Your feature should handle diverse data formats seamlessly using libraries like Pandas and NumPy .

Ensure numerical values aren't stored as strings and vice versa.

Before publishing, the data must be validated against specific quality standards.

Create new variables by transforming or combining existing columns, such as extracting "Day of Week" from a timestamp. 4. Validation & Quality Control

When building a feature for , your goal is to bridge the gap between messy, raw data and structured, analysis-ready datasets. Data wrangling (or munging) typically involves six key stages: discovery, structuring, cleaning, enriching, validating, and publishing. Here are the core components to include in your feature: 1. Robust Data Ingestion