Agent skill · data analytics · github
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.
Why this skill is useful
Adds a complete executable data cleaning pipeline with specific functions for credit risk analysis that are not commonly found in public resources.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
Data Cleaning and Variable Screening Quick Start 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 correlation features based on original gain 11. Export Report - Generate Excel report containing details and statistics of all steps Core Functions Function Purpose Module ------ ------ ---------- getdataset() Load and format data references.func organalysis() Organization sample analysis references.func missingcheck() Calculate missing rate references.func dropabnormalym() Filter abnormal months references.analysis drophighmissfeatures() Drop high missing rate features references.analysis droplowivfeatures() Drop low IV features references.analysis drophighpsifeatures() Drop high PSI features references.analysis drophighnoisefeatures() Null Importance denoising references.analysis drophighcorrfeatures() Drop high correlation features references.analysis ivdistributionbyorg() IV distribution statistics references.analysis psidistributionbyorg() PSI distribution statistics references.analysis valueratiodistributionbyorg() Value ratio distribution statistics r …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/datanalysis-credit-risk