This project focuses on Credit Card Approval Prediction using a dataset from Kaggle. The goal is to predict whether a credit card application will be approved based on applicant data. The project utilizes data preprocessing techniques like encoding categorical variables, handling imbalanced data using SMOTE (Synthetic Minority Over-sampling Technique), and building classification models to evaluate the prediction performance. Various models like Soft Voting, Hard Voting, AdaBoost, and XGBoost are used, and the final model achieved an F1 score of 82% with XGBoost.
Dataset Link : https://www.kaggle.com/datasets/rikdifos/credit-card-approval-prediction
