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datanalysis-credit-risk

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

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.

github/awesome-copilot

연동 도구Excel

설치 방법 보기

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Prepare credit data before building a model
Compare missing values and bad-sample rates across organizations
Screen variables for usefulness, stability, and redundancy
Review cleaning results in an Excel report

포함된 파일(4)

SKILL.md6.2 KB
references/analysis.py52.6 KB
references/func.py8.5 KB
scripts/example.py17.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Data Cleaning and Variable Screening

Quick Start

# 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:

Get Data - Load and format raw data
Organization Sample Analysis - Statistics of sample count and bad sample rate for each organization
Separate OOS Data - Separate out-of-sample (OOS) samples from modeling samples
Filter Abnormal Months - Remove months with insufficient bad sample count or total sample count
Calculate Missing Rate - Calculate overall and organization-level missing rates for each feature
Drop High Missing Rate Features - Remove features with overall missing rate exceeding threshold
Drop Low IV Features - Remove features with overall IV too low or IV too low in too many organizations
Drop High PSI Features - Remove features with unstable PSI
Null Importance Denoising - Remove noise features using label permutation method
Drop High Correlation Features - Remove high correlation features based on original gain
Export Report - Generate Excel report containing details and statistics of all steps

Core Functions

Function	Purpose	Module
`get_dataset()`	Load and format data	references.func
`org_analysis()`	Organization sample analysis	references.func
`missing_check()`	Calculate missing rate	references.func
`drop_abnormal_ym()`	Filter abnormal months	references.analysis
`drop_highmiss_features()`	Drop high missing rate features	references.analysis
`drop_lowiv_features()`	Drop low IV features	references.analysis
`drop_highpsi_features()`	Drop high PSI features	references.analysis
`drop_highnoise_features()`	Null Importance denoising	references.analysis
`drop_highcorr_features()`	Drop high correlation features	references.analysis
`iv_distribution_by_org()`	IV distribution statistics	references.analysis
`psi_distribution_by_org()`	PSI distribution statistics	references.analysis
`value_ratio_distribution_by_org()`	Value ratio distribution statistics	references.analysis
`export_cleaning_report()`	Export cleaning report	references.analysis

Parameter Description

Data Loading Parameters

DATA_PATH: Data file path (best are parquet format)
DATE_COL: Date column name
Y_COL: Label column name
ORG_COL: Organization column name
KEY_COLS: Primary key column name list

OOS Organization Configuration

OOS_ORGS: Out-of-sample organization list

Abnormal Month Filtering Parameters

min_ym_bad_sample: Minimum bad sample count per month (default 10)
min_ym_sample: Minimum total sample count per month (default 500)

Missing Rate Parameters

missing_ratio: Overall missing rate threshold (default 0.6)

IV Parameters

overall_iv_threshold: Overall IV threshold (default 0.1)
org_iv_threshold: Single organization IV threshold (default 0.1)
max_org_threshold: Maximum tolerated low IV organization count (default 2)

PSI Parameters

psi_threshold: PSI threshold (default 0.1)
max_months_ratio: Maximum unstable month ratio (default 1/3)
max_orgs: Maximum unstable organization count (default 6)

Null Importance Parameters

n_estimators: Number of trees (default 100)
max_depth: Maximum tree depth (default 5)
gain_threshold: Gain difference threshold (default 50)

High Correlation Parameters

max_corr: Correlation threshold (default 0.9)
top_n_keep: Keep top N features by original gain ranking (default 20)

Output Report

The generated Excel report contains the following sheets:

汇总 - Summary information of all steps, including operation results and conditions
机构样本统计 - Sample count and bad sample rate for each organization
分离OOS数据 - OOS sample and modeling sample counts
Step4-异常月份处理 - Abnormal months that were removed
缺失率明细 - Overall and organization-level missing rates for each feature
Step5-有值率分布统计 - Distribution of features in different value ratio ranges
Step6-高缺失率处理 - High missing rate features that were removed
Step7-IV明细 - IV values of each feature in each organization and overall
Step7-IV处理 - Features that do not meet IV conditions and low IV organizations
Step7-IV分布统计 - Distribution of features in different IV ranges
Step8-PSI明细 - PSI values of each feature in each organization each month
Step8-PSI处理 - Features that do not meet PSI conditions and unstable organizations
Step8-PSI分布统计 - Distribution of features in different PSI ranges
Step9-null importance处理 - Noise features that were removed
Step10-高相关性剔除 - High correlation features that were removed

Features

Interactive Input: Parameters can be input before each step execution, with default values supported
Independent Execution: Each step is executed independently without deleting original data, facilitating comparative analysis
Complete Report: Generate complete Excel report containing details, statistics, and distributions
Multi-process Support: IV and PSI calculations support multi-process acceleration
Organization-level Analysis: Support organization-level statistics and modeling/OOS distinction

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설치

명령으로 설치

npx skills add https://github.com/github/awesome-copilot --skill datanalysis-credit-risk

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/github/awesome-copilot/tree/main/skills/datanalysis-credit-risk

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/datanalysis-credit-risk/, Codex는 ~/.agents/skills/datanalysis-credit-risk/ 에 배치해 주세요.

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GitHub에서 소스 보기

저장소: github/awesome-copilot
스타: 40,186
포크: 5,160
카테고리: 데이터·AI
라이선스: MIT
원문 언어: 영어
최종 업데이트: 2026년 10월 17일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.