
Scikit Learn
This is your go-to for classical machine learning tasks when you need something interpretable and battle-tested. Covers the full workflow: train/test splits, preprocessing with StandardScaler and OneHotEncoder, classification with RandomForest or GradientBoosting, clustering with KMeans or DBSCAN, and proper evaluation with cross-validation. The Pipeline and ColumnTransformer examples are especially useful since they prevent data leakage and make it easy to handle mixed numeric and categorical features in one workflow. Skip this if you're doing deep learning, but for tabular data, customer segmentation, or any project where you need to explain your model to stakeholders, scikit-learn remains the standard for good reason.
- skills.sh installs
- 944
- GitHub stars
- 33.2K
- last updated
- Aug 21, 2026
Install
$ npx -y skills add davila7/claude-code-templates --skill scikit-learn --agent claude-codeInstalls into .claude/skills of the current project.
Files
Scikit-learn
Overview
This skill provides comprehensive guidance for machine learning tasks using scikit-learn, the industry-standard Python library for classical machine learning. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.
Installation
# Install scikit-learn using uv
uv uv pip install scikit-learn
# Optional: Install visualization dependencies
uv uv pip install matplotlib seaborn
# Commonly used with
uv uv pip install pandas numpy
When to Use This Skill
Use the scikit-learn skill when:
- Building classification or regression models
- Performing clustering or dimensionality reduction
- Preprocessing and transforming data for machine learning
- Evaluating model performance with cross-validation
- Tuning hyperparameters with grid or random search
- Creating ML pipelines for production workflows
- Comparing different algorithms for a task
- Working with both structured (tabular) and text data
- Need interpretable, classical machine learning approaches
Quick Start
Classification Example
Sponsors
Details
- Repository
- github.com/davila7/claude-code-templates
- Categories
- Documentation, Data Science & ML, Python
- First seen
- May 16, 2026
More from davila7/claude-code-templates
All 618- Qdrant Vector Search922
- Mermaid Diagrams913
- Modal905
- Docker Expert899
- Pptx892
- Security Compliance848
- Draw Io843
- Pdf821
- Excalidraw804
- Senior Ml Engineer799
- Devops Iac Engineer783
- Copywriting776
- Marketing Ideas769
- Statistical Analysis769
- Browser Automation753
- Agent Memory Systems728
- Context7 Auto Research724
- Content Creator723
- Frontend Dev Guidelines713
- Pubmed Database713
- Game Development691
- Ai Agents Architect688
- Clinical Reports679
- Perplexity Search674