# Matplotlib

Visualization examples using Matplotlib.

Use this folder to build plotting fundamentals before relying on higher-level charting libraries. The goal is to understand how to choose a chart, label it clearly, and inspect whether it actually communicates the underlying data.

## What This Folder Is For

- Line, bar, scatter, histogram, and subplot basics
- Labeling, legends, axes, and annotation habits
- Building readable plots for analysis notebooks and reports
- Strengthening the visualization layer that supports EDA and model evaluation

## How To Use This Folder Well

- Start with simple quantitative plots before trying to make polished dashboards.
- Focus on readability and interpretation, not just API memorization.
- Recreate one or two plots from your pandas work so the skills transfer immediately.

## What Comes Next

- Continue to [../5-scikit-learn/README.md](../5-scikit-learn/README.md) to apply plotting during modeling and evaluation.
- Continue to [../../16-model-evaluation/README.md](../../16-model-evaluation/README.md) later when plots start becoming metric and error-analysis tools.
- Revisit [../2-pandas-examples/README.md](../2-pandas-examples/README.md) if you need more real datasets to visualize.
