SkillRepo
Sign InSign Up
Catalog
github/datanalysis-credit-risk

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.

v1.0Latest
New~1.5kUpdated Jun 26, 2026

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
Files4
4 files · 79.5 KB

Select a file to preview

Overall Score

76/100

Grade

B

Good

Safety

78

Quality

74

Clarity

79

Completeness

71

Summary

A credit risk data cleaning and variable screening pipeline for pre-loan modeling. It guides agents through 11 independent processing steps—from raw data loading and abnormal period filtering to advanced feature selection via IV, PSI, null importance, and correlation analysis—with interactive parameter input and comprehensive Excel report generation. All steps operate non-destructively, preserving original data.

Detected Capabilities

file read (parquet, csv, xlsx, pkl)file write (xlsx report generation)data processing (pandas/numpy)statistical analysis (IV, PSI, correlation)machine learning model training (LightGBM)multi-process parallel computationinteractive user input (command-line parameters)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

credit risk data cleaningfeature screening pre-modelmissing rate filteringIV PSI analysisvariable selection pipelinedata quality reportnull importance denoising

Risk Signals

WARNING

Interactive input for DATA_PATH with no validation

scripts/example.py:line ~95
INFO

User input converted to int/float without error handling in certain branches

scripts/example.py:get_user_input() function
INFO

LightGBM training with no input validation (X could contain invalid data)

references/analysis.py:drop_highnoise_features()
INFO

N_JOBS set to CPU_COUNT - 1, acceptable for compute-intensive tasks

scripts/example.py:line ~27
INFO

Default OOS organization list hardcoded ('orgA', 'orgB', etc.) — users can override interactively

scripts/example.py:line ~78

Use Cases

  • Clean raw credit risk data for pre-modeling
  • Identify and remove high-missing-rate variables
  • Filter unstable features using IV and PSI thresholds
  • Remove noise features via label-permutation denoising
  • Eliminate redundant features based on correlation and gain
  • Generate comprehensive data quality and feature selection reports
  • Perform organization-level and month-level feature stability analysis

Quality Notes

  • ✓ Well-structured 11-step pipeline with clear separation of concerns
  • ✓ Comprehensive documentation of each step's purpose and output
  • ✓ Interactive parameter input with sensible defaults for all thresholds
  • ✓ Multi-process support documented and configurable (N_JOBS)
  • ✓ Excel report generation with 15 detailed sheets covering all steps
  • ✓ Functions are modular and independently executable
  • ✗ Input validation is minimal (DATA_PATH, column names not verified before use)
  • ✗ Error handling in some core functions (calculate_iv, drop_highnoise_features) relies on try-except silently dropping failed features
  • ✗ Missing docstrings for exported functions like drop_highpsi_features (referenced but implementation truncated)
  • ✗ No explicit bounds checking on interactive parameters (e.g., max_corr could be >1)
  • ✗ Assumes column names follow pattern (i_ prefix for features) without documentation
  • ✗ Example.py hardcodes default paths and OOS orgs—users must provide these interactively or modify code
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

Reviews

Add this skill to your library to leave a review.

No reviews yet

Be the first to share your experience.

Use github/datanalysis-credit-risk in your dev environment

Start a Developer trial

Command Palette

Search for a command to run...