datanalysis-credit-risk

The data cleaning pipeline consists of the following 11 steps, each executed independently without deleting the original data:

| getdataset() | Load and format data | references.func | | organalysis() | Organization sample analysis | references.func | | missingcheck() | Calculate missing rate | references.func | | dropabnormalym() | Filter abnormal months | references.analysis | | drophighmissfeatures() | Drop high missing rate features | references.analysis |

| droplowivfeatures() | Drop low IV features | references.analysis | | drophighpsifeatures() | Drop high PSI features | references.analysis | | drophighnoisefeatures() | Null Importance denoising | references.analysis | | drophighcorrfeatures() | Drop high correlation features | references.analysis |

用于贷前建模的信用风险数据清理和变量筛选管道。在处理建模前需要质量评估、缺失值分析或变量选择的原始信用数据时使用。它涵盖了数据加载和格式化、异常周期过滤、缺失率计算、高缺失变量去除、低IV变量过滤、高PSI变量去除、Null Importance去噪、高相关变量去除和清理报告生成。适用场景有信用风险数据清洗、变量筛选、贷前建模预处理。 来源：github/awesome-copilot。