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/datanalysis-credit-risk

@0ea5aa1 official
by githubgithub/awesome-copilot40k stars
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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.

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Use this Skill: https://skilld.dev/gh/github/awesome-copilot/datanalysis-credit-risk

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SKILL.md

≈153 tokens always: the name and description. ≈1.4k when used: this file.

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:

  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
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:

  1. 汇总 - Summary information of all steps, including operation results and conditions
  2. 机构样本统计 - Sample count and bad sample rate for each organization
  3. 分离OOS数据 - OOS sample and modeling sample counts
  4. Step4-异常月份处理 - Abnormal months that were removed
  5. 缺失率明细 - Overall and organization-level missing rates for each feature
  6. Step5-有值率分布统计 - Distribution of features in different value ratio ranges
  7. Step6-高缺失率处理 - High missing rate features that were removed
  8. Step7-IV明细 - IV values of each feature in each organization and overall
  9. Step7-IV处理 - Features that do not meet IV conditions and low IV organizations
  10. Step7-IV分布统计 - Distribution of features in different IV ranges
  11. Step8-PSI明细 - PSI values of each feature in each organization each month
  12. Step8-PSI处理 - Features that do not meet PSI conditions and unstable organizations
  13. Step8-PSI分布统计 - Distribution of features in different PSI ranges
  14. Step9-null importance处理 - Noise features that were removed
  15. 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

Source: SKILL.md on GitHub

Third-party checks

2 warnings16d5 checks · Risk MEDIUM
  • Gen Agent Trust Hub16d

    The skill provides a credit risk data cleaning and variable screening pipeline. A security concern was identified regarding unsafe deserialization when loading dataset files, as the utility attempts to parse user-provided file paths using Python pickle deserialization.

  • Socket16d

    1 alert: gptAnomaly

  • Snyk16d

    Risk: LOW · No issues

  • Runlayer7mo

    2/4 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at 0ea5aa1. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 7 months ago

Topics

  • Python
  • credit-risk
  • data-cleaning
  • feature-selection
  • pandas
  • preprocessing
  • iv
  • psi
  • missing-values
  • modeling

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README badge for github/awesome-copilot/datanalysis-credit-risk

What it does

Cleans and screens credit risk datasets for pre-loan modeling through a 11-step pipeline: missing value analysis, abnormal period filtering, low-IV and high-PSI variable removal, null importance denoising, and correlation filtering. Generates an Excel report with statistics at organization and feature levels for each step. Target use is preparing raw credit data for risk model development.

Generated from the current SKILL.md.

Frequently asked

What data formats does this skill support?
The skill accepts parquet format data (noted as best practice) and other formats via the DATA_PATH parameter. Data must include date, label, organization, and primary key columns.
Can I run individual cleaning steps without executing the full pipeline?
Yes. Each of the 11 steps executes independently without modifying the original data, so you can run specific steps in isolation for comparative analysis.
Does this skill support multi-organization credit data?
Yes. The skill includes organization-level analysis, organization-specific IV and PSI thresholds, out-of-sample (OOS) organization configuration, and organization-level statistics in the output report.
What does the output report include?
A multi-sheet Excel report covering all 11 steps: summary, sample statistics by organization, removed features at each stage, IV and PSI distributions, correlation analysis, and detailed metrics for filtering decisions.
Can I customize the filtering thresholds?
Yes. The skill exposes parameters for missing rate threshold, IV thresholds, PSI thresholds, correlation threshold, null importance gain threshold, and abnormal month sample count minimums.

Generated from the current SKILL.md. These answers refresh after source changes.