Dataset Splitter

About

This skill streamlines the data preparation phase of machine learning workflows by automatically dividing raw datasets into optimized subsets. It handles complex partitioning tasks like train-test-validation splits, ensuring data integrity through proper randomization and optional stratification for imbalanced datasets. By generating and executing specialized Python code using industry-standard libraries, it eliminates manual data handling errors and accelerates the transition from data collection to model training within the Claude Code environment.

Key Features

• Direct generation and execution of Python-based data processing code
• Built-in randomization to prevent selection bias in subsets
• 897 GitHub stars
• Support for custom split ratios and user-defined proportions
• Stratification capabilities to maintain class distributions
• Automated three-way splitting into training, validation, and testing sets

Use Cases

• Creating dedicated hold-out test sets for final model performance evaluation
• Implementing stratified splits for imbalanced classification datasets
• Preparing raw CSV data for deep learning or neural network training