aysecnkci/banking-risk-analysis-imbalanced-data
The project focuses on handling imbalanced data using techniques like RandomUnderSampler and TomekLinks, while exploring various models such as CART, Random Forest, GBM, and LightGBM. The BalancedRandomClassifier, optimized through hyperparameter tuning, achieved an 80% recall on high-risk customers with an accuracy of 74%.
No commits in the last 6 months.
Stars
—
Forks
—
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Sep 10, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aysecnkci/banking-risk-analysis-imbalanced-data"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
xRiskLab/xBooster
Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into...
ing-bank/skorecard
scikit-learn compatible tools for building credit risk acceptance models
minerva-ml/open-solution-home-credit
Open solution to the Home Credit Default Risk challenge :house_with_garden:
semasuka/Credit-card-approval-prediction-classification
Credit risk analysis for credit card applicants
ParthS007/Loan-Approval-Prediction
Loan Application Data Analysis