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data-preprocessing-pipeline

Automated data preprocessing and cleaning pipelines

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AuthorJeremy Longshore
Namespace@jeremylongshore/claude-code-plugins-plus
Category#ai-ml
Version1.0.0
Stars★ 1,193
Downloads↓ 6
Verification[ ✓ ] self.md verified

Automated data preprocessing and cleaning pipelines

Installation

npx claude-plugins install @jeremylongshore/claude-code-plugins-plus/data-preprocessing-pipeline

Contents

Folders: commands, skills

Files: LICENSE, README.md

Documentation

Automated data preprocessing and cleaning pipelines

Installation

/plugin install data-preprocessing-pipeline@claude-code-plugins-plus

Usage

/preprocess

Features

  • Automated workflows
  • Best practices implementation
  • Performance optimization
  • Error handling

Requirements

  • Python 3.8+
  • Standard ML libraries

License

MIT

Included Skills

This plugin includes 1 skill definition:

preprocessing-data-with-automated-pipelines

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View skill definition

Data Preprocessing Pipeline

This skill provides automated assistance for data preprocessing pipeline tasks.

Overview

This skill enables Claude to construct and execute automated data preprocessing pipelines, ensuring data quality and readiness for machine learning. It streamlines the data preparation process by automating common tasks such as data cleaning, transformation, and validation.

How It Works

  1. Analyze Requirements: Claude analyzes the user’s request to understand the specific data preprocessing needs, including data sources, target format, and desired transformations.
  2. Generate Pipeline Code: Based on the requirements, Claude generates Python code for an automated data preprocessing pipeline using relevant libraries and best practices. This includes data validation and error handling.
  3. Execute Pipeline: The generated code is executed, performing the data preprocessing steps.
  4. Provide Metrics and Insights: Claude provides performance metrics and insights about the pipeline’s execution, including data quality reports and potential issues encountered.

When to Use This Skill

This skill activates when you need to:

  • Prepare raw data for machine learning models.
  • Automate data cleaning and transformation processes.
  • Implement a robust ETL (Extract, Transform, Load) pipeline.

Examples

Example 1: Cleaning Customer Data

User request: “Preprocess the customer data from the CSV file to remove duplicates and handle missing va

Source

View on GitHub