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scikit-learn ML Assistant

A skill that guides classical machine learning in Python with scikit-learn — classification, regression, clustering, preprocessing, evaluation and tuning — following leakage-free best practices.

Data & AnalyticsIntermediate★ 33,030⑂ 3,248AI score 9/10Last updated: Aug 9, 2026

What it does

  • Helps you pick and build supervised (classification, regression) and unsupervised (clustering, dimensionality reduction) models.
  • Writes Pipeline / ColumnTransformer code so preprocessing is refit per CV fold and test data never leaks into training.
  • Covers cross-validation, GridSearchCV/RandomizedSearchCV tuning, and metric choice for balanced vs. imbalanced data.
  • Ships reference docs for five areas (supervised, unsupervised, evaluation, preprocessing, pipelines) plus runnable scripts: classification_pipeline.py and clustering_analysis.py.
  • Includes a troubleshooting section for ConvergenceWarning, overfitting, and memory errors on large datasets.

Who it's for

  • Data analysts and data scientists working with tabular data in Python.
  • ML engineers who need a solid classical baseline before reaching for deep learning.
  • Students and newcomers who want correct experiment hygiene (stratified splits, CV, no leakage).

Examples

  1. "Build a churn classifier from this mixed numeric/categorical CSV" → generates an impute + scale + one-hot ColumnTransformer pipeline and compares models via cross-validation.
  2. "My fraud dataset is highly imbalanced — which metric should I optimize?" → recommends Precision/Recall/ROC AUC/Balanced Accuracy and drafts a custom scorer.
  3. "Segment my customers" → finds optimal k with elbow and silhouette analysis, compares K-Means/DBSCAN/GMM, and plots a PCA projection.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/scikit-learn/SKILL.md

What I want: (describe your task here)

If Claude can't open the link, open it yourself and paste the contents instead.

↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/scikit-learn folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/scikit-learn/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git /tmp/sas && mkdir -p ~/.claude/skills && cp -r /tmp/sas/skills/scikit-learn ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  2. Create the skills directory if needed: mkdir -p ~/.claude/skills
  3. Copy just this skill: cp -r scientific-agent-skills/skills/scikit-learn ~/.claude/skills/
  4. Install the Python dependencies: uv pip install "scikit-learn>=1.7" pandas numpy (add matplotlib seaborn for the bundled plotting scripts).
  5. Restart Claude Code and ask something like "build a classification pipeline for this CSV" — the skill activates automatically.
View source on GitHub ↗License: MIT