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jeverett32
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crisp-dm-pipeline

Automate end-to-end CRISP-DM machine learning pipelines from problem framing to evaluation.

Updated Apr 1, 2026
Search Tags:#evaluation#reproducibility#data-science#machine-learning#ml-pipeline#crisp-dm
One-click install
npx skills add https://github.com/jeverett32/INTEX_2026_SKILLS --skill crisp-dm-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: crisp-dm-pipeline
Source: https://github.com/jeverett32/INTEX_2026_SKILLS/tree/main/crisp-dm-pipeline
Command: npx skills add https://github.com/jeverett32/INTEX_2026_SKILLS --skill crisp-dm-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CRISP-DM provides a structured, end-to-end approach to building machine learning solutions. This skill automates the entire workflow from problem framing to evaluation, producing either a notebook with clearly documented phases or a runnable Python project that can be deployed and scheduled.

Core Features & Use Cases

  • End-to-end CRISP-DM pipeline covering problem framing, data understanding, preparation, modeling, and evaluation.
  • Outputs a reusable artifact (notebook or Python project) with an optional operational layout for ETL, training, inference, and scheduling.
  • Suitable for both classification and regression tasks, with deterministic experiments and versioned artifacts.

Quick Start

Use this skill to generate your first CRISP-DM artifact (notebook or Python project) from your data and target variable.

Frequently Asked Questions about crisp-dm-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate a CRISP-DM machine learning pipeline for a Python project?▼

You can automate a CRISP-DM machine learning pipeline by generating a runnable Python project or notebook that covers problem framing, data understanding, preparation, modeling, and evaluation with deterministic experiments.

What is the best way to structure reproducible ML pipelines for classification tasks?▼

The best way to structure reproducible ML pipelines is using a lab-tree project layout that produces versioned artifacts, ensuring repeatable and auditable classification or regression results.

Can I use this CRISP-DM workflow for both notebooks and scheduled Python deployments?▼

Yes, the CRISP-DM workflow outputs either a documented notebook or a runnable Python project with an optional operational layout for ETL, training, inference, and scheduling.

Does an end-to-end machine learning pipeline include deployment and scheduling paths?▼

Yes, an end-to-end machine learning pipeline includes optional deployment paths and scheduling layouts alongside data preparation, modeling, evaluation, and versioned artifact generation.

How do I ensure deterministic experiments when building data science workflows?▼

You ensure deterministic experiments in data science workflows by generating a structured CRISP-DM pipeline that produces versioned artifacts within a lab-tree project layout for repeatable results.

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