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ml-analysis

Automate end-to-end ML analysis from raw data to evaluated models.

1|Updated Mar 16, 2026
Search Tags:#pipeline#model#predict#eda#classify#automl#train
One-click install
npx skills add https://github.com/alonegg/skills --skill ml-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ml-analysis
Source: https://github.com/alonegg/skills/tree/main/ml-analysis
Command: npx skills add https://github.com/alonegg/skills --skill ml-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates end-to-end ML analysis from raw data to evaluated models.

Core Features & Use Cases

  • Performs data understanding and profiling (EDA) before modeling.
  • Builds robust preprocessing pipelines (imputation, encoding, scaling) with leakage prevention and reproducibility.
  • Trains baseline and main models, supports AutoML guidance, and generates full evaluation reports.
  • Example: build a churn/prediction model on tabular data with feature engineering and model comparison.

Quick Start

Provide the dataset path and an optional target column, and the skill will execute the full ML pipeline from EDA through feature engineering and model evaluation to a final report.

Frequently Asked Questions about ml-analysis

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

FAQPage Schema
How do I automate an end-to-end ML analysis pipeline from raw tabular data?▼

Automating an ML pipeline requires running raw data through profiling, preprocessing, and model evaluation stages. This skill delivers deterministic seeds, leakage prevention, and final reports for tabular data analysis.

How does data leakage prevention work during feature engineering in an automated ML pipeline?▼

Data leakage prevention during feature engineering works by building robust preprocessing pipelines that fit transformations only on training data before applying them to test sets. This ensures scaling and imputation do not expose validation data information prematurely.

How do I train and compare classification models using pandas and numpy datasets?▼

Training classification models on pandas and numpy data is handled by an automated pipeline performing EDA, feature engineering, and model training. It supports AutoML guidance and outputs evaluation metrics for model comparison.

Can I use this automated ML pipeline for tabular data prediction tasks like churn modeling?▼

Yes, you can use this automated ML pipeline for tabular data prediction tasks like churn modeling. It handles tabular data profiling, feature engineering, and model comparison to build robust prediction models with evaluation reports.

Do I need to manually preprocess data before running AutoML model training?▼

No manual preprocessing is required before AutoML model training. The pipeline automatically executes imputation, encoding, and scaling within robust preprocessing workflows to ensure data leakage prevention.

What outputs do I get from an automated machine learning analysis pipeline?▼

You receive metrics.json, a comprehensive evaluation report, and model artifacts from an automated ML analysis pipeline. These outputs document performance metrics and models built using deterministic seeds.

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