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https://github.com/gaizkiaadeline/credit-card-approval-prediction
Credit Card Approval Prediction using Soft Voting, Hard Voting, ADABoost, and XGBoost
https://github.com/gaizkiaadeline/credit-card-approval-prediction
adaboost machine-learning xgboost
Last synced: 1 day ago
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Credit Card Approval Prediction using Soft Voting, Hard Voting, ADABoost, and XGBoost
- Host: GitHub
- URL: https://github.com/gaizkiaadeline/credit-card-approval-prediction
- Owner: gaizkiaadeline
- Created: 2024-07-26T15:38:40.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-22T05:01:47.000Z (17 days ago)
- Last Synced: 2024-10-23T07:20:03.899Z (16 days ago)
- Topics: adaboost, machine-learning, xgboost
- Language: Jupyter Notebook
- Homepage:
- Size: 3.03 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Credit Card Approval Prediction
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
