datanalysis-credit-risk
Credit risk data cleaning and variable screening pipeline for pre-loan modeling.
- Executes 11 independent steps covering data loading, abnormal period filtering, missing rate analysis, low-IV and high-PSI variable removal, null importance denoising, and correlation-based feature elimination
- Supports organization-level analysis with separate modeling and out-of-sample (OOS) sample handling, plus multi-process acceleration for IV and PSI calculations
- Generates comprehensive Excel report with 15 sheets detailing operation results, feature statistics, distributions, and removed variables across all pipeline stages
- Configurable thresholds for missing rate, IV, PSI, correlation, and null importance parameters with sensible defaults
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:
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