Attribution: Sourced from github/awesome-copilot by GitHub Community, licensed MIT. Modified for this collection.

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