# Scikit-Learn Examples

Gallery of scikit-learn examples covering classification, regression, clustering, decomposition, and more.

Use this folder as a model-building workshop, not as a checklist to clear. It is large by design. The goal is to learn the core workflow well enough to train, validate, compare, and explain models on realistic problems.

## What This Folder Is For

- Supervised learning workflows for classification and regression
- Preprocessing, feature engineering, and model selection
- Clustering, decomposition, and unsupervised exploration
- Evaluation habits that carry into later AI and ML phases

## How To Use This Folder Well

- Start with `linear_model`, `model_selection`, `preprocessing`, and `ensemble` before branching out.
- Build one full baseline-first workflow instead of sampling many isolated notebooks.
- Pay close attention to train/test splits, leakage, metrics, and error analysis.
- Treat this folder as practice for thinking clearly about experiments, not just for importing estimators.

## What Comes Next

- Continue to [../../03-maths/README.md](../../03-maths/README.md) if you want stronger theoretical grounding behind the models you use here.
- Continue to [../../09-mlops/README.md](../../09-mlops/README.md) if you want to package and deploy classical ML systems.
- Continue to [../../28-practical-data-science/README.md](../../28-practical-data-science/README.md) if you want end-to-end applied project work.
