prepare-dataset

What problem does it solve?

Manually loading, preprocessing, and validating datasets for machine learning model training is a complex, repetitive, and error-prone process.

Core Features & Use Cases

• Data Preprocessing: Guides through normalization, standardization, and encoding of categorical features.
• Train/Validation/Test Split: Automates the splitting of datasets into appropriate sets for training, validation, and testing.
• Use Case: When setting up a new machine learning project, use this skill to define a robust data pipeline that loads raw data, validates its integrity, normalizes features, and splits it into ready-to-use training and testing sets.

Quick Start

Use the prepare-dataset skill to outline a Python class for loading, normalizing, splitting, and augmenting a dataset.