mcp-data-cleaning

What problem does it solve?

Data cleaning and preprocessing are essential to unlock reliable insights from messy clinical and research datasets. This skill provides a standard MCP-driven workflow to diagnose data issues, handle missing values, encode categoricals, filter rows, remove irrelevant columns, and check multicollinearity, enabling consistent preparation for downstream analyses.

Core Features & Use Cases

• Diagnose data quality and column information to guide cleaning decisions.
• Apply flexible missing-value strategies (mean, median, mode, constant, or drop) per column.
• Encode categorical variables, filter rows, remove columns, and perform VIF checks to ensure model-ready data.
• Use Case: Prepare Titanic-like datasets for ML modeling with transparent, repeatable steps.

Quick Start

Run the data cleaning workflow on your raw dataset to produce a cleaned dataset ready for modeling.