Data Cleaning and Variable Screening
OfficialFreeStreamline credit risk data preprocessing for modeling.
Free · Opens the source repo
What Data Cleaning and Variable Screening does
The Data Cleaning and Variable Screening skill provides a comprehensive pipeline specifically designed for preparing raw credit data for pre-loan modeling. This skill is essential for data scientists and analysts who need to ensure that their datasets are of high quality before applying machine learning models. The pipeline consists of 11 distinct steps, each focusing on a critical aspect of data cleaning and variable selection, allowing users to maintain the integrity of their original data while performing necessary transformations.
The process begins with data loading and formatting, followed by an analysis of sample statistics for each organization. It includes filtering out-of-sample data, removing months with insufficient data, and calculating missing rates at both overall and organization levels. The skill then systematically eliminates features based on high missing rates, low Information Value (IV), and high Population Stability Index (PSI), ensuring that only the most relevant variables are retained for modeling. Additionally, it employs techniques for denoising and correlation checks, further refining the dataset.
At the end of the pipeline, users receive a detailed Excel report summarizing the cleaning process, including statistics and conditions for each step. This report serves as a valuable reference for understanding the data quality and the rationale behind variable selection. Overall, this skill is tailored for those working in finance or data analysis who require a robust approach to preparing credit risk data for predictive modeling.
When to use it
Use this skill when you have raw credit data that requires extensive preprocessing before applying machine learning models.
When not to use it
This skill is not suitable for datasets that are already clean or for scenarios that do not involve credit risk analysis.
What you can build with it
Preparing Credit Data for Modeling
Use this skill to preprocess raw credit data, ensuring it meets quality standards before modeling.
Analyzing Missing Data
Employ this skill to conduct a thorough missing value analysis and remove problematic features.
Generating Cleaning Reports
Leverage the skill to generate comprehensive reports that document the data cleaning process and outcomes.
How to install Data Cleaning and Variable Screening
View source1. Install with the skills CLI
npx skills add github/awesome-copilot/datanalysis-credit-risk --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by githubData 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 nameY_COL: Label column nameORG_COL: Organization column nameKEY_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
Frequently asked questions about Data Cleaning and Variable Screening
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