Individual skill
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.
skills/datanalysis-credit-risk/SKILL.md
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- Nov 6, 2026
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SKILL.mdcore · 6,301 bytesSKILL.mdcore · 6,301 bytesSKILL.mdcore · 6,301 bytesreferences/analysis.pyruns · 53,911 bytesreferences/func.pyruns · 8,749 bytesscripts/example.pyruns · 17,643 bytes
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# 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