datanalysis-credit-risk
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
Profile →npx skills add github/awesome-copilot --skill datanalysis-credit-risk --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Cleaning and Variable Screening ## Quick Start ```bash # Run the complete data cleaning pipeline python ".github/skills/datanalysis-credit-risk/scripts/example.py" ``` ## Complete Process Description The data cleaning pipeline consists of the following 11 steps, each executed independently without deleting the original data: 1. **Get Data** - Load and format raw data 2. **Organization Sample Analysis** - Statistics of sample count and bad sample rate for each organization 3. **Separate OOS Data** - Separate out-of-sample (OOS) samples from modeling samples 4. **Filter Abnormal Months** - Remove months with insufficient bad sample count or total sample count 5. **Calculate Missing Rate** - Calculate overall and organization-level missing rates for each feature 6. **Drop High Missing Rate Features** - Remove features with overall missing rate exceeding threshold 7. **Drop Low IV Features** - Remove features with overall IV too low or IV too low in too many organizations 8. **Drop High PSI Features** - Remove features with unstable PSI 9. **Null Importance Denoising** - Remove noise features using label permutation method 10. **Drop High Correlation Features** - Remove high cor
- Quick Start
- Complete Process Description
- Core Functions
- Parameter Description
- Data Loading Parameters
- OOS Organization Configuration
- Abnormal Month Filtering Parameters
- Missing Rate Parameters
- IV Parameters
- PSI Parameters
- Null Importance Parameters
- High Correlation Parameters
- Output Report
- Features
Run the complete data cleaning pipeline python ".github/skills/datanalysis-credit-risk/scripts/example.py"
What does the datanalysis-credit-risk skill do?
Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.
How do I install it?
Run `npx skills add github/awesome-copilot --skill datanalysis-credit-risk --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.