datanalysis-credit-risk

oleh github

Pipeline pembersihan data risiko kredit dan penyaringan variabel untuk pemodelan pra-pinjaman. Menjalankan 11 langkah independen yang mencakup pemuatan data, penyaringan periode abnormal, analisis tingkat missing, penghapusan variabel dengan IV rendah dan PSI tinggi, denoising null importance, serta eliminasi fitur berbasis korelasi. Mendukung analisis tingkat organisasi dengan penanganan sampel pemodelan terpisah dan sampel out-of-sample (OOS), serta akselerasi multi-proses untuk perhitungan IV dan PSI. Menghasilkan laporan Excel komprehensif dengan 15...

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

Unduh ZIP GitHub

35.8k

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