zeon 0xatm

1. Core Coding Principles

Language: All code, comments, variable names, function names, class names, and commit messages must be strictly in English.
Idiomatic Style:
- Write code that adheres to the idiomatic style, conventions, and official style guides of the target language. This includes formatting, naming, and general structure.
- Assume a linter or formatter will eventually run; aim to produce code that is already close to passing common linting rules.
Clarity and Readability:
- Prioritize clarity, readability, and maintainability over unnecessary "cleverness" or extreme brevity if it sacrifices understanding.
- Write self-documenting code.
Follow the Principle of Least Surprise: code should behave in a way that users and other developers would naturally expect.

<think>
Okay, let's see. The user wants me to analyze the given datasets to find hidden correlations between the string structures and churn. First, I need to understand what fields are in the dataset.

Looking at the citations, there are several columns: order_id, date, region, channel, rep_id, product_code, color, os, browser, issue_type, unit_price, quantity, discount_pct, revenue, returned, support_ticket, satisfaction, churned_90d, summary_word_count, priority. The churn is indicated by 'churned_90d' which seems to be a binary flag (0 or 1). 

The task is about correlations between string structures and churn. String columns include region, channel, rep_id, product_code, color, os, browser, issue_type, summary_word_count (but that's numeric), priority (which is categorical: Low, Medium, High). So I should focus on these string or categorical fields.

First thought: maybe certain regions have higher churn rates. The dataset mentions Northeast, West, Midwest, etc. If in the data, for example, the 'Nort

	from typing import Optional, Dict, Any, List
	from typing_extensions import Self
	from pydantic import (
	BaseModel,
	ConfigDict,
	Field,
	model_validator,
	)

	from typing import Optional, Dict, List
	from pydantic import (
	BaseModel,
	Field,
	create_model,
	)
	from pydantic.fields import FieldInfo


	# Existing evaluation config models