datanalysis-credit-risk - Claude MCP Skill

# Data Cleaning and Variable Screening

## Quick Start

```bash
# 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