splitting-datasets

## Overview This skill automates the process of dividing a dataset into subsets for training, validating, and testing machine learning models. It ensures proper data preparation and facilitates robust model evaluation. ## How It Works 1. **Analyze Request**: The skill analyzes the user's request to determine the dataset to be split and the desired proportions for each subset. 2. **Generate Code**: Based on the request, the skill generates Python code utilizing standard ML libraries to perform the data splitting. 3. **Execute Splitting**: The code is executed to split the dataset into training, validation, and testing sets according to the specified ratios. ## When to Use This Skill This skill activates when you need to: - Prepare a dataset for machine learning model training. - Create training, validation, and testing sets. - Partition data to evaluate model performance. ## Examples ### Example 1: Splitting a CSV file User request: "Split the data in 'my_data.csv' into 70% training, 15% validation, and 15% testing sets." The skill will: 1. Generate Python code to read the 'my_data.csv' file. 2. Execute the code to split the data according to the specified proportions, creating 'train.csv', 'validation.csv', and 'test.csv' files. ### Example 2: Creating a Train-Test Split User request: "Create a train-test split of 'large_dataset.csv' with an 80/20 ratio." The skill will: 1. Generate Python code to load 'large_dataset.csv'. 2. Execute the code to split the dataset ...