---
title: "From Raw Data to Risk Classes"
url: https://daily.dev/posts/from-raw-data-to-risk-classes-qye2zmgfs
source_url: https://towardsdatascience.com/from-raw-data-to-risk-classes
type: article
source: "Towards Data Science"
published: 2026-05-15T17:09:59.766Z
updated: 2026-05-15T17:10:22.550Z
tags: ["python", "feature-engineering", "logistic-regression"]
reading_time: 26
upvotes: 1
comments: 0
language: en
---

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# From Raw Data to Risk Classes

**[Towards Data Science](https://daily.dev/sources/tds)** · 26 min read · 1 upvotes · 0 comments

## Summary

A practical guide to variable categorization (binning) in credit scoring model development. Covers why categorization matters for both categorical and continuous variables, including dimensionality reduction, capturing non-linear risk patterns, outlier handling, missing value treatment, and model stability. Explains graphical monotonicity analysis using equal-frequency binning, then details supervised methods including Chi-square-based grouping and Weight of Evidence (WoE)-based grouping. Includes Python implementations for computing WoE/IV, plotting default rate curves over time, and combined bar/line plots for category analysis. Demonstrates the full workflow using variables like person_income and loan_int_rate, emphasizing that binning must be statistically sound, business-coherent, and stable across train/test/out-of-time datasets.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://towardsdatascience.com/from-raw-data-to-risk-classes>

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---

Tags: [#python](https://daily.dev/tags/python), [#feature-engineering](https://daily.dev/tags/feature-engineering), [#logistic-regression](https://daily.dev/tags/logistic-regression)

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