What file formats does the Dataset Splitter support?

The skill primarily handles CSV files but can be adapted to any data format supported by Python's data science ecosystem, such as Parquet or Excel, via automated code generation.

Can I specify custom split percentages?

Yes, you can request specific ratios such as 80/20 or 70/15/15, and the skill will generate the corresponding Python logic to partition your data exactly as requested.

How do I trigger the Dataset Splitter skill?

You can trigger it by asking Claude to 'split the dataset', 'create a train-test split', or 'partition the data' followed by the filename and desired ratios.

Is the data splitting process randomized?

Yes, the skill utilizes randomization to ensure that the training and testing sets are representative of the overall population and free from selection bias.

Does it handle imbalanced datasets?

Yes, the skill follows best practices including stratification to ensure that class distributions are maintained across all resulting subsets.

Dataset Splitter

Name: Dataset Splitter
Author: intent-solutions-io

byintent-solutions-io

Data Science & ML

Automates the partitioning of data into training, validation, and testing sets for machine learning workflows.

About

The Dataset Splitter skill streamlines the data preparation phase of machine learning projects by automatically dividing raw datasets into optimized subsets. It analyzes user requirements for specific ratios, generates robust Python code using standard libraries, and executes the partitioning to produce clean training, validation, and test files. This skill ensures data integrity and supports advanced techniques like randomization and stratification, making it an essential tool for developers and data scientists building and evaluating predictive models within the Claude Code environment.

Key Features

Support for customizable split ratios and multi-way partitioning
Ensures data integrity and randomization to prevent model bias
Supports stratification to maintain class distribution in imbalanced datasets
Automated data partitioning for training, validation, and testing sets
Generates and executes Python code using industry-standard ML libraries
0 GitHub stars

Use Cases

Creating a hold-out test set from a large CSV file for final performance verification
Preparing high-quality training and evaluation sets for a new machine learning model
Automating the data preprocessing step in a Nixtla-based forecasting pipeline

About

Key Features

Support for customizable split ratios and multi-way partitioning
Ensures data integrity and randomization to prevent model bias
Supports stratification to maintain class distribution in imbalanced datasets
Automated data partitioning for training, validation, and testing sets
Generates and executes Python code using industry-standard ML libraries
0 GitHub stars

Use Cases

Creating a hold-out test set from a large CSV file for final performance verification
Preparing high-quality training and evaluation sets for a new machine learning model
Automating the data preprocessing step in a Nixtla-based forecasting pipeline