datanalysis-credit-risk

name datanalysis-credit-risk description Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing. 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