This project builds a robust credit risk classification model using machine learning techniques, specifically the XGBoost classifier. The notebook walks through a complete end-to-end pipeline — from preprocessing and SMOTE-based class balancing to hyperparameter optimization using Optuna. The goal is to predict the likelihood of loan default.
active 2025-06-28 → 2025-06-28 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 1 days from 2025-06-28 to 2025-06-28. Pushes: 5 total, peak 5 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| DaBestCode | 5 | 5 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.