Jan 11, 2022 · We built a machine learning-based classifier that predicts if a credit card application will get approved or not, based on the information provided in the application.

This project is a Credit Card Approval Prediction system that uses machine learning techniques to evaluate credit card applications and predict whether an applicant is likely to be approved or rejected.

Enhance credit approval predictions using machine learning. Improve fairness and transparency in financial decision-making. Optimize feature selection to identify the most impactful applicant attributes. Reduce systematic bias in credit approvals.

This project focuses on developing a machine learning model to predict credit card approval based on customer information. By automating the approval process, banks can streamline operations and provide faster responses to customers, improving customer satisfaction and reducing manual review time.

Oct 29, 2024 · We trained three models to predict credit approval: Decision Tree, Logistic Regression, and Random Forest. Each model provided unique insights, but the Random Forest Classifier...

In this blog post, we will explore how to build a Multilayer Perceptron (MLP) model to predict credit card approval and, more importantly, explain the model’s predictions using LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations).

Jun 21, 2024 · Three machine learning algorithms, Random Forest, Logistic Regression, and Gradient Boosting are applied. Regularization techniques (L1 and L2) and hyperparameter tuning are used to prevent...

Oct 27, 2020 · In this project, we will be build an automatic credit card approval predictor using machine learning techniques, just like the real banks do. Importing the pandas package and loading the dataset. pandas : Pandas is used to read the dataset file and import it as a dataframe, which is similar to a table with rows and columns.

Dec 15, 2024 · Predicting credit card approvals is crucial for financial institutions to streamline decision-making and mitigate risks.

1 day ago · Of the two aforementioned data sets, the first one is taken from a major Greek bank whereas the second one is the Australian Credit Approval data set taken from the UCI Machine Learning Repository ...