splitting-datasets

Automates dataset splitting into training, validation, and testing sets for ML model preparation.

• Splits datasets into training, validation, and testing subsets.
• Generates and executes Python code for data partitioning.
• Supports stratified splitting for imbalanced data.
• Ensures data integrity during splitting.

• Uses standard ML libraries for splitting.
• Supports custom proportions for each subset.
• Creates train, validation, and test files.
• Maintains class distribution via stratification.
• Ensures data integrity and no corruption.
• Randomized splits to avoid bias.
• Integrates with ML workflows.
• Handles CSV file inputs.

1. 1.Specify dataset file and split proportions in request.
2. 2.Skill generates Python code for data splitting.
3. 3.Code executes and creates separate train/validation/test files.
4. 4.Verify output files and data integrity.