy of the credit card approval forecasts in the optimised logistic regression model is quite high. What has been described in this paper is a promising predictive model that can help financial institution to have an efficient approval process, as well as significantly reduce the chances of people defaulting on their loans. The study also focuses on features like machine learning to enhance the quality of financial decision, the enhancement of business operations.
