Clean and screen credit data before modeling.
credit risk modeling
Cleans and screens raw credit-risk data for pre-loan modeling.
When to use it
Use it when raw credit data needs quality assessment, missing-value analysis, or variable selection before modeling.
Give it raw credit data and its column mappings; it accepts parameters before each step and produces a detailed Excel report.
What you provide
This skill
raw credit data file
Reads your raw credit data
Python runs the complete data-cleaning pipeline.
The processing functions import pandas for tabular data handling.
The processing functions import NumPy for missing-value and numeric operations.
The pipeline imports toad and uses its binning and IV-quality functions.