himanshkr03/Loan_Default_Prediction_Using_Machine_Learning

This repository contains a Python-based project that uses machine learning to predict loan defaults. It explores data preprocessing, feature engineering, and model training techniques to build a predictive model for assessing loan risk.

11
/ 100
Experimental

No commits in the last 6 months.

Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 0 / 25
Maturity 11 / 25
Community 0 / 25

How are scores calculated?

Stars

—

Forks

—

Language

Jupyter Notebook

License

MIT

Category

credit-risk-modeling

Last pushed

Jan 02, 2025

Commits (30d)

0

GitHub
Credit Risk Modeling · 240 frameworks

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/himanshkr03/Loan_Default_Prediction_Using_Machine_Learning"

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...

57

ing-bank/skorecard

scikit-learn compatible tools for building credit risk acceptance models

55

minerva-ml/open-solution-home-credit

Open solution to the Home Credit Default Risk challenge :house_with_garden:

51

semasuka/Credit-card-approval-prediction-classification

Credit risk analysis for credit card applicants

50

ParthS007/Loan-Approval-Prediction

Loan Application Data Analysis

48

Explore ML Frameworks

All categories Trending ML Framework directory Insights